EMA20 Cross Strategy with countertrades and signalsEMA20 Cross Strategy Documentation
Overview
The EMA20 Cross Strategy with Counter-Trades and Instant Signals is a Pine Script (version 6) trading strategy designed for the TradingView platform. It implements an Exponential Moving Average (EMA) crossover system to generate buy and sell signals, with optional trend filtering, session-based trading, instant signal processing, and visual/statistical feedback. The strategy supports counter-trades (closing opposing positions before entering new ones) and operates with a fixed trade size in EUR.
Features
EMA Crossover Mechanism:
Uses a short-term EMA (configurable length, default: 1) and a long-term EMA (default: 20) to detect crossovers.
A buy signal is generated when the short EMA crosses above the long EMA.
A sell signal is generated when the short EMA crosses below the long EMA.
Instant Signals:
If enabled (useInstantSignals), signals are based on the current price crossing the short EMA, rather than waiting for the candle close.
This allows faster trade execution but may increase sensitivity to price fluctuations.
Trend Filter:
Optionally filters trades based on the trend direction (useTrendFilter).
Long trades are allowed only when the short EMA (or price, for instant signals) is above the long EMA.
Short trades are allowed only when the short EMA (or price) is below the long EMA.
Session Filter:
Restricts trading to specific market hours (sessionStart, default: 09:00–17:00) if enabled (useSessionFilter).
Ensures trades occur only during active market sessions, reducing exposure to low-liquidity periods.
Customizable Timeframe:
The EMA calculations can use a higher timeframe (e.g., 5m, 15m, 1H, 4H, 1D, default: 1H) via request.security.
This allows the strategy to base signals on longer-term trends while operating on a shorter-term chart.
Trade Management:
Fixed trade size of €100,000 per trade (tradeAmount), with a maximum quantity cap (maxQty = 10,000) to prevent oversized trades.
Counter-trades: Closes short positions before entering a long position and vice versa.
Trades are executed with a minimum quantity of 1 to ensure valid orders.
Visualization:
EMA Lines: The short EMA is colored based on the last signal (green for buy, red for sell, gray for neutral), and the long EMA is orange.
Signal Markers: Displays buy/sell signals as arrows (triangles) above/below candles if enabled (showSignalShapes).
Background/Candle Coloring: Optionally colors the chart background or candles green (bullish) or red (bearish) based on the trend (useColoredBars).
Statistics Display:
If enabled (useStats), a label on the chart shows:
Total closed trades
Open trades
Win rate (%)
Number of winning/losing trades
Profit factor (gross profit / gross loss)
Net profit
Maximum drawdown
Configuration Inputs
EMA Short Length (emaLength): Length of the short-term EMA (default: 1).
Trend EMA Length (trendLength): Length of the long-term EMA (default: 20).
Enable Trend Filter (useTrendFilter): Toggles trend-based filtering (default: true).
Color Candles (useColoredBars): Colors candles instead of the background (default: true).
Enable Session Filter (useSessionFilter): Restricts trading to specified hours (default: false).
Trading Session (sessionStart): Defines trading hours (default: 09:00–17:00).
Show Statistics (useStats): Displays performance stats on the chart (default: true).
Show Signal Arrows (showSignalShapes): Displays buy/sell signals as arrows (default: true).
Use Instant Signals (useInstantSignals): Generates signals based on live price action (default: false).
EMA Timeframe (emaTimeframe): Timeframe for EMA calculations (options: 5m, 15m, 1H, 4H, 1D; default: 1H).
Strategy Logic
Signal Generation:
Standard Mode: Signals are based on EMA crossovers (short EMA crossing long EMA) at candle close.
Instant Mode: Signals are based on the current price crossing the short EMA, enabling faster reactions.
Trade Execution:
On a buy signal, closes any short position and opens a long position.
On a sell signal, closes any long position and opens a short position.
Position size is calculated as the minimum of €100,000 or available equity, divided by the current price, capped at 10,000 units.
Filters:
Trend Filter: Ensures trades align with the trend direction (if enabled).
Session Filter: Restricts trades to user-defined market hours (if enabled).
Visual Feedback
EMA Lines: Provide a clear view of the short and long EMAs, with the short EMA’s color reflecting the latest signal.
Signal Arrows: Large green triangles (buy) below candles or red triangles (sell) above candles for easy signal identification.
Chart Coloring: Highlights bullish (green) or bearish (red) trends via background or candle colors.
Statistics Label: Displays key performance metrics in a label above the chart for quick reference.
Usage Notes
Initial Capital: €100,000 (configurable via initial_capital).
Currency: EUR (set via currency).
Order Processing: Orders are processed at candle close (process_orders_on_close=true) unless instant signals are enabled.
Dynamic Requests: Allows dynamic timeframe adjustments for EMA calculations (dynamic_requests=true).
Platform: Designed for TradingView, compatible with any market supported by the platform (e.g., stocks, forex, crypto).
Example Use Case
Scenario: Trading on a 5-minute chart with a 1-hour EMA timeframe, trend filter enabled, and session filter set to 09:00–17:00.
Behavior: The strategy will:
Calculate EMAs on the 1-hour timeframe.
Generate buy signals when the short EMA crosses above the long EMA (and price is above the long EMA).
Generate sell signals when the short EMA crosses below the long EMA (and price is below the long EMA).
Execute trades only during 09:00–17:00.
Display green/red candles and performance stats on the chart.
Limitations
Instant Signals: May lead to more frequent signals, increasing the risk of false positives in volatile markets.
Fixed Trade Size: Does not adjust dynamically based on market conditions beyond equity and max quantity limits.
Session Filter: Simplified and may not account for complex session rules or holidays.
Statistics: Displayed on-chart, which may clutter the view in smaller charts.
Customization
Adjust emaLength and trendLength to suit different market conditions (e.g., shorter for scalping, longer for swing trading).
Toggle useInstantSignals for faster or more stable signal generation.
Modify sessionStart to align with specific market hours.
Disable useStats or showSignalShapes for a cleaner chart.
This strategy is versatile for both manual and automated trading, offering flexibility for various markets and trading styles while providing clear visual and statistical feedback.
Search in scripts for "order"
Simple Liquidity Zones [Supertrade]🔎 What this indicator does
This indicator is designed to highlight liquidity sweep zones on the chart.
• A liquidity sweep occurs when price briefly breaks above a recent swing high or below a recent swing low, but fails to close beyond it.
• Such behavior often indicates that price has taken liquidity (stop orders resting above highs or below lows) and may reverse.
The indicator marks these events as bullish or bearish liquidity zones:
• Bullish Zone (green) → Price swept a swing low and closed back above it (possible bullish reversal area).
• Bearish Zone (red) → Price swept a swing high and closed back below it (possible bearish reversal area).
These zones are drawn as shaded horizontal bands that extend forward in time, providing visual areas where liquidity grabs occurred.
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⚙️ How calculations are made
The indicator does not use moving averages or smoothing.
Instead, it works with raw price action:
1. Swing Detection → It checks the highest high and lowest low of the past N bars (swing length).
2. Sweep Logic →
o A bearish sweep happens if the high breaks above the previous swing high, but the close returns below that level.
o A bullish sweep happens if the low breaks below the previous swing low, but the close returns above that level.
3. Zone Creation → When a sweep is detected, a shaded zone is drawn just above/below the swing level.
4. Persistence → Zones extend into the future until replaced by new ones (or optionally until price fully trades through them).
This makes the calculations simple, transparent, and responsive to actual market structure without lag.
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📈 How it helps traders
This tool helps traders by:
• Visualizing liquidity areas → Shows where price previously swept liquidity and may act as support/resistance.
• Identifying reversals → Helps spot potential turning points after liquidity grabs.
• Risk management → Zones highlight areas where stops may be targeted, useful for positioning stop-loss orders.
• Confluence tool → Works best when combined with other strategies such as order blocks, trendlines, or volume analysis.
⚠️ Note: Like all indicators, this should not be used in isolation. It provides context, not guaranteed trade signals.
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🏦 Markets & Timeframes
• Works across all markets (crypto, forex, stocks, indices, commodities).
• Particularly effective in high-liquidity environments where stop-hunting is common (e.g., forex majors, BTC/ETH, S&P500).
• Timeframes:
o Lower timeframes (1m–15m) → Scalpers can spot intraday liquidity sweeps.
o Higher timeframes (1H–1D) → Swing traders can identify major liquidity pools.
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TimeSeriesBenchmarkMeasuresLibrary "TimeSeriesBenchmarkMeasures"
Time Series Benchmark Metrics. \
Provides a comprehensive set of functions for benchmarking time series data, allowing you to evaluate the accuracy, stability, and risk characteristics of various models or strategies. The functions cover a wide range of statistical measures, including accuracy metrics (MAE, MSE, RMSE, NRMSE, MAPE, SMAPE), autocorrelation analysis (ACF, ADF), and risk measures (Theils Inequality, Sharpness, Resolution, Coverage, and Pinball).
___
Reference:
- github.com .
- medium.com .
- www.salesforce.com .
- towardsdatascience.com .
- github.com .
mae(actual, forecasts)
In statistics, mean absolute error (MAE) is a measure of errors between paired observations expressing the same phenomenon. Examples of Y versus X include comparisons of predicted versus observed, subsequent time versus initial time, and one technique of measurement versus an alternative technique of measurement.
Parameters:
actual (array) : List of actual values.
forecasts (array) : List of forecasts values.
Returns: - Mean Absolute Error (MAE).
___
Reference:
- en.wikipedia.org .
- The Orange Book of Machine Learning - Carl McBride Ellis .
mse(actual, forecasts)
The Mean Squared Error (MSE) is a measure of the quality of an estimator. As it is derived from the square of Euclidean distance, it is always a positive value that decreases as the error approaches zero.
Parameters:
actual (array) : List of actual values.
forecasts (array) : List of forecasts values.
Returns: - Mean Squared Error (MSE).
___
Reference:
- en.wikipedia.org .
rmse(targets, forecasts, order, offset)
Calculates the Root Mean Squared Error (RMSE) between target observations and forecasts. RMSE is a standard measure of the differences between values predicted by a model and the values actually observed.
Parameters:
targets (array) : List of target observations.
forecasts (array) : List of forecasts.
order (int) : Model order parameter that determines the starting position in the targets array, `default=0`.
offset (int) : Forecast offset related to target, `default=0`.
Returns: - RMSE value.
nmrse(targets, forecasts, order, offset)
Normalised Root Mean Squared Error.
Parameters:
targets (array) : List of target observations.
forecasts (array) : List of forecasts.
order (int) : Model order parameter that determines the starting position in the targets array, `default=0`.
offset (int) : Forecast offset related to target, `default=0`.
Returns: - NRMSE value.
rmse_interval(targets, forecasts)
Root Mean Squared Error for a set of interval windows. Computes RMSE by converting interval forecasts (with min/max bounds) into point forecasts using the mean of the interval bounds, then compares against actual target values.
Parameters:
targets (array) : List of target observations.
forecasts (matrix) : The forecasted values in matrix format with at least 2 columns (min, max).
Returns: - RMSE value for the combined interval list.
mape(targets, forecasts)
Mean Average Percentual Error.
Parameters:
targets (array) : List of target observations.
forecasts (array) : List of forecasts.
Returns: - MAPE value.
smape(targets, forecasts, mode)
Symmetric Mean Average Percentual Error. Calculates the Mean Absolute Percentage Error (MAPE) between actual targets and forecasts. MAPE is a common metric for evaluating forecast accuracy, expressed as a percentage, lower values indicate a better forecast accuracy.
Parameters:
targets (array) : List of target observations.
forecasts (array) : List of forecasts.
mode (int) : Type of method: default=0:`sum(abs(Fi-Ti)) / sum(Fi+Ti)` , 1:`mean(abs(Fi-Ti) / ((Fi + Ti) / 2))` , 2:`mean(abs(Fi-Ti) / (abs(Fi) + abs(Ti))) * 100`
Returns: - SMAPE value.
mape_interval(targets, forecasts)
Mean Average Percentual Error for a set of interval windows.
Parameters:
targets (array) : List of target observations.
forecasts (matrix) : The forecasted values in matrix format with at least 2 columns (min, max).
Returns: - MAPE value for the combined interval list.
acf(data, k)
Autocorrelation Function (ACF) for a time series at a specified lag.
Parameters:
data (array) : Sample data of the observations.
k (int) : The lag period for which to calculate the autocorrelation. Must be a non-negative integer.
Returns: - The autocorrelation value at the specified lag, ranging from -1 to 1.
___
The autocorrelation function measures the linear dependence between observations in a time series
at different time lags. It quantifies how well the series correlates with itself at different
time intervals, which is useful for identifying patterns, seasonality, and the appropriate
lag structure for time series models.
ACF values close to 1 indicate strong positive correlation, values close to -1 indicate
strong negative correlation, and values near 0 indicate no linear correlation.
___
Reference:
- statisticsbyjim.com
acf_multiple(data, k)
Autocorrelation function (ACF) for a time series at a set of specified lags.
Parameters:
data (array) : Sample data of the observations.
k (array) : List of lag periods for which to calculate the autocorrelation. Must be a non-negative integer.
Returns: - List of ACF values for provided lags.
___
The autocorrelation function measures the linear dependence between observations in a time series
at different time lags. It quantifies how well the series correlates with itself at different
time intervals, which is useful for identifying patterns, seasonality, and the appropriate
lag structure for time series models.
ACF values close to 1 indicate strong positive correlation, values close to -1 indicate
strong negative correlation, and values near 0 indicate no linear correlation.
___
Reference:
- statisticsbyjim.com
adfuller(data, n_lag, conf)
: Augmented Dickey-Fuller test for stationarity.
Parameters:
data (array) : Data series.
n_lag (int) : Maximum lag.
conf (string) : Confidence Probability level used to test for critical value, (`90%`, `95%`, `99%`).
Returns: - `adf` The test statistic.
- `crit` Critical value for the test statistic at the 10 % levels.
- `nobs` Number of observations used for the ADF regression and calculation of the critical values.
___
The Augmented Dickey-Fuller test is used to determine whether a time series is stationary
or contains a unit root (non-stationary). The null hypothesis is that the series has a unit root
(is non-stationary), while the alternative hypothesis is that the series is stationary.
A stationary time series has statistical properties that do not change over time, making it
suitable for many time series forecasting models. If the test statistic is less than the
critical value, we reject the null hypothesis and conclude the series is stationary.
___
Reference:
- www.jstor.org
- en.wikipedia.org
theils_inequality(targets, forecasts)
Calculates Theil's Inequality Coefficient, a measure of forecast accuracy that quantifies the relative difference between actual and predicted values.
Parameters:
targets (array) : List of target observations.
forecasts (array) : Matrix with list of forecasts, ordered column wise.
Returns: - Theil's Inequality Coefficient value, value closer to 0 is better.
___
Theil's Inequality Coefficient is calculated as: `sqrt(Sum((y_i - f_i)^2)) / (sqrt(Sum(y_i^2)) + sqrt(Sum(f_i^2)))`
where `y_i` represents actual values and `f_i` represents forecast values.
This metric ranges from 0 to infinity, with 0 indicating perfect forecast accuracy.
___
Reference:
- en.wikipedia.org
sharpness(forecasts)
The average width of the forecast intervals across all observations, representing the sharpness or precision of the predictive intervals.
Parameters:
forecasts (matrix) : The forecasted values in matrix format with at least 2 columns (min, max).
Returns: - Sharpness The sharpness level, which is the average width of all prediction intervals across the forecast horizon.
___
Sharpness is an important metric for evaluating forecast quality. It measures how narrow or wide the
prediction intervals are. Higher sharpness (narrower intervals) indicates greater precision in the
forecast intervals, while lower sharpness (wider intervals) suggests less precision.
The sharpness metric is calculated as the mean of the interval widths across all observations, where
each interval width is the difference between the upper and lower bounds of the prediction interval.
Note: This function assumes that the forecasts matrix has at least 2 columns, with the first column
representing the lower bounds and the second column representing the upper bounds of prediction intervals.
___
Reference:
- Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: principles and practice. OTexts. otexts.com
resolution(forecasts)
Calculates the resolution of forecast intervals, measuring the average absolute difference between individual forecast interval widths and the overall sharpness measure.
Parameters:
forecasts (matrix) : The forecasted values in matrix format with at least 2 columns (min, max).
Returns: - The average absolute difference between individual forecast interval widths and the overall sharpness measure, representing the resolution of the forecasts.
___
Resolution is a key metric for evaluating forecast quality that measures the consistency of prediction
interval widths. It quantifies how much the individual forecast intervals vary from the average interval
width (sharpness). High resolution indicates that the forecast intervals are relatively consistent
across observations, while low resolution suggests significant variation in interval widths.
The resolution is calculated as the mean absolute deviation of individual interval widths from the
overall sharpness value. This provides insight into the uniformity of the forecast uncertainty
estimates across the forecast horizon.
Note: This function requires the forecasts matrix to have at least 2 columns (min, max) representing
the lower and upper bounds of prediction intervals.
___
Reference:
- (sites.stat.washington.edu)
- (www.jstor.org)
coverage(targets, forecasts)
Calculates the coverage probability, which is the percentage of target values that fall within the corresponding forecasted prediction intervals.
Parameters:
targets (array) : List of target values.
forecasts (matrix) : The forecasted values in matrix format with at least 2 columns (min, max).
Returns: - Percent of target values that fall within their corresponding forecast intervals, expressed as a decimal value between 0 and 1 (or 0% and 100%).
___
Coverage probability is a crucial metric for evaluating the reliability of prediction intervals.
It measures how well the forecast intervals capture the actual observed values. An ideal forecast
should have a coverage probability close to the nominal confidence level (e.g., 90%, 95%, or 99%).
For example, if a 95% prediction interval is used, we expect approximately 95% of the actual
target values to fall within those intervals. If the coverage is significantly lower than the
nominal level, the intervals may be too narrow; if it's significantly higher, the intervals may
be too wide.
Note: This function requires the targets array and forecasts matrix to have the same number of
observations, and the forecasts matrix must have at least 2 columns (min, max) representing
the lower and upper bounds of prediction intervals.
___
Reference:
- (www.jstor.org)
pinball(tau, target, forecast)
Pinball loss function, measures the asymmetric loss for quantile forecasts.
Parameters:
tau (float) : The quantile level (between 0 and 1), where 0.5 represents the median.
target (float) : The actual observed value to compare against.
forecast (float) : The forecasted value.
Returns: - The Pinball loss value, which quantifies the distance between the forecast and target relative to the specified quantile level.
___
The Pinball loss function is specifically designed for evaluating quantile forecasts. It is
asymmetric, meaning it penalizes underestimates and overestimates differently depending on the
quantile level being evaluated.
For a given quantile τ, the loss function is defined as:
- If target >= forecast: (target - forecast) * τ
- If target < forecast: (forecast - target) * (1 - τ)
This loss function is commonly used in quantile regression and probabilistic forecasting
to evaluate how well forecasts capture specific quantiles of the target distribution.
___
Reference:
- (www.otexts.com)
pinball_mean(tau, targets, forecasts)
Calculates the mean pinball loss for quantile regression.
Parameters:
tau (float) : The quantile level (between 0 and 1), where 0.5 represents the median.
targets (array) : The actual observed values to compare against.
forecasts (matrix) : The forecasted values in matrix format with at least 2 columns (min, max).
Returns: - The mean pinball loss value across all observations.
___
The pinball_mean() function computes the average Pinball loss across multiple observations,
making it suitable for evaluating overall forecast performance in quantile regression tasks.
This function leverages the asymmetric Pinball loss function to evaluate how well forecasts
capture specific quantiles of the target distribution. The choice of which column from the
forecasts matrix to use depends on the quantile level:
- For τ ≤ 0.5: Uses the first column (min) of forecasts
- For τ > 0.5: Uses the second column (max) of forecasts
This loss function is commonly used in quantile regression and probabilistic forecasting
to evaluate how well forecasts capture specific quantiles of the target distribution.
___
Reference:
- (www.otexts.com)
Opening-Range BreakoutNote: Default trading date range looks mediocre. Set date range to "Entire History" to see full effect of the strategy. 50.91% profitable trades, 1.178 profit factor, steady profits and limited drawdown. Total P&L: $154,141.18, Max Drawdown: $18,624.36. High R^2
█ Overview
The Opening-Range Breakout strategy is a mechanical, session‑based day‑trading system designed to capture the initial burst of directional momentum immediately following the market open. It defines a user‑configurable “opening range” window, measures its high and low boundaries, then places breakout stop orders at those levels once the range closes. Built‑in filters on minimum range width, reward‑to‑risk ratios, and optional reversal logic help refine entries and manage risk dynamically.
█ How It Works
Opening‑Range Formation
Between 9:30–10:15 AM ET (configurable), the script tracks the highest high and lowest low to form the day’s opening range box.
On the first bar after the range window closes, the range high (OR_high) and low (OR_low) are “locked in.”
Range‑Width Filter
To avoid false breakouts in low‑volatility mornings, the range must be at least X% of the current price (default 0.35%).
If the measured opening-range width < minimum threshold, no orders are placed that day.
Entry & Order Placement
Long: a stop‑buy order at the opening‑range high.
Short: a stop‑sell order at the opening‑range low.
Only one side can trigger (or both if reverse logic is enabled after a losing trade).
Risk Management
Once triggered, each trade uses an ATR‑style stop-loss defined as a percentage retracement of the range (default 50% of range width).
Profit target is set at a configurable Reward/Risk Ratio (default 1.1×).
Optional: Reverse on Stop‑Loss – if the initial breakout loses, immediately reverse into the opposite side on the same day.
Session Exit
Any open positions are closed at the end of the regular trading day (default 3:45 PM ET window end, with hard flat at session close).
Visual cues are provided via green (range high) and red (range low) step‑line plots directly on the chart, allowing you to see the range box and breakout triggers in real time.
█ Why It Works
Early Momentum Capture: The first 15 – 60 minutes of trading encapsulate overnight news digestion and institutional order flow, creating a well‑defined volatility “range.”
Mechanical Discipline: Clear, rule‑based entries and exits remove emotional guesswork, ensuring consistency.
Volatility Filtering: By requiring a minimum range width, the system avoids choppy, low‑range days where false breakouts are common.
Dynamic Sizing: Stops and targets scale with the opening range, adapting automatically to each day’s volatility environment.
█ How to Use
Set Your Instruments & Timeframe
-Apply to any futures contract on a 1‑ to 5‑minute chart.
-Ensure chart timezone is set to America/New_York.
Configure Inputs
-Opening‑Range Window: e.g. “0930-1015” for a 45‑minute range.
-Min. OR Width (%): e.g. 0.35 for 0.35% of current price.
-Reward/Risk Ratio: e.g. 1.1 for a modest profit target above your stop.
-Max OR Retracement %: e.g. 50 to set stop at 50% of range width.
-One Trade Per Day: toggle to limit to a single breakout.
-Reverse on Stop Loss: toggle to flip direction after a losing breakout.
Monitor the Chart
-Watch the green and red range boundaries form during the session open.
-Orders will automatically submit on the first bar after the range window closes, conditioned on your filters.
Review & Adjust
-Backtest across multiple months to validate performance on your preferred contract.
-Tweak range duration, minimum width, and R/R multiple to fit your risk tolerance and desired win‑rate vs. expectancy balance.
█ Settings Reference
Input Defaults
Opening‑Range Window - Time window to form OR (HHMM-HHMM) - 0930–1015
Regular Trading Day - Full session for EOD flat (HHMM-HHMM) - 0930–1545
Min. OR Width (%) - Minimum OR size as % of close to trigger orders - 0.35
Reward/Risk Ratio - Profit target multiple of stop‑loss distance - 1.1
Max OR Retracement (%) - % of OR width to use as stop‑loss distance - 50
One Trade Per Day - Limit to a single breakout order per day - false
Reverse on Stop Loss - Reverse direction immediately after a losing trade - true
Disclaimer
This strategy description and any accompanying code are provided for educational purposes only and do not constitute financial advice or a solicitation to trade. Futures trading involves substantial risk, including possible loss of capital. Past performance is not indicative of future results. Traders should assess their own risk tolerance and conduct thorough backtesting and forward-testing before committing real capital.
TextLibrary "Text"
library to format text in different fonts or cases plus a sort function.
🔸 Credits and Usage
This library is inspired by the work of three authors (in chronological order of publication date):
Unicode font function - JD - Duyck
UnicodeReplacementFunction - wlhm
font - kaigouthro
🔹 Fonts
Besides extra added font options, the toFont(fromText, font) method uses a different technique. On the first runtime bar (whether it is barstate.isfirst , barstate.islast , or between) regular letters and numbers and mapped with the chosen font. After this, each character is replaced using the build-in key - value pair map function .
Also an enum Efont is included.
Note: Some fonts are not complete, for example there isn't a replacement for every character in Superscript/Subscript.
Example of usage (besides the included table example):
import fikira/Text/1 as t
i_font = input.enum(t.Efont.Blocks)
if barstate.islast
sentence = "this sentence contains words"
label.new(bar_index, 0, t.toFont(fromText = sentence, font = str.tostring(i_font)), style=label.style_label_lower_right)
label.new(bar_index, 0, t.toFont(fromText = sentence, font = "Circled" ), style=label.style_label_lower_left )
label.new(bar_index, 0, t.toFont(fromText = sentence, font = "Wiggly" ), style=label.style_label_upper_right)
label.new(bar_index, 0, t.toFont(fromText = sentence, font = "Upside Latin" ), style=label.style_label_upper_left )
🔹 Cases
The script includes a toCase(fromText, case) method to transform text into snake_case, UPPER SNAKE_CASE, kebab-case, camelCase or PascalCase, as well as an enum Ecase .
Example of usage (besides the included table example):
import fikira/Text/1 as t
i_case = input.enum(t.Ecase.camel)
if barstate.islast
sentence = "this sentence contains words"
label.new(bar_index, 0, t.toCase(fromText = sentence, case = str.tostring(i_case)), style=label.style_label_lower_right)
label.new(bar_index, 0, t.toCase(fromText = sentence, case = "snake_case" ), style=label.style_label_lower_left )
label.new(bar_index, 0, t.toCase(fromText = sentence, case = "PascalCase" ), style=label.style_label_upper_right)
label.new(bar_index, 0, t.toCase(fromText = sentence, case = "SNAKE_CASE" ), style=label.style_label_upper_left )
🔹 Sort
The sort(strings, order, sortByUnicodeDecimalNumbers) method returns a sorted array of strings.
strings: array of strings, for example words = array.from("Aword", "beyond", "Space", "salt", "pepper", "swing", "someThing", "otherThing", "12345", "_firstWord")
order: "asc" / "desc" (ascending / descending)
sortByUnicodeDecimalNumbers: true/false; default = false
_____
• sortByUnicodeDecimalNumbers: every Unicode character is linked to a Unicode Decimal number ( wikipedia.org/wiki/List_of_Unicode_characters ), for example:
1 49
2 50
3 51
...
A 65
B 66
...
S 83
...
_ 95
` 96
a 97
b 98
...
o 111
p 112
q 113
r 114
s 115
...
This means, if we sort without adjusting ( sortByUnicodeDecimalNumbers = true ), in ascending order, the letter b (98 - small) would be after S (83 - Capital).
By disabling sortByUnicodeDecimalNumbers , Capital letters are intermediate transformed to str.lower() after which the Unicode Decimal number is retrieved from the small number instead of the capital number. For example S (83) -> s (115), after which the number 115 is used to sort instead of 83.
Example of usage (besides the included table example):
import fikira/Text/1 as t
if barstate.islast
aWords = array.from("Aword", "beyond", "Space", "salt", "pepper", "swing", "someThing", "otherThing", "12345", "_firstWord")
label.new(bar_index, 0, str.tostring(t.sort(strings= aWords, order = 'asc' , sortByUnicodeDecimalNumbers = false)), style=label.style_label_lower_right)
label.new(bar_index, 0, str.tostring(t.sort(strings= aWords, order = 'desc', sortByUnicodeDecimalNumbers = false)), style=label.style_label_lower_left )
label.new(bar_index, 0, str.tostring(t.sort(strings= aWords, order = 'asc' , sortByUnicodeDecimalNumbers = true )), style=label.style_label_upper_right)
label.new(bar_index, 0, str.tostring(t.sort(strings= aWords, order = 'desc', sortByUnicodeDecimalNumbers = true )), style=label.style_label_upper_left )
🔸 Methods/functions
method toFont(fromText, font)
toFont : Transforms text into the selected font
Namespace types: series string, simple string, input string, const string
Parameters:
fromText (string)
font (string)
Returns: `fromText` transformed to desired `font`
method toCase(fromText, case)
toCase : formats text to snake_case, UPPER SNAKE_CASE, kebab-case, camelCase or PascalCase
Namespace types: series string, simple string, input string, const string
Parameters:
fromText (string)
case (string)
Returns: `fromText` formatted to desired `case`
method sort(strings, order, sortByUnicodeDecimalNumbers)
sort : sorts an array of strings, ascending/descending and by Unicode Decimal numbers or not.
Namespace types: array
Parameters:
strings (array)
order (string)
sortByUnicodeDecimalNumbers (bool)
Returns: Sorted array of strings
Anomalous Holonomy Field Theory🌌 Anomalous Holonomy Field Theory (AHFT) - Revolutionary Quantum Market Analysis
Where Theoretical Physics Meets Trading Reality
A Groundbreaking Synthesis of Differential Geometry, Quantum Field Theory, and Market Dynamics
🔬 THEORETICAL FOUNDATION - THE MATHEMATICS OF MARKET REALITY
The Anomalous Holonomy Field Theory represents an unprecedented fusion of advanced mathematical physics with practical market analysis. This isn't merely another indicator repackaging old concepts - it's a fundamentally new lens through which to view and understand market structure .
1. HOLONOMY GROUPS (Differential Geometry)
In differential geometry, holonomy measures how vectors change when parallel transported around closed loops in curved space. Applied to markets:
Mathematical Formula:
H = P exp(∮_C A_μ dx^μ)
Where:
P = Path ordering operator
A_μ = Market connection (price-volume gauge field)
C = Closed price path
Market Implementation:
The holonomy calculation measures how price "remembers" its journey through market space. When price returns to a previous level, the holonomy captures what has changed in the market's internal geometry. This reveals:
Hidden curvature in the market manifold
Topological obstructions to arbitrage
Geometric phase accumulated during price cycles
2. ANOMALY DETECTION (Quantum Field Theory)
Drawing from the Adler-Bell-Jackiw anomaly in quantum field theory:
Mathematical Formula:
∂_μ j^μ = (e²/16π²)F_μν F̃^μν
Where:
j^μ = Market current (order flow)
F_μν = Field strength tensor (volatility structure)
F̃^μν = Dual field strength
Market Application:
Anomalies represent symmetry breaking in market structure - moments when normal patterns fail and extraordinary opportunities arise. The system detects:
Spontaneous symmetry breaking (trend reversals)
Vacuum fluctuations (volatility clusters)
Non-perturbative effects (market crashes/melt-ups)
3. GAUGE THEORY (Theoretical Physics)
Markets exhibit gauge invariance - the fundamental physics remains unchanged under certain transformations:
Mathematical Formula:
A'_μ = A_μ + ∂_μΛ
This ensures our signals are gauge-invariant observables , immune to arbitrary market "coordinate changes" like gaps or reference point shifts.
4. TOPOLOGICAL DATA ANALYSIS
Using persistent homology and Morse theory:
Mathematical Formula:
β_k = dim(H_k(X))
Where β_k are the Betti numbers describing topological features that persist across scales.
🎯 REVOLUTIONARY SIGNAL CONFIGURATION
Signal Sensitivity (0.5-12.0, default 2.5)
Controls the responsiveness of holonomy field calculations to market conditions. This parameter directly affects the threshold for detecting quantum phase transitions in price action.
Optimization by Timeframe:
Scalping (1-5min): 1.5-3.0 for rapid signal generation
Day Trading (15min-1H): 2.5-5.0 for balanced sensitivity
Swing Trading (4H-1D): 5.0-8.0 for high-quality signals only
Score Amplifier (10-200, default 50)
Scales the raw holonomy field strength to produce meaningful signal values. Higher values amplify weak signals in low-volatility environments.
Signal Confirmation Toggle
When enabled, enforces additional technical filters (EMA and RSI alignment) to reduce false positives. Essential for conservative strategies.
Minimum Bars Between Signals (1-20, default 5)
Prevents overtrading by enforcing quantum decoherence time between signals. Higher values reduce whipsaws in choppy markets.
👑 ELITE EXECUTION SYSTEM
Execution Modes:
Conservative Mode:
Stricter signal criteria
Higher quality thresholds
Ideal for stable market conditions
Adaptive Mode:
Self-adjusting parameters
Balances signal frequency with quality
Recommended for most traders
Aggressive Mode:
Maximum signal sensitivity
Captures rapid market moves
Best for experienced traders in volatile conditions
Dynamic Position Sizing:
When enabled, the system scales position size based on:
Holonomy field strength
Current volatility regime
Recent performance metrics
Advanced Exit Management:
Implements trailing stops based on ATR and signal strength, with mode-specific multipliers for optimal profit capture.
🧠 ADAPTIVE INTELLIGENCE ENGINE
Self-Learning System:
The strategy analyzes recent trade outcomes and adjusts:
Risk multipliers based on win/loss ratios
Signal weights according to performance
Market regime detection for environmental adaptation
Learning Speed (0.05-0.3):
Controls adaptation rate. Higher values = faster learning but potentially unstable. Lower values = stable but slower adaptation.
Performance Window (20-100 trades):
Number of recent trades analyzed for adaptation. Longer windows provide stability, shorter windows increase responsiveness.
🎨 REVOLUTIONARY VISUAL SYSTEM
1. Holonomy Field Visualization
What it shows: Multi-layer quantum field bands representing market resonance zones
How to interpret:
Blue/Purple bands = Primary holonomy field (strongest resonance)
Band width = Field strength and volatility
Price within bands = Normal quantum state
Price breaking bands = Quantum phase transition
Trading application: Trade reversals at band extremes, breakouts on band violations with strong signals.
2. Quantum Portals
What they show: Entry signals with recursive depth patterns indicating momentum strength
How to interpret:
Upward triangles with portals = Long entry signals
Downward triangles with portals = Short entry signals
Portal depth = Signal strength and expected momentum
Color intensity = Probability of success
Trading application: Enter on portal appearance, with size proportional to portal depth.
3. Field Resonance Bands
What they show: Fibonacci-based harmonic price zones where quantum resonance occurs
How to interpret:
Dotted circles = Minor resonance levels
Solid circles = Major resonance levels
Color coding = Resonance strength
Trading application: Use as dynamic support/resistance, expect reactions at resonance zones.
4. Anomaly Detection Grid
What it shows: Fractal-based support/resistance with anomaly strength calculations
How to interpret:
Triple-layer lines = Major fractal levels with high anomaly probability
Labels show: Period (H8-H55), Price, and Anomaly strength (φ)
⚡ symbol = Extreme anomaly detected
● symbol = Strong anomaly
○ symbol = Normal conditions
Trading application: Expect major moves when price approaches high anomaly levels. Use for precise entry/exit timing.
5. Phase Space Flow
What it shows: Background heatmap revealing market topology and energy
How to interpret:
Dark background = Low market energy, range-bound
Purple glow = Building energy, trend developing
Bright intensity = High energy, strong directional move
Trading application: Trade aggressively in bright phases, reduce activity in dark phases.
📊 PROFESSIONAL DASHBOARD METRICS
Holonomy Field Strength (-100 to +100)
What it measures: The Wilson loop integral around price paths
>70: Strong positive curvature (bullish vortex)
<-70: Strong negative curvature (bearish collapse)
Near 0: Flat connection (range-bound)
Anomaly Level (0-100%)
What it measures: Quantum vacuum expectation deviation
>70%: Major anomaly (phase transition imminent)
30-70%: Moderate anomaly (elevated volatility)
<30%: Normal quantum fluctuations
Quantum State (-1, 0, +1)
What it measures: Market wave function collapse
+1: Bullish eigenstate |↑⟩
0: Superposition (uncertain)
-1: Bearish eigenstate |↓⟩
Signal Quality Ratings
LEGENDARY: All quantum fields aligned, maximum probability
EXCEPTIONAL: Strong holonomy with anomaly confirmation
STRONG: Good field strength, moderate anomaly
MODERATE: Decent signals, some uncertainty
WEAK: Minimal edge, high quantum noise
Performance Metrics
Win Rate: Rolling performance with emoji indicators
Daily P&L: Real-time profit tracking
Adaptive Risk: Current risk multiplier status
Market Regime: Bull/Bear classification
🏆 WHY THIS CHANGES EVERYTHING
Traditional technical analysis operates on 100-year-old principles - moving averages, support/resistance, and pattern recognition. These work because many traders use them, creating self-fulfilling prophecies.
AHFT transcends this limitation by analyzing markets through the lens of fundamental physics:
Markets have geometry - The holonomy calculations reveal this hidden structure
Price has memory - The geometric phase captures path-dependent effects
Anomalies are predictable - Quantum field theory identifies symmetry breaking
Everything is connected - Gauge theory unifies disparate market phenomena
This isn't just a new indicator - it's a new way of thinking about markets . Just as Einstein's relativity revolutionized physics beyond Newton's mechanics, AHFT revolutionizes technical analysis beyond traditional methods.
🔧 OPTIMAL SETTINGS FOR MNQ 10-MINUTE
For the Micro E-mini Nasdaq-100 on 10-minute timeframe:
Signal Sensitivity: 2.5-3.5
Score Amplifier: 50-70
Execution Mode: Adaptive
Min Bars Between: 3-5
Theme: Quantum Nebula or Dark Matter
💭 THE JOURNEY - FROM IMPOSSIBLE THEORY TO TRADING REALITY
Creating AHFT was a mathematical odyssey that pushed the boundaries of what's possible in Pine Script. The journey began with a seemingly impossible question: Could the profound mathematical structures of theoretical physics be translated into practical trading tools?
The Theoretical Challenge:
Months were spent diving deep into differential geometry textbooks, studying the works of Chern, Simons, and Witten. The mathematics of holonomy groups and gauge theory had never been applied to financial markets. Translating abstract mathematical concepts like parallel transport and fiber bundles into discrete price calculations required novel approaches and countless failed attempts.
The Computational Nightmare:
Pine Script wasn't designed for quantum field theory calculations. Implementing the Wilson loop integral, managing complex array structures for anomaly detection, and maintaining computational efficiency while calculating geometric phases pushed the language to its limits. There were moments when the entire project seemed impossible - the script would timeout, produce nonsensical results, or simply refuse to compile.
The Breakthrough Moments:
After countless sleepless nights and thousands of lines of code, breakthrough came through elegant simplifications. The realization that market anomalies follow patterns similar to quantum vacuum fluctuations led to the revolutionary anomaly detection system. The discovery that price paths exhibit holonomic memory unlocked the geometric phase calculations.
The Visual Revolution:
Creating visualizations that could represent 4-dimensional quantum fields on a 2D chart required innovative approaches. The multi-layer holonomy field, recursive quantum portals, and phase space flow representations went through dozens of iterations before achieving the perfect balance of beauty and functionality.
The Balancing Act:
Perhaps the greatest challenge was maintaining mathematical rigor while ensuring practical trading utility. Every formula had to be both theoretically sound and computationally efficient. Every visual had to be both aesthetically pleasing and information-rich.
The result is more than a strategy - it's a synthesis of pure mathematics and market reality that reveals the hidden order within apparent chaos.
📚 INTEGRATED DOCUMENTATION
Once applied to your chart, AHFT includes comprehensive tooltips on every input parameter. The source code contains detailed explanations of the mathematical theory, practical applications, and optimization guidelines. This published description provides the overview - the indicator itself is a complete educational resource.
⚠️ RISK DISCLAIMER
While AHFT employs advanced mathematical models derived from theoretical physics, markets remain inherently unpredictable. No mathematical model, regardless of sophistication, can guarantee future results. This strategy uses realistic commission ($0.62 per contract) and slippage (1 tick) in all calculations. Past performance does not guarantee future results. Always use appropriate risk management and never risk more than you can afford to lose.
🌟 CONCLUSION
The Anomalous Holonomy Field Theory represents a quantum leap in technical analysis - literally. By applying the profound insights of differential geometry, quantum field theory, and gauge theory to market analysis, AHFT reveals structure and opportunities invisible to traditional methods.
From the holonomy calculations that capture market memory to the anomaly detection that identifies phase transitions, from the adaptive intelligence that learns and evolves to the stunning visualizations that make the invisible visible, every component works in mathematical harmony.
This is more than a trading strategy. It's a new lens through which to view market reality.
Trade with the precision of physics. Trade with the power of mathematics. Trade with AHFT.
I hope this serves as a good replacement for Quantum Edge Pro - Adaptive AI until I'm able to fix it.
— Dskyz, Trade with insight. Trade with anticipation.
WebhookGeneratorLibrary "WebhookGenerator"
Generates Json objects for webhook messages.
GenerateOT(license_id, symbol, action, order_type, trade_type, size, price, tp, sl, risk, trailPrice, trailOffset)
CreateOrderTicket: Establishes a order ticket.
Parameters:
license_id (string) : Provide your license index
symbol (string) : Symbol on which to execute the trade
action (string) : Execution method of the trade : "MRKT" or "PENDING"
order_type (string) : Direction type of the order: "BUY" or "SELL"
trade_type (string) : Is it a "SPREAD" trade or a "SINGLE" symbol execution?
size (float) : Size of the trade, in units
price (float) : If the order is pending you must specify the execution price
tp (float) : (Optional) Take profit of the order
sl (float) : (Optional) Stop loss of the order
risk (float) : Percent to risk for the trade, if size not specified
trailPrice (float) : (Optional) Price at which trailing stop is starting
trailOffset (float) : (Optional) Amount to trail by
Returns: Return Order string
Volume Profile & Smart Money Explorer🔍 Volume Profile & Smart Money Explorer: Decode Institutional Footprints
Master the art of institutional trading with this sophisticated volume analysis tool. Track smart money movements, identify peak liquidity windows, and align your trades with major market participants.
🌟 Key Features:
📊 Triple-Layer Volume Analysis
• Total Volume Patterns
• Directional Volume Split (Up/Down)
• Institutional Flow Detection
• Real-time Smart Money Tracking
• Historical Pattern Recognition
⚡ Smart Money Detection
• Institutional Trade Identification
• Large Block Order Tracking
• Smart Money Concentration Periods
• Whale Activity Alerts
• Volume Threshold Analysis
📈 Advanced Profiling
• Hourly Volume Distribution
• Directional Bias Analysis
• Liquidity Heat Maps
• Volume Pattern Recognition
• Custom Threshold Settings
🎯 Strategic Applications:
Institutional Trading:
• Track Big Player Movements
• Identify Accumulation/Distribution
• Follow Smart Money Flow
• Detect Institutional Trading Windows
• Monitor Block Orders
Risk Management:
• Identify High Liquidity Windows
• Avoid Thin Market Periods
• Optimize Position Sizing
• Track Market Participation
• Monitor Volume Quality
Market Analysis:
• Volume Pattern Recognition
• Smart Money Flow Analysis
• Liquidity Window Identification
• Institutional Activity Cycles
• Market Depth Analysis
💡 Perfect For:
• Professional Traders
• Volume Profile Traders
• Institutional Traders
• Risk Managers
• Algorithmic Traders
• Smart Money Followers
• Day Traders
• Swing Traders
📊 Key Metrics:
• Normalized Volume Profiles
• Institutional Thresholds
• Directional Volume Split
• Smart Money Concentration
• Historical Patterns
• Real-time Analysis
⚡ Trading Edge:
• Trade with Institution Flow
• Identify Optimal Entry Points
• Recognize Distribution Patterns
• Follow Smart Money Positioning
• Avoid Thin Markets
• Capitalize on Peak Liquidity
🎓 Educational Value:
• Understand Market Structure
• Learn Volume Analysis
• Master Institutional Patterns
• Develop Market Intuition
• Track Smart Money Flow
🛠️ Customization:
• Adjustable Time Windows
• Flexible Volume Thresholds
• Multiple Timeframe Analysis
• Custom Alert Settings
• Visual Preference Options
Whether you're tracking institutional flows in crypto markets or following smart money in traditional markets, the Volume Profile & Smart Money Explorer provides the deep insights needed to trade alongside the biggest players.
Transform your trading from retail guesswork to institutional precision. Know exactly when and where smart money moves, and position yourself ahead of major market shifts.
#VolumeProfile #SmartMoney #InstitutionalTrading #MarketAnalysis #TradingView #VolumeAnalysis #CryptoTrading #ForexTrading #TechnicalAnalysis #Trading #PriceAction #MarketStructure #OrderFlow #Liquidity #RiskManagement #TradingStrategy #DayTrading #SwingTrading #AlgoTrading #QuantitativeTrading
Poisson Projection of Price Levels### **Poisson Projection of Price Levels**
**Overview:**
The *Poisson Projection of Price Levels* is a cutting-edge technical indicator designed to identify and visualize potential support and resistance levels based on historical price interactions. By leveraging the Poisson distribution, this tool dynamically adjusts the significance of each price level's past "touches" to project future interactions with varying degrees of probability. This probabilistic approach offers traders a nuanced view of where price levels may hold or react in upcoming bars, enhancing both analysis and trading strategies.
---
**🔍 **Math & Methodology**
1. **Strata Levels:**
- **Definition:** Strata are horizontal lines spaced evenly around the current closing price.
- **Calculation:**
\
where \(i\) ranges from 0 to \(\text{Strata Count} - 1\).
2. **Forecast Iterations:**
- **Structure:** The indicator projects five forecast iterations into the future, each spaced by a Fibonacci sequence of bars: 2, 3, 5, 8, and 13 bars ahead. This spacing is inspired by the Fibonacci sequence, which is prevalent in financial market analysis for identifying key levels.
- **Purpose:** Each iteration represents a distinct forecast point where the price may interact with the strata, allowing for a multi-step projection of potential price levels.
3. **Touch Counting:**
- **Definition:** A "touch" occurs when the closing price of a bar is within half the increment of a stratum level.
- **Process:** For each stratum and each forecast iteration, the indicator counts the number of touches within a specified lookback window (e.g., 80 bars), offset by the forecasted position. This ensures that each iteration's touch count is independent and contextually relevant to its forecast horizon.
- **Adjustment:** Each forecast iteration analyzes a unique segment of the lookback window, offset by its forecasted position to ensure independent probability calculations.
4. **Poisson Probability Calculation:**
- **Formula:**
\
\
- **Interpretation:** \(p(k=1)\) represents the probability of exactly one touch occurring within the lookback window for each stratum and iteration.
- **Application:** This probability is used to determine the transparency of each stratum line, where higher probabilities result in more opaque (less transparent) lines, indicating stronger historical significance.
5. **Transparency Mapping:**
- **Calculation:**
\
- **Purpose:** Maps the Poisson probability to a visual transparency level, enhancing the readability of significant strata levels.
- **Outcome:** Strata with higher probabilities (more historical touches) appear more opaque, while those with lower probabilities appear fainter.
---
**📊 **Comparability to Standard Techniques**
1. **Support and Resistance Levels:**
- **Traditional Approach:** Traders identify support and resistance based on historical price reversals, pivot points, or psychological price levels.
- **Poisson Projection:** Automates and quantifies this process by statistically analyzing the frequency of price interactions with specific levels, providing a probabilistic measure of significance.
2. **Statistical Modeling:**
- **Standard Models:** Techniques like Moving Averages, Bollinger Bands, or Fibonacci Retracements offer dynamic and rule-based levels but lack direct probabilistic interpretation.
- **Poisson Projection:** Introduces a discrete event probability framework, offering a unique blend of statistical rigor and visual clarity that complements traditional indicators.
3. **Event-Based Analysis:**
- **Financial Industry Practices:** Event studies and high-frequency trading models often use Poisson processes to model order arrivals or price jumps.
- **Indicator Application:** While not identical, the use of Poisson probabilities in this indicator draws inspiration from event-based modeling, applying it to the context of price level interactions.
---
**💡 **Strengths & Advantages**
1. **Innovative Visualization:**
- Combines statistical probability with traditional support/resistance visualization, offering a fresh perspective on price level significance.
2. **Dynamic Adaptability:**
- Parameters like strata increment, lookback window, and probability threshold are user-defined, allowing customization across different markets and timeframes.
3. **Independent Probability Calculations:**
- Each forecast iteration calculates its own Poisson probability, ensuring that projections are contextually relevant and independent of other iterations.
4. **Clear Visual Cues:**
- Transparency-based coloring intuitively highlights significant price levels, making it easier for traders to identify key areas of interest at a glance.
---
**⚠️ **Limitations & Considerations**
1. **Poisson Assumptions:**
- Assumes that touches occur independently and at a constant average rate (\(\lambda\)), which may not always align with market realities characterized by trends and volatility clustering.
2. **Computational Intensity:**
- Managing multiple iterations and strata can be resource-intensive, potentially affecting performance on lower-powered devices or with very high lookback windows.
3. **Interpretation Complexity:**
- While transparency offers visual clarity, understanding the underlying probability calculations requires a basic grasp of Poisson statistics, which may be a barrier for some traders.
---
**📢 **How to Use It**
1. **Add to TradingView:**
- Open TradingView and navigate to the Pine Script Editor.
- Paste the script above and click **Add to Chart**.
2. **Configure Inputs:**
- **Strata Increment:** Set the desired price step between strata (e.g., `0.1` for 10 cents).
- **Lookback Window:** Define how many past bars to consider for calculating Poisson probabilities (e.g., `80`).
- **Probability Transparency Threshold (%):** Set the threshold percentage to map probabilities to line transparency (e.g., `25%`).
3. **Understand the Forecast Iterations:**
- The indicator projects five forecast points into the future at bar spacings of 2, 3, 5, 8, and 13 bars ahead.
- Each iteration independently calculates its Poisson probability based on the touch counts within its specific lookback window offset by its forecasted position.
4. **Interpret the Visualization:**
- **Opaque Lines:** Indicate higher Poisson probabilities, suggesting historically significant price levels that are more likely to interact again.
- **Fainter Lines:** Represent lower probabilities, indicating less historically significant levels that may be less likely to interact.
- **Forecast Spacing:** The spacing of 2, 3, 5, 8, and 13 bars ahead aligns with Fibonacci principles, offering a natural progression in forecast horizons.
5. **Apply to Trading Strategies:**
- **Support/Resistance Identification:** Use the opaque lines as potential support and resistance levels for placing trades.
- **Entry and Exit Points:** Anticipate price interactions at forecasted levels to plan strategic entries and exits.
- **Risk Management:** Utilize the transparency mapping to determine where to place stop-loss and take-profit orders based on the probability of price interactions.
6. **Customize as Needed:**
- Adjust the **Strata Increment** to fit different price ranges or volatility levels.
- Modify the **Lookback Window** to capture more or fewer historical touches, adapting to different timeframes or market conditions.
- Tweak the **Probability Transparency Threshold** to control the sensitivity of transparency mapping to Poisson probabilities.
**📈 **Practical Applications**
1. **Identifying Key Levels:**
- Quickly visualize which price levels have historically had significant interactions, aiding in the identification of potential support and resistance zones.
2. **Forecasting Price Reactions:**
- Use the forecast iterations to anticipate where price may interact in the near future, assisting in planning entry and exit points.
3. **Risk Management:**
- Determine areas of high probability for price reversals or consolidations, enabling better placement of stop-loss and take-profit orders.
4. **Market Analysis:**
- Assess the strength of market levels over different forecast horizons, providing a multi-layered understanding of market structure.
---
**🔗 **Conclusion**
The *Poisson Projection of Price Levels* bridges the gap between statistical modeling and traditional technical analysis, offering traders a sophisticated tool to quantify and visualize the significance of price levels. By integrating Poisson probabilities with dynamic transparency mapping, this indicator provides a unique and insightful perspective on potential support and resistance zones, enhancing both analysis and trading strategies.
---
**📞 **Contact:**
For support or inquiries, please contact me on TradingView!
---
**📢 **Join the Conversation!**
Have questions, feedback, or suggestions for further enhancements? Feel free to comment below or reach out directly. Your input helps refine and evolve this tool to better serve the trading community.
---
**Happy Trading!** 🚀
Trend Trader-Remastered StrategyOfficial Strategy for Trend Trader - Remastered
Indicator: Trend Trader-Remastered (TTR)
Overview:
The Trend Trader-Remastered is a refined and highly sophisticated implementation of the Parabolic SAR designed to create strategic buy and sell entry signals, alongside precision take profit and re-entry signals based on marked Bill Williams (BW) fractals. Built with a deep emphasis on clarity and accuracy, this indicator ensures that only relevant and meaningful signals are generated, eliminating any unnecessary entries or exits.
Please check the indicator details and updates via the link above.
Important Disclosure:
My primary objective is to provide realistic strategies and a code base for the TradingView Community. Therefore, the default settings of the strategy version of the indicator have been set to reflect realistic world trading scenarios and best practices.
Key Features:
Strategy execution date&time range.
Take Profit Reduction Rate: The percentage of progressive reduction on active position size for take profit signals.
Example:
TP Reduce: 10%
Entry Position Size: 100
TP1: 100 - 10 = 90
TP2: 90 - 9 = 81
Re-Entry When Rate: The percentage of position size on initial entry of the signal to determine re-entry.
Example:
RE When: 50%
Entry Position Size: 100
Re-Entry Condition: Active Position Size < 50
Re-Entry Fill Rate: The percentage of position size on initial entry of the signal to be completed.
Example:
RE Fill: 75%
Entry Position Size: 100
Active Position Size: 50
Re-Entry Order Size: 25
Final Active Position Size:75
Important: Even RE When condition is met, the active position size required to drop below RE Fill rate to trigger re-entry order.
Key Points:
'Process Orders on Close' is enabled as Take Profit and Re-Entry signals must be executed on candle close.
'Calculate on Every Tick' is enabled as entry signals are required to be executed within candle time.
'Initial Capital' has been set to 10,000 USD.
'Default Quantity Type' has been set to 'Percent of Equity'.
'Default Quantity' has been set to 10% as the best practice of investing 10% of the assets.
'Currency' has been set to USD.
'Commission Type' has been set to 'Commission Percent'
'Commission Value' has been set to 0.05% to reflect the most realistic results with a common taker fee value.
DCA Strategy with Mean Reversion and Bollinger BandDCA Strategy with Mean Reversion and Bollinger Band
The Dollar-Cost Averaging (DCA) Strategy with Mean Reversion and Bollinger Bands is a sophisticated trading strategy that combines the principles of DCA, mean reversion, and technical analysis using Bollinger Bands. This strategy aims to capitalize on market corrections by systematically entering positions during periods of price pullbacks and reversion to the mean.
Key Concepts and Principles
1. Dollar-Cost Averaging (DCA)
DCA is an investment strategy that involves regularly purchasing a fixed dollar amount of an asset, regardless of its price. The idea behind DCA is that by spreading out investments over time, the impact of market volatility is reduced, and investors can avoid making large investments at inopportune times. The strategy reduces the risk of buying all at once during a market high and can smooth out the cost of purchasing assets over time.
In the context of this strategy, the Investment Amount (USD) is set by the user and represents the amount of capital to be invested in each buy order. The strategy executes buy orders whenever the price crosses below the lower Bollinger Band, which suggests a potential market correction or pullback. This is an effective way to average the entry price and avoid the emotional pitfalls of trying to time the market perfectly.
2. Mean Reversion
Mean reversion is a concept that suggests prices will tend to return to their historical average or mean over time. In this strategy, mean reversion is implemented using the Bollinger Bands, which are based on a moving average and standard deviation. The lower band is considered a potential buy signal when the price crosses below it, indicating that the asset has become oversold or underpriced relative to its historical average. This triggers the DCA buy order.
Mean reversion strategies are popular because they exploit the natural tendency of prices to revert to their mean after experiencing extreme deviations, such as during market corrections or panic selling.
3. Bollinger Bands
Bollinger Bands are a technical analysis tool that consists of three lines:
Middle Band: The moving average, usually a 200-period Exponential Moving Average (EMA) in this strategy. This serves as the "mean" or baseline.
Upper Band: The middle band plus a certain number of standard deviations (multiplier). The upper band is used to identify overbought conditions.
Lower Band: The middle band minus a certain number of standard deviations (multiplier). The lower band is used to identify oversold conditions.
In this strategy, the Bollinger Bands are used to identify potential entry points for DCA trades. When the price crosses below the lower band, this is seen as a potential opportunity for mean reversion, suggesting that the asset may be oversold and could reverse back toward the middle band (the EMA). Conversely, when the price crosses above the upper band, it indicates overbought conditions and signals potential market exhaustion.
4. Time-Based Entry and Exit
The strategy has specific entry and exit points defined by time parameters:
Open Date: The date when the strategy begins opening positions.
Close Date: The date when all positions are closed.
This time-bound approach ensures that the strategy is active only during a specified window, which can be useful for testing specific market conditions or focusing on a particular time frame.
5. Position Sizing
Position sizing is determined by the Investment Amount (USD), which is the fixed amount to be invested in each buy order. The quantity of the asset to be purchased is calculated by dividing the investment amount by the current price of the asset (investment_amount / close). This ensures that the amount invested remains constant despite fluctuations in the asset's price.
6. Closing All Positions
The strategy includes an exit rule that closes all positions once the specified close date is reached. This allows for controlled exits and limits the exposure to market fluctuations beyond the strategy's timeframe.
7. Background Color Based on Price Relative to Bollinger Bands
The script uses the background color of the chart to provide visual feedback about the price's relationship with the Bollinger Bands:
Red background indicates the price is above the upper band, signaling overbought conditions.
Green background indicates the price is below the lower band, signaling oversold conditions.
This provides an easy-to-interpret visual cue for traders to assess the current market environment.
Postscript: Configuring Initial Capital for Backtesting
To ensure the backtest results align with the actual investment scenario, users must adjust the Initial Capital in the TradingView strategy properties. This is done by calculating the Initial Capital as the product of the Total Closed Trades and the Investment Amount (USD). For instance:
If the user is investing 100 USD per trade and has 10 closed trades, the Initial Capital should be set to 1,000 USD.
Similarly, if the user is investing 200 USD per trade and has 24 closed trades, the Initial Capital should be set to 4,800 USD.
This adjustment ensures that the backtesting results reflect the actual capital deployed in the strategy and provides an accurate representation of potential gains and losses.
Conclusion
The DCA strategy with Mean Reversion and Bollinger Bands is a systematic approach to investing that leverages the power of regular investments and technical analysis to reduce market timing risks. By combining DCA with the insights offered by Bollinger Bands and mean reversion, this strategy offers a structured way to navigate volatile markets while targeting favorable entry points. The clear entry and exit rules, coupled with time-based constraints, make it a robust and disciplined approach to long-term investing.
Hybrid Adaptive Double Exponential Smoothing🙏🏻 This is HADES (Hybrid Adaptive Double Exponential Smoothing) : fully data-driven & adaptive exponential smoothing method, that gains all the necessary info directly from data in the most natural way and needs no subjective parameters & no optimizations. It gets applied to data itself -> to fit residuals & one-point forecast errors, all at O(1) algo complexity. I designed it for streaming high-frequency univariate time series data, such as medical sensor readings, orderbook data, tick charts, requests generated by a backend, etc.
The HADES method is:
fit & forecast = a + b * (1 / alpha + T - 1)
T = 0 provides in-sample fit for the current datum, and T + n provides forecast for n datapoints.
y = input time series
a = y, if no previous data exists
b = 0, if no previous data exists
otherwise:
a = alpha * y + (1 - alpha) * a
b = alpha * (a - a ) + (1 - alpha) * b
alpha = 1 / sqrt(len * 4)
len = min(ceil(exp(1 / sig)), available data)
sig = sqrt(Absolute net change in y / Sum of absolute changes in y)
For the start datapoint when both numerator and denominator are zeros, we define 0 / 0 = 1
...
The same set of operations gets applied to the data first, then to resulting fit absolute residuals to build prediction interval, and finally to absolute forecasting errors (from one-point ahead forecast) to build forecasting interval:
prediction interval = data fit +- resoduals fit * k
forecasting interval = data opf +- errors fit * k
where k = multiplier regulating intervals width, and opf = one-point forecasts calculated at each time t
...
How-to:
0) Apply to your data where it makes sense, eg. tick data;
1) Use power transform to compensate for multiplicative behavior in case it's there;
2) If you have complete data or only the data you need, like the full history of adjusted close prices: go to the next step; otherwise, guided by your goal & analysis, adjust the 'start index' setting so the calculations will start from this point;
3) Use prediction interval to detect significant deviations from the process core & make decisions according to your strategy;
4) Use one-point forecast for nowcasting;
5) Use forecasting intervals to ~ understand where the next datapoints will emerge, given the data-generating process will stay the same & lack structural breaks.
I advise k = 1 or 1.5 or 4 depending on your goal, but 1 is the most natural one.
...
Why exponential smoothing at all? Why the double one? Why adaptive? Why not Holt's method?
1) It's O(1) algo complexity & recursive nature allows it to be applied in an online fashion to high-frequency streaming data; otherwise, it makes more sense to use other methods;
2) Double exponential smoothing ensures we are taking trends into account; also, in order to model more complex time series patterns such as seasonality, we need detrended data, and this method can be used to do it;
3) The goal of adaptivity is to eliminate the window size question, in cases where it doesn't make sense to use cumulative moving typical value;
4) Holt's method creates a certain interaction between level and trend components, so its results lack symmetry and similarity with other non-recursive methods such as quantile regression or linear regression. Instead, I decided to base my work on the original double exponential smoothing method published by Rob Brown in 1956, here's the original source , it's really hard to find it online. This cool dude is considered the one who've dropped exponential smoothing to open access for the first time🤘🏻
R&D; log & explanations
If you wanna read this, you gotta know, you're taking a great responsability for this long journey, and it gonna be one hell of a trip hehe
Machine learning, apprentissage automatique, машинное обучение, digital signal processing, statistical learning, data mining, deep learning, etc., etc., etc.: all these are just artificial categories created by the local population of this wonderful world, but what really separates entities globally in the Universe is solution complexity / algorithmic complexity.
In order to get the game a lil better, it's gonna be useful to read the HTES script description first. Secondly, let me guide you through the whole R&D; process.
To discover (not to invent) the fundamental universal principle of what exponential smoothing really IS, it required the review of the whole concept, understanding that many things don't add up and don't make much sense in currently available mainstream info, and building it all from the beginning while avoiding these very basic logical & implementation flaws.
Given a complete time t, and yet, always growing time series population that can't be logically separated into subpopulations, the very first question is, 'What amount of data do we need to utilize at time t?'. Two answers: 1 and all. You can't really gain much info from 1 datum, so go for the second answer: we need the whole dataset.
So, given the sequential & incremental nature of time series, the very first and basic thing we can do on the whole dataset is to calculate a cumulative , such as cumulative moving mean or cumulative moving median.
Now we need to extend this logic to exponential smoothing, which doesn't use dataset length info directly, but all cool it can be done via a formula that quantifies the relationship between alpha (smoothing parameter) and length. The popular formulas used in mainstream are:
alpha = 1 / length
alpha = 2 / (length + 1)
The funny part starts when you realize that Cumulative Exponential Moving Averages with these 2 alpha formulas Exactly match Cumulative Moving Average and Cumulative (Linearly) Weighted Moving Average, and the same logic goes on:
alpha = 3 / (length + 1.5) , matches Cumulative Weighted Moving Average with quadratic weights, and
alpha = 4 / (length + 2) , matches Cumulative Weighted Moving Average with cubic weghts, and so on...
It all just cries in your shoulder that we need to discover another, native length->alpha formula that leverages the recursive nature of exponential smoothing, because otherwise, it doesn't make sense to use it at all, since the usual CMA and CMWA can be computed incrementally at O(1) algo complexity just as exponential smoothing.
From now on I will not mention 'cumulative' or 'linearly weighted / weighted' anymore, it's gonna be implied all the time unless stated otherwise.
What we can do is to approach the thing logically and model the response with a little help from synthetic data, a sine wave would suffice. Then we can think of relationships: Based on algo complexity from lower to higher, we have this sequence: exponential smoothing @ O(1) -> parametric statistics (mean) @ O(n) -> non-parametric statistics (50th percentile / median) @ O(n log n). Based on Initial response from slow to fast: mean -> median Based on convergence with the real expected value from slow to fast: mean (infinitely approaches it) -> median (gets it quite fast).
Based on these inputs, we need to discover such a length->alpha formula so the resulting fit will have the slowest initial response out of all 3, and have the slowest convergence with expected value out of all 3. In order to do it, we need to have some non-linear transformer in our formula (like a square root) and a couple of factors to modify the response the way we need. I ended up with this formula to meet all our requirements:
alpha = sqrt(1 / length * 2) / 2
which simplifies to:
alpha = 1 / sqrt(len * 8)
^^ as you can see on the screenshot; where the red line is median, the blue line is the mean, and the purple line is exponential smoothing with the formulas you've just seen, we've met all the requirements.
Now we just have to do the same procedure to discover the length->alpha formula but for double exponential smoothing, which models trends as well, not just level as in single exponential smoothing. For this comparison, we need to use linear regression and quantile regression instead of the mean and median.
Quantile regression requires a non-closed form solution to be solved that you can't really implement in Pine Script, but that's ok, so I made the tests using Python & sklearn:
paste.pics
^^ on this screenshot, you can see the same relationship as on the previous screenshot, but now between the responses of quantile regression & linear regression.
I followed the same logic as before for designing alpha for double exponential smoothing (also considered the initial overshoots, but that's a little detail), and ended up with this formula:
alpha = sqrt(1 / length) / 2
which simplifies to:
alpha = 1 / sqrt(len * 4)
Btw, given the pattern you see in the resulting formulas for single and double exponential smoothing, if you ever want to do triple (not Holt & Winters) exponential smoothing, you'll need len * 2 , and just len * 1 for quadruple exponential smoothing. I hope that based on this sequence, you see the hint that Maybe 4 rounds is enough.
Now since we've dealt with the length->alpha formula, we can deal with the adaptivity part.
Logically, it doesn't make sense to use a slower-than-O(1) method to generate input for an O(1) method, so it must be something universal and minimalistic: something that will help us measure consistency in our data, yet something far away from statistics and close enough to topology.
There's one perfect entity that can help us, this is fractal efficiency. The way I define fractal efficiency can be checked at the very beginning of the post, what matters is that I add a square root to the formula that is not typically added.
As explained in the description of my metric QSFS , one of the reasons for SQRT-transformed values of fractal efficiency applied in moving window mode is because they start to closely resemble normal distribution, yet with support of (0, 1). Data with this interesting property (normally distributed yet with finite support) can be modeled with the beta distribution.
Another reason is, in infinitely expanding window mode, fractal efficiency of every time series that exhibits randomness tends to infinitely approach zero, sqrt-transform kind of partially neutralizes this effect.
Yet another reason is, the square root might better reflect the dimensional inefficiency or degree of fractal complexity, since it could balance the influence of extreme deviations from the net paths.
And finally, fractals exhibit power-law scaling -> measures like length, area, or volume scale in a non-linear way. Adding a square root acknowledges this intrinsic property, while connecting our metric with the nature of fractals.
---
I suspect that, given analogies and connections with other topics in geometry, topology, fractals and most importantly positive test results of the metric, it might be that the sqrt transform is the fundamental part of fractal efficiency that should be applied by default.
Now the last part of the ballet is to convert our fractal efficiency to length value. The part about inverse proportionality is obvious: high fractal efficiency aka high consistency -> lower window size, to utilize only the last data that contain brand new information that seems to be highly reliable since we have consistency in the first place.
The non-obvious part is now we need to neutralize the side effect created by previous sqrt transform: our length values are too low, and exponentiation is the perfect candidate to fix it since translating fractal efficiency into window sizes requires something non-linear to reflect the fractal dynamics. More importantly, using exp() was the last piece that let the metric shine, any other transformations & formulas alike I've tried always had some weird results on certain data.
That exp() in the len formula was the last piece that made it all work both on synthetic and on real data.
^^ a standalone script calculating optimal dynamic window size
Omg, THAT took time to write. Comment and/or text me if you need
...
"Versace Pip-Boy, I'm a young gun coming up with no bankroll" 👻
∞
ICT Setup 03 [TradingFinder] Judas Swing NY 9:30am + CHoCH/FVG🔵 Introduction
Judas Swing is an advanced trading setup designed to identify false price movements early in the trading day. This advanced trading strategy operates on the principle that major market players, or "smart money," drive price in a certain direction during the early hours to mislead smaller traders.
This deceptive movement attracts liquidity at specific levels, allowing larger players to execute primary trades in the opposite direction, ultimately causing the price to return to its true path.
The Judas Swing setup functions within two primary time frames, tailored separately for Forex and Stock markets. In the Forex market, the setup uses the 8:15 to 8:30 AM window to identify the high and low points, followed by the 8:30 to 8:45 AM frame to execute the Judas move and identify the CISD Level break, where Order Block and Fair Value Gap (FVG) zones are subsequently detected.
In the Stock market, these time frames shift to 9:15 to 9:30 AM for identifying highs and lows and 9:30 to 9:45 AM for executing the Judas move and CISD Level break.
Concepts such as Order Block and Fair Value Gap (FVG) are crucial in this setup. An Order Block represents a chart region with a high volume of buy or sell orders placed by major financial institutions, marking significant levels where price reacts.
Fair Value Gap (FVG) refers to areas where price has moved rapidly without balance between supply and demand, highlighting zones of potential price action and future liquidity.
Bullish Setup :
Bearish Setup :
🔵 How to Use
The Judas Swing setup enables traders to pinpoint entry and exit points by utilizing Order Block and FVG concepts, helping them align with liquidity-driven moves orchestrated by smart money. This setup applies two distinct time frames for Forex and Stocks to capture early deceptive movements, offering traders optimized entry or exit moments.
🟣 Bullish Setup
In the Bullish Judas Swing setup, the first step is to identify High and Low points within the initial time frame. These levels serve as key points where price may react, forming the basis for analyzing the setup and assisting traders in anticipating future market shifts.
In the second time frame, a critical stage of the bullish setup begins. During this phase, the price may create a false break or Fake Break below the low level, a deceptive move by major players to absorb liquidity. This false move often causes smaller traders to enter positions incorrectly. After this fake-out, the price reverses upward, breaking the CISD Level, a critical point in the market structure, signaling a potential bullish trend.
Upon breaking the CISD Level and reversing upward, the indicator identifies both the Order Block and Fair Value Gap (FVG). The Order Block is an area where major players typically place large buy orders, signaling potential price support. Meanwhile, the FVG marks a region of supply-demand imbalance, signaling areas where price might react.
Ultimately, after these key zones are identified, a trader may open a buy position if the price reaches one of these critical areas—Order Block or FVG—and reacts positively. Trading at these levels enhances the chance of success due to liquidity absorption and support from smart money, marking an opportune time for entering a long position.
🟣 Bearish Setup
In the Bearish Judas Swing setup, analysis begins with marking the High and Low levels in the initial time frame. These levels serve as key zones where price could react, helping to signal possible trend reversals. Identifying these levels is essential for locating significant bearish zones and positioning traders to capitalize on downward movements.
In the second time frame, the primary bearish setup unfolds. During this stage, price may exhibit a Fake Break above the high, causing a brief move upward and misleading smaller traders into incorrect positions. After this false move, the price typically returns downward, breaking the CISD Level—a crucial bearish trend indicator.
With the CISD Level broken and a bearish trend confirmed, the indicator identifies the Order Block and Fair Value Gap (FVG). The Bearish Order Block is a region where smart money places significant sell orders, prompting a negative price reaction. The FVG denotes an area of supply-demand imbalance, signifying potential selling pressure.
When the price reaches one of these critical areas—the Bearish Order Block or FVG—and reacts downward, a trader may initiate a sell position. Entering trades at these levels, due to increased selling pressure and liquidity absorption, offers traders an advantage in profiting from price declines.
🔵 Settings
Market : The indicator allows users to choose between Forex and Stocks, automatically adjusting the time frames for the "Opening Range" and "Trading Permit" accordingly: Forex: 8:15–8:30 AM for identifying High and Low points, and 8:30–8:45 AM for capturing the Judas move and CISD Level break. Stocks: 9:15–9:30 AM for identifying High and Low points, and 9:30–9:45 AM for executing the Judas move and CISD Level break.
Refine Order Block : Enables finer adjustments to Order Block levels for more accurate price responses.
Mitigation Level OB : Allows users to set specific reaction points within an Order Block, including: Proximal: Closest level to the current price. 50% OB: Midpoint of the Order Block. Distal: Farthest level from the current price.
FVG Filter : The Judas Swing indicator includes a filter for Fair Value Gap (FVG), allowing different filtering based on FVG width: FVG Filter Type: Can be set to "Very Aggressive," "Aggressive," "Defensive," or "Very Defensive." Higher defensiveness narrows the FVG width, focusing on narrower gaps.
Mitigation Level FVG : Like the Order Block, you can set price reaction levels for FVG with options such as Proximal, 50% OB, and Distal.
CISD : The Bar Back Check option enables traders to specify the number of past candles checked for identifying the CISD Level, enhancing CISD Level accuracy on the chart.
🔵 Conclusion
The Judas Swing indicator helps traders spot reliable trading opportunities by detecting false price movements and key levels such as Order Block and FVG. With a focus on early market movements, this tool allows traders to align with major market participants, selecting entry and exit points with greater precision, thereby reducing trading risks.
Its extensive customization options enable adjustments for various market types and trading conditions, giving traders the flexibility to optimize their strategies. Based on ICT techniques and liquidity analysis, this indicator can be highly effective for those seeking precision in their entry points.
Overall, Judas Swing empowers traders to capitalize on significant market movements by leveraging price volatility. Offering precise and dependable signals, this tool presents an excellent opportunity for enhancing trading accuracy and improving performance
Uptrick: RSI Histogram
1. **Introduction to the RSI and Moving Averages**
2. **Detailed Breakdown of the Uptrick: RSI Histogram**
3. **Calculation and Formula**
4. **Visual Representation**
5. **Customization and User Settings**
6. **Trading Strategies and Applications**
7. **Risk Management**
8. **Case Studies and Examples**
9. **Comparison with Other Indicators**
10. **Advanced Usage and Tips**
---
## 1. Introduction to the RSI and Moving Averages
### **1.1 Relative Strength Index (RSI)**
The Relative Strength Index (RSI) is a momentum oscillator developed by J. Welles Wilder and introduced in his 1978 book "New Concepts in Technical Trading Systems." It is widely used in technical analysis to measure the speed and change of price movements.
**Purpose of RSI:**
- **Identify Overbought/Oversold Conditions:** RSI values range from 0 to 100. Traditionally, values above 70 are considered overbought, while values below 30 are considered oversold. These thresholds help traders identify potential reversal points in the market.
- **Trend Strength Measurement:** RSI also indicates the strength of a trend. High RSI values suggest strong bullish momentum, while low values indicate bearish momentum.
**Calculation of RSI:**
1. **Calculate the Average Gain and Loss:** Over a specified period (e.g., 14 days), calculate the average gain and loss.
2. **Compute the Relative Strength (RS):** RS is the ratio of average gain to average loss.
3. **RSI Formula:** RSI = 100 - (100 / (1 + RS))
### **1.2 Moving Averages (MA)**
Moving Averages are used to smooth out price data and identify trends by filtering out short-term fluctuations. Two common types are:
**Simple Moving Average (SMA):** The average of prices over a specified number of periods.
**Exponential Moving Average (EMA):** A type of moving average that gives more weight to recent prices, making it more responsive to recent price changes.
**Smoothed Moving Average (SMA):** Used to reduce the impact of volatility and provide a clearer view of the underlying trend. The RMA, or Running Moving Average, used in the USH script is similar to an EMA but based on the average of RSI values.
## 2. Detailed Breakdown of the Uptrick: RSI Histogram
### **2.1 Indicator Overview**
The Uptrick: RSI Histogram (USH) is a technical analysis tool that combines the RSI with a moving average to create a histogram that reflects momentum and trend strength.
**Key Components:**
- **RSI Calculation:** Determines the relative strength of price movements.
- **Moving Average Application:** Smooths the RSI values to provide a clearer trend indication.
- **Histogram Plotting:** Visualizes the deviation of the smoothed RSI from a neutral level.
### **2.2 Indicator Purpose**
The primary purpose of the USH is to provide a clear visual representation of the market's momentum and trend strength. It helps traders identify:
- **Bullish and Bearish Trends:** By showing how far the smoothed RSI is from the neutral 50 level.
- **Potential Reversal Points:** By highlighting changes in momentum.
### **2.3 Indicator Design**
**RSI Moving Average (RSI MA):** The RSI MA is a smoothed version of the RSI, calculated using a running moving average. This smooths out short-term fluctuations and provides a clearer indication of the underlying trend.
**Histogram Calculation:**
- **Neutral Level:** The histogram is plotted relative to the neutral level of 50. This level represents a balanced market where neither bulls nor bears have dominance.
- **Histogram Values:** The histogram bars show the difference between the RSI MA and the neutral level. Positive values indicate bullish momentum, while negative values indicate bearish momentum.
## 3. Calculation and Formula
### **3.1 RSI Calculation**
The RSI calculation involves:
1. **Average Gain and Loss:** Calculated over the specified length (e.g., 14 periods).
2. **Relative Strength (RS):** RS = Average Gain / Average Loss.
3. **RSI Formula:** RSI = 100 - (100 / (1 + RS)).
### **3.2 Moving Average Calculation**
For the USH indicator, the RSI is smoothed using a running moving average (RMA). The RMA formula is similar to that of the EMA but is based on averaging RSI values over the specified length.
### **3.3 Histogram Calculation**
The histogram value is calculated as:
- **Histogram Value = RSI MA - 50**
**Plotting the Histogram:**
- **Positive Histogram Values:** Indicate that the RSI MA is above the neutral level, suggesting bullish momentum.
- **Negative Histogram Values:** Indicate that the RSI MA is below the neutral level, suggesting bearish momentum.
## 4. Visual Representation
### **4.1 Histogram Bars**
The histogram is plotted as bars on the chart:
- **Bullish Bars:** Colored green when the RSI MA is above 50.
- **Bearish Bars:** Colored red when the RSI MA is below 50.
### **4.2 Customization Options**
Traders can customize:
- **RSI Length:** Adjust the length of the RSI calculation to match their trading style.
- **Bull and Bear Colors:** Choose colors for histogram bars to enhance visual clarity.
### **4.3 Interpretation**
**Bullish Signal:** A histogram bar that moves from red to green indicates a potential shift to a bullish trend.
**Bearish Signal:** A histogram bar that moves from green to red indicates a potential shift to a bearish trend.
## 5. Customization and User Settings
### **5.1 Adjusting RSI Length**
The length parameter determines the number of periods over which the RSI is calculated and smoothed. Shorter lengths make the RSI more sensitive to price changes, while longer lengths provide a smoother view of trends.
### **5.2 Color Settings**
Traders can adjust:
- **Bull Color:** Color of histogram bars indicating bullish momentum.
- **Bear Color:** Color of histogram bars indicating bearish momentum.
**Customization Benefits:**
- **Visual Clarity:** Traders can choose colors that stand out against their chart’s background.
- **Personal Preference:** Adjust settings to match individual trading styles and preferences.
## 6. Trading Strategies and Applications
### **6.1 Trend Following**
**Identifying Entry Points:**
- **Bullish Entry:** When the histogram changes from red to green, it signals a potential entry point for long positions.
- **Bearish Entry:** When the histogram changes from green to red, it signals a potential entry point for short positions.
**Trend Confirmation:** The histogram helps confirm the strength of a trend. Strong, consistent green bars indicate robust bullish momentum, while strong, consistent red bars indicate robust bearish momentum.
### **6.2 Swing Trading**
**Momentum Analysis:**
- **Entry Signals:** Look for significant shifts in the histogram to time entries. A shift from bearish to bullish (red to green) indicates potential for upward movement.
- **Exit Signals:** A shift from bullish to bearish (green to red) suggests a potential weakening of the trend, signaling an exit or reversal point.
### **6.3 Range Trading**
**Market Conditions:**
- **Consolidation:** The histogram close to zero suggests a range-bound market. Traders can use this information to identify support and resistance levels.
- **Breakout Potential:** A significant move away from the neutral level may indicate a potential breakout from the range.
### **6.4 Risk Management**
**Stop-Loss Placement:**
- **Bullish Positions:** Place stop-loss orders below recent support levels when the histogram is green.
- **Bearish Positions:** Place stop-loss orders above recent resistance levels when the histogram is red.
**Position Sizing:** Adjust position sizes based on the strength of the histogram signals. Strong trends (indicated by larger histogram bars) may warrant larger positions, while weaker signals suggest smaller positions.
## 7. Risk Management
### **7.1 Importance of Risk Management**
Effective risk management is crucial for long-term trading success. It involves protecting capital, managing losses, and optimizing trade setups.
### **7.2 Using USH for Risk Management**
**Stop-Loss and Take-Profit Levels:**
- **Stop-Loss Orders:** Use the histogram to set stop-loss levels based on trend strength. For instance, place stops below support levels in bullish trends and above resistance levels in bearish trends.
- **Take-Profit Targets:** Adjust take-profit levels based on histogram changes. For example, lock in profits as the histogram starts to shift from green to red.
**Position Sizing:**
- **Trend Strength:** Scale position sizes based on the strength of histogram signals. Larger histogram bars indicate stronger trends, which may justify larger positions.
- **Volatility:** Consider market volatility and adjust position sizes to mitigate risk.
## 8. Case Studies and Examples
### **8.1 Example 1: Bullish Trend**
**Scenario:** A trader notices a transition from red to green histogram bars.
**Analysis:**
- **Entry Point:** The transition indicates a potential bullish trend. The trader decides to enter a long position.
- **Stop-Loss:** Set stop-loss below recent support levels.
- **Take-Profit:** Consider taking profits as the histogram moves back towards zero or turns red.
**Outcome:** The bullish trend continues, and the histogram remains green, providing a profitable trade setup.
### **8.2 Example 2: Bearish Trend**
**Scenario:** A trader observes a transition from green to red histogram bars.
**Analysis:**
- **Entry Point:** The transition suggests a potential
bearish trend. The trader decides to enter a short position.
- **Stop-Loss:** Set stop-loss above recent resistance levels.
- **Take-Profit:** Consider taking profits as the histogram approaches zero or shifts to green.
**Outcome:** The bearish trend continues, and the histogram remains red, resulting in a successful trade.
## 9. Comparison with Other Indicators
### **9.1 RSI vs. USH**
**RSI:** Measures momentum and identifies overbought/oversold conditions.
**USH:** Builds on RSI by incorporating a moving average and histogram to provide a clearer view of trend strength and momentum.
### **9.2 RSI vs. MACD**
**MACD (Moving Average Convergence Divergence):** A trend-following momentum indicator that uses moving averages to identify changes in trend direction.
**Comparison:**
- **USH:** Provides a smoothed RSI perspective and visual histogram for trend strength.
- **MACD:** Offers signals based on the convergence and divergence of moving averages.
### **9.3 RSI vs. Stochastic Oscillator**
**Stochastic Oscillator:** Measures the level of the closing price relative to the high-low range over a specified period.
**Comparison:**
- **USH:** Focuses on smoothed RSI values and histogram representation.
- **Stochastic Oscillator:** Provides overbought/oversold signals and potential reversals based on price levels.
## 10. Advanced Usage and Tips
### **10.1 Combining Indicators**
**Multi-Indicator Strategies:** Combine the USH with other technical indicators (e.g., Moving Averages, Bollinger Bands) for a comprehensive trading strategy.
**Confirmation Signals:** Use the USH to confirm signals from other indicators. For instance, a bullish histogram combined with a moving average crossover may provide a stronger buy signal.
### **10.2 Customization Tips**
**Adjust RSI Length:** Experiment with different RSI lengths to match various market conditions and trading styles.
**Color Preferences:** Choose histogram colors that enhance visibility and align with personal preferences.
### **10.3 Continuous Learning**
**Backtesting:** Regularly backtest the USH with historical data to refine strategies and improve accuracy.
**Education:** Stay updated with trading education and adapt strategies based on market changes and personal experiences.
Smart Money Concepts by WeloTradesThe "Smart Money Concepts by WeloTrades" indicator is designed to offer traders a comprehensive tool that integrates multiple advanced features to aid in market analysis. By combining order blocks, liquidity levels, fair value gaps, trendlines, and market structure analysis, the indicator provides a holistic approach to understanding market dynamics and making informed trading decisions.
Components and Their Integration:
Order Blocks and Breaker Blocks Detection
Functionality: Order blocks represent areas where significant buying or selling occurred, creating potential support or resistance zones. Breaker blocks signal potential reversals.
Integration: By detecting and visualizing these blocks, the indicator helps traders identify key levels where price might react, aiding in entry and exit decisions. The customizable settings allow traders to adjust the visibility and parameters to suit their specific trading strategy.
Liquidity Levels Analysis
Functionality: Liquidity levels indicate zones where significant price movements can occur due to the presence of large orders. These are areas where smart money might be executing trades.
Integration: By tracking these high-probability liquidity areas, traders can anticipate potential price movements. Customizable display limits and mitigation strategies ensure that the information is tailored to the trader’s needs, providing precise and actionable insights.
Fair Value Gaps (FVG)
Functionality: Fair value gaps highlight areas where there is an imbalance between buyers and sellers. These gaps often represent potential trading opportunities.
Integration: The ability to identify and analyze FVGs helps traders spot potential entries based on market inefficiencies. The touch and break detection functionalities provide further refinement, enhancing the precision of trading signals.
Trendlines
Functionality: Trendlines help in identifying the direction of the market and potential reversal points. The additional trendline adds a layer of confirmation for breaks or retests.
Integration: Automatically drawn trendlines assist traders in visualizing market trends and making decisions about potential entries and exits. The additional trendline for stronger confirmation reduces the risk of false signals, providing more reliable trading opportunities.
Market Structure Analysis
Functionality: Understanding market structure is crucial for identifying key support and resistance levels and overall market dynamics. This component displays internal, external, and composite market structures.
Integration: By automatically highlighting shifts in market structure, the indicator helps traders recognize important levels and potential changes in market direction. This analysis is critical for strategic planning and execution in trading.
Customizable Alerts
Functionality: Alerts ensure that traders do not miss significant market events, such as the formation or breach of order blocks, liquidity levels, and trendline interactions.
Integration: Customizable alerts enhance the user experience by providing timely notifications of key events. This feature ensures that traders can act quickly and efficiently, leveraging the insights provided by the indicator.
Interactive Visualization
Functionality: Customizable visual aspects of the indicator allow traders to tailor the display to their preferences and trading style.
Integration: This feature enhances user engagement and usability, making it easier for traders to interpret the data and make informed decisions. Personalization options like colors, styles, and display formats improve the overall effectiveness of the indicator.
How Components Work Together
Comprehensive Market Analysis
Each component of the indicator addresses a different aspect of market analysis. Order blocks and liquidity levels highlight potential support and resistance zones, while fair value gaps and trendlines provide additional context for potential entries and exits. Market structure analysis ties everything together by offering a broad view of market dynamics.
Synergistic Insights
The integration of multiple features allows for cross-validation of trading signals. For instance, an order block coinciding with a high-probability liquidity level and a fair value gap can provide a stronger signal than any of these features alone. This synergy enhances the reliability of the insights and trading signals generated by the indicator.
Enhanced Decision Making
By combining these advanced features into a single tool, traders are equipped with a powerful resource for making informed decisions. The customizable alerts and interactive visualization further support this by ensuring that traders can act quickly on the insights provided.
Order Blocks ( OB) & Breaker Blocks (BB) Visuals:
📝 OB Input Settings
📊 Timeframe #1
TF #1🕑: Enable or disable Timeframe 1.
What it is: A boolean input to toggle the use of the first timeframe.
What it does: Enables or disables Timeframe 1 for the OB settings.
How to use it: Check or uncheck the box to enable or disable.
📊 Timeframe 1 Selection
Timeframe #1🕑: Select the timeframe for Timeframe 1.
What it is: A dropdown to select the desired timeframe.
What it does: Sets the timeframe for Timeframe 1.
How to use it: Choose a timeframe from the dropdown list.
📊 Timeframe #2
TF #2🕑: Enable or disable Timeframe 2.
What it is: A boolean input to toggle the use of the second timeframe.
What it does: Enables or disables Timeframe 2 for the OB settings.
How to use it: Check or uncheck the box to enable or disable.
📊 Timeframe 2 Selection
Timeframe #2🕑: Select the timeframe for Timeframe 2.
What it is: A dropdown to select the desired timeframe.
What it does: Sets the timeframe for Timeframe 2.
How to use it: Choose a timeframe from the dropdown list.
Additional Info: Higher TF Chart & Lower TF Setting / Lower TF Chart & Higher TF Setting.
📏 Show OBs
OB (Length)📏: Toggle the display of Order Blocks.
What it is: A boolean input to enable or disable the display of Order Blocks.
What it does: Shows or hides Order Blocks based on the selected swing length.
How to use it: Check or uncheck the box to enable or disable.
📏 Swing Length Option
Swing Length Option: Select the swing length option.
What it is: A dropdown to choose between SHORT, MID, LONG, or CUSTOM.
What it does: Sets the length of swings for Order Blocks.
How to use it: Choose an option from the dropdown.
Additional Info: Default lengths are SHORT=10, MID=28, LONG=50.
🔧 Custom Swing Length
🔧custom: Specify a custom swing length.
What it is: An integer input for setting a custom swing length.
What it does: Overrides the default swing lengths if set to CUSTOM.
How to use it: Enter a custom integer value (only shown when CUSTOM is selected).
📛 Show BBs
BB (Method)📛: Toggle the display of Breaker Blocks.
What it is: A boolean input to enable or disable the display of Breaker Blocks.
What it does: Shows or hides Breaker Blocks.
How to use it: Check or uncheck the box to enable or disable.
📛 OB End Method
OB End Method: Select the method for determining the end of a Breaker Block.
What it is: A dropdown to choose between Wick and Close.
What it does: Sets the criteria for when a Breaker Block is considered mitigated.
How to use it: Choose an option from the dropdown.
Additional Info: Wicks: OB is mitigated when the price wicks through the OB Level. Close: OB is mitigated when the closing price is within the OB Level.
🔍 Max Bullish Zones
🔍Max Bullish: Set the maximum number of Bullish Order Blocks to display.
What it is: A dropdown to select the maximum number of Bullish Order Blocks.
What it does: Limits the number of Bullish Order Blocks shown on the chart.
How to use it: Choose a value from the dropdown (1-10).
🔍 Max Bearish Zones
🔍Max Bearish: Set the maximum number of Bearish Order Blocks to display.
What it is: A dropdown to select the maximum number of Bearish Order Blocks.
What it does: Limits the number of Bearish Order Blocks shown on the chart.
How to use it: Choose a value from the dropdown (1-10).
🟩 Bullish OB Color
Bullish OB Color: Set the color for Bullish Order Blocks.
What it is: A color picker to set the color of Bullish Order Blocks.
What it does: Changes the color of Bullish Order Blocks on the chart.
How to use it: Select a color from the color picker.
🟥 Bearish OB Color
Bearish OB Color: Set the color for Bearish Order Blocks.
What it is: A color picker to set the color of Bearish Order Blocks.
What it does: Changes the color of Bearish Order Blocks on the chart.
How to use it: Select a color from the color picker.
🔧 OB & BB Range
↔ OB & BB Range: Select the range option for OB and BB.
What it is: A dropdown to choose between RANGE and CUSTOM.
What it does: Sets how far the OB or BB should extend.
How to use it: Choose an option from the dropdown.
Additional Info: RANGE = Current price, CUSTOM = Adjustable Range.
🔧 Custom OB & BB Range
🔧Custom: Specify a custom range for OB and BB.
What it is: An integer input for setting a custom range.
What it does: Defines how far the OB or BB should go, based on a custom value.
How to use it: Enter a custom integer value (range: 1000-500000).
💬 Text Options
💬Text Options: Set text size and color for OB and BB.
What it is: A dropdown to select text size and a color picker to choose text color.
What it does: Changes the size and color of the text displayed for OB and BB.
How to use it: Select a size from the dropdown and a color from the color picker.
💬 Show Timeframe OB
Text: Toggle to display the timeframe of OB.
What it is: A boolean input to show or hide the timeframe text for OB.
What it does: Displays the timeframe information for Order Blocks on the chart.
How to use it: Check or uncheck the box to enable or disable.
💬 Show Volume
Volume: Toggle to display the volume of OB.
What it is: A boolean input to show or hide the volume information for Order Blocks.
What it does: Displays the volume information for Order Blocks on the chart.
How to use it: Check or uncheck the box to enable or disable.
Additional Info:
What it represents: The volume displayed represents the total trading volume that occurred during the formation of the Order Block. This can indicate the level of participation or interest in that price level.
How it's calculated: The volume is the sum of all traded volumes within the candles that form the Order Block.
What it means: Higher volume at an Order Block level may suggest stronger support or resistance. It shows the amount of trading activity and can be an indicator of the potential strength or validity of the Order Block.
Why it's shown: To give traders an idea of the market participation and to help assess the strength of the Order Block.
💬 Show Percentage
%: Toggle to display the percentage of OB.
What it is: A boolean input to show or hide the percentage information for Order Blocks.
What it does: Displays the percentage information for Order Blocks on the chart.
How to use it: Check or uncheck the box to enable or disable.
Additional Info:
What it represents: The percentage displayed usually represents the proportion of price movement relative to the Order Block.
How it's calculated: This can be the percentage move from the start to the end of the Order Block or the retracement level that price has reached relative to the Order Block's range.
What it means: It helps traders understand the extent of price movement within the Order Block and can indicate the significance of the price level.
Why it's shown: To provide a clearer understanding of the price dynamics and the importance of the Order Block within the overall price movement.
Additional Information
Volume Example: If an Order Block forms over three candles with volumes of 100, 150, and 200, the total volume displayed for that Order Block would be 450.
Percentage Example: If the price moves from 100 to 110 within an Order Block, and the total range of the Order Block is from 100 to 120, the percentage shown might be 50% (since the price has moved halfway through the Order Block's range).
Liquidity Levels visuals:
📊 Liquidity Levels Input Settings
📊 Current Timeframe
TF #1🕑: Enable or disable the current timeframe.
What it is: A boolean input to toggle the use of the current timeframe.
What it does: Enables or disables the display of liquidity levels for the current timeframe.
How to use it: Check or uncheck the box to enable or disable.
📊 Higher Timeframe
Higher Timeframe: Select the higher timeframe for liquidity levels.
What it is: A dropdown to select the desired higher timeframe.
What it does: Sets the higher timeframe for liquidity levels.
How to use it: Choose a timeframe from the dropdown list.
📏 Liquidity Length Option
📏Liquidity Length: Select the length for liquidity levels.
What it is: A dropdown to choose between SHORT, MID, LONG, or CUSTOM.
What it does: Sets the length of swings for liquidity levels.
How to use it: Choose an option from the dropdown.
Additional Info: Default lengths are SHORT=10, MID=28, LONG=50.
🔧 Custom Liquidity Length
🔧custom: Specify a custom length for liquidity levels.
What it is: An integer input for setting a custom swing length.
What it does: Overrides the default liquidity lengths if set to CUSTOM.
How to use it: Enter a custom integer value (only shown when CUSTOM is selected).
📛 Mitigation Method
📛Mitigation (Method): Select the method for determining the mitigation of liquidity levels.
What it is: A dropdown to choose between Close and Wick.
What it does: Sets the criteria for when a liquidity level is considered mitigated.
How to use it: Choose an option from the dropdown.
Additional Info:
Wick: Level is mitigated when the price wicks through the level.
Close: Level is mitigated when the closing price is within the level.
📛 Display Mitigated Levels
-: Select to display or hide mitigated levels.
What it is: A dropdown to choose between Remove and Show.
What it does: Displays or hides mitigated liquidity levels.
How to use it: Choose an option from the dropdown.
Additional Info:
Remove: Hide mitigated levels.
Show: Display mitigated levels.
🔍 Max Buy Side Liquidity
🔍Max Buy Side Liquidity: Set the maximum number of Buy Side Liquidity Levels to display.
What it is: An integer input to set the maximum number of Buy Side Liquidity Levels.
What it does: Limits the number of Buy Side Liquidity Levels shown on the chart.
How to use it: Enter a value between 0 and 50.
🟦 Buy Side Liquidity Color
Buy Side Liquidity Color: Set the color for Buy Side Liquidity Levels.
What it is: A color picker to set the color of Buy Side Liquidity Levels.
What it does: Changes the color of Buy Side Liquidity Levels on the chart.
How to use it: Select a color from the color picker.
Additional Info:
Tooltip: Set the maximum number of Buy Side Liquidity Levels to display. Default: 5, Min: 1, Max: 50.
If liquidity levels are not displayed as expected, try increasing the max count.
🔍 Max Sell Side Liquidity
🔍Max Sell Side Liquidity: Set the maximum number of Sell Side Liquidity Levels to display.
What it is: An integer input to set the maximum number of Sell Side Liquidity Levels.
What it does: Limits the number of Sell Side Liquidity Levels shown on the chart.
How to use it: Enter a value between 0 and 50.
🟥 Sell Side Liquidity Color
Sell Side Liquidity Color: Set the color for Sell Side Liquidity Levels.
What it is: A color picker to set the color of Sell Side Liquidity Levels.
What it does: Changes the color of Sell Side Liquidity Levels on the chart.
How to use it: Select a color from the color picker.
Additional Info:
Tooltip: Set the maximum number of Sell Side Liquidity Levels to display. Default: 5, Min: 1, Max: 50.
If liquidity levels are not displayed as expected, try increasing the max count.
✂ Box Style (Height)
✂ Box Style (↕): Set the box height style for liquidity levels.
What it is: A float input to set the height of the boxes.
What it does: Adjusts the height of the boxes displaying liquidity levels.
How to use it: Enter a value between -50 and 50.
Additional Info: Default value is -5.
📏 Box Length
b: Set the box length of liquidity levels.
What it is: An integer input to set the length of the boxes.
What it does: Adjusts the length of the boxes displaying liquidity levels.
How to use it: Enter a value between 0 and 500.
Additional Info: Default value is 20.
⏭ Extend Liquidity Levels
Extend ⏭: Toggle to extend liquidity levels beyond the current range.
What it is: A boolean input to enable or disable the extension of liquidity levels.
What it does: Extends liquidity levels beyond their default range.
How to use it: Check or uncheck the box to enable or disable.
Additional Info: Extend liquidity levels beyond the current range.
💬 Text Options
💬 Text Options: Set text size and color for liquidity levels.
What it is: A dropdown to select text size and a color picker to choose text color.
What it does: Changes the size and color of the text displayed for liquidity levels.
How to use it: Select a size from the dropdown and a color from the color picker.
💬 Show Text
Text: Toggle to display text for liquidity levels.
What it is: A boolean input to show or hide the text for liquidity levels.
What it does: Displays the text information for liquidity levels on the chart.
How to use it: Check or uncheck the box to enable or disable.
💬 Show Volume
Volume: Toggle to display the volume of liquidity levels.
What it is: A boolean input to show or hide the volume information for liquidity levels.
What it does: Displays the volume information for liquidity levels on the chart.
How to use it: Check or uncheck the box to enable or disable.
Additional Info:
What it represents: The volume displayed represents the total trading volume that occurred during the formation of the liquidity level. This can indicate the level of participation or interest in that price level.
How it's calculated: The volume is the sum of all traded volumes within the candles that form the liquidity level.
What it means: Higher volume at a liquidity level may suggest stronger support or resistance. It shows the amount of trading activity and can be an indicator of the potential strength or validity of the liquidity level.
Why it's shown: To give traders an idea of the market participation and to help assess the strength of the liquidity level.
💬 Show Percentage
%: Toggle to display the percentage of liquidity levels.
What it is: A boolean input to show or hide the percentage information for liquidity levels.
What it does: Displays the percentage information for liquidity levels on the chart.
How to use it: Check or uncheck the box to enable or disable.
Additional Info:
What it represents: The percentage displayed usually represents the proportion of price movement relative to the liquidity level.
How it's calculated: This can be the percentage move from the start to the end of the liquidity level or the retracement level that price has reached relative to the liquidity level's range.
What it means: It helps traders understand the extent of price movement within the liquidity level and can indicate the significance of the price level.
Why it's shown: To provide a clearer understanding of the price dynamics and the importance of the liquidity level within the overall price movement.
Fair Value Gaps visuals:
📊 Fair Value Gaps Input Settings
📊 Show FVG
TF #1🕑: Enable or disable Fair Value Gaps for Timeframe 1.
What it is: A boolean input to toggle the display of Fair Value Gaps.
What it does: Shows or hides Fair Value Gaps on the chart.
How to use it: Check or uncheck the box to enable or disable.
📊 Select Timeframe
Timeframe: Select the timeframe for Fair Value Gaps.
What it is: A dropdown to select the desired timeframe.
What it does: Sets the timeframe for Fair Value Gaps.
How to use it: Choose a timeframe from the dropdown list.
Additional Info: Higher TF Chart & Lower TF Setting or Lower TF Chart & Higher TF Setting.
📛 FVG Break Method
📛FVG Break (Method): Select the method for determining when an FVG is mitigated.
What it is: A dropdown to choose between Touch, Wicks, Close, or Average.
What it does: Sets the criteria for when a Fair Value Gap is considered mitigated.
How to use it: Choose an option from the dropdown.
Additional Info:
Touch: FVG is mitigated when the price touches the gap.
Wicks: FVG is mitigated when the price wicks through the gap.
Close: FVG is mitigated when the closing price is within the gap.
Average: FVG is mitigated when the average price (average of high and low) is within the gap.
📛 Show Mitigated FVG
show: Toggle to display mitigated FVGs.
What it is: A boolean input to show or hide mitigated Fair Value Gaps.
What it does: Displays or hides mitigated Fair Value Gaps.
How to use it: Check or uncheck the box to enable or disable.
📛 Fill FVG
Fill: Toggle to fill Fair Value Gaps.
What it is: A boolean input to fill the Fair Value Gaps with color.
What it does: Adds a color fill to the Fair Value Gaps.
How to use it: Check or uncheck the box to enable or disable.
📛 Shade FVG
Shade: Toggle to shade Fair Value Gaps.
What it is: A boolean input to shade the Fair Value Gaps.
What it does: Adds a shade effect to the Fair Value Gaps.
How to use it: Check or uncheck the box to enable or disable.
Additional Info: Select the method to break FVGs and toggle the visibility of FVG Breaks (fill FVG and/or shade FVG).
🔍 Max Bullish FVG
🔍Max Bullish FVG: Set the maximum number of Bullish Fair Value Gaps to display.
What it is: An integer input to set the maximum number of Bullish Fair Value Gaps.
What it does: Limits the number of Bullish Fair Value Gaps shown on the chart.
How to use it: Enter a value between 0 and 50.
🔍 Max Bearish FVG
🔍Max Bearish FVG: Set the maximum number of Bearish Fair Value Gaps to display.
What it is: An integer input to set the maximum number of Bearish Fair Value Gaps.
What it does: Limits the number of Bearish Fair Value Gaps shown on the chart.
How to use it: Enter a value between 0 and 50.
🟥 Bearish FVG Color
Bearish FVG Color: Set the color for Bearish Fair Value Gaps.
What it is: A color picker to set the color of Bearish Fair Value Gaps.
What it does: Changes the color of Bearish Fair Value Gaps on the chart.
How to use it: Select a color from the color picker.
Additional Info:
Tooltip: Set the maximum number of Bearish Fair Value Gaps to display. Default: 5, Min: 1, Max: 50.
If Fair Value Gaps are not displayed as expected, try increasing the max count.
🟦 Bullish FVG Color
Bullish FVG Color: Set the color for Bullish Fair Value Gaps.
What it is: A color picker to set the color of Bullish Fair Value Gaps.
What it does: Changes the color of Bullish Fair Value Gaps on the chart.
How to use it: Select a color from the color picker.
Additional Info:
Tooltip: Set the maximum number of Bullish Fair Value Gaps to display. Default: 5, Min: 1, Max: 50.
If Fair Value Gaps are not displayed as expected, try increasing the max count.
📏 FVG Range
↔ FVG Range: Set the range for Fair Value Gaps.
What it is: An integer input to set the range of the Fair Value Gaps.
What it does: Adjusts the range of the Fair Value Gaps displayed.
How to use it: Enter a value between 0 and 100.
Additional Info: Adjustable length only works when both RANGE & EXTEND display OFF. Range=current price, Extend=Full Range.
⏭ Extend FVG
Extend⏭: Toggle to extend Fair Value Gaps beyond the current range.
What it is: A boolean input to enable or disable the extension of Fair Value Gaps.
What it does: Extends Fair Value Gaps beyond their default range.
How to use it: Check or uncheck the box to enable or disable.
⏯ FVG Range
Range⏯: Toggle the range of Fair Value Gaps.
What it is: A boolean input to enable or disable the range display for Fair Value Gaps.
What it does: Sets the range of Fair Value Gaps displayed.
How to use it: Check or uncheck the box to enable or disable.
↕ Max Width
↕ Max Width: Set the maximum width of Fair Value Gaps.
What it is: A float input to set the maximum width of Fair Value Gaps.
What it does: Limits the width of Fair Value Gaps as a percentage of the price range.
How to use it: Enter a value between 0 and 5.0.
Additional Info: FVGs wider than this value will be ignored.
♻ Filter FVG
Filter FVG ♻: Toggle to filter out small Fair Value Gaps.
What it is: A boolean input to filter out small Fair Value Gaps.
What it does: Ignores Fair Value Gaps smaller than the specified max width.
How to use it: Check or uncheck the box to enable or disable.
➖ Mid Line Style
➖Mid Line Style: Select the style of the mid line for Fair Value Gaps.
What it is: A dropdown to choose between Solid, Dashed, or Dotted.
What it does: Sets the style of the mid line within Fair Value Gaps.
How to use it: Choose an option from the dropdown.
🎨 Mid Line Color
Mid Line Color: Set the color for the mid line within Fair Value Gaps.
What it is: A color picker to set the color of the mid line.
What it does: Changes the color of the mid line within Fair Value Gaps.
How to use it: Select a color from the color picker.
Additional Information
Mitigation Methods: Each method (Touch, Wicks, Close, Average) provides different criteria for when a Fair Value Gap is considered mitigated, helping traders to understand the dynamics of price movements within gaps.
Volume and Percentage: Displaying volume and percentage information for Fair Value Gaps helps traders gauge the strength and significance of these gaps in relation to trading activity and price movements.
Trendlines visuals:
📊 Trendlines Input Settings
📊 Show Trendlines
Trendlines & Trendlines Difference(%) ↕: Enable or disable trendlines and set the percentage difference from the first trendline.
What it is: A boolean input to toggle the display of trendlines.
What it does: Shows or hides trendlines on the chart and allows setting a percentage difference from the first trendline.
How to use it: Check or uncheck the box to enable or disable.
Additional Info: The percentage difference determines the distance of the second trendline from the first one.
📏 Trendline Length Option
📏Trendline Length: Select the length for trendlines.
What it is: A dropdown to choose between SHORT, MID, LONG, or CUSTOM.
What it does: Sets the length of trendlines.
How to use it: Choose an option from the dropdown.
Additional Info: Default lengths are SHORT=50, MID=100, LONG=200.
🔧 Custom Trendline Length
🔧custom: Specify a custom length for trendlines.
What it is: An integer input for setting a custom trendline length.
What it does: Overrides the default trendline lengths if set to CUSTOM.
How to use it: Enter a custom integer value (only shown when CUSTOM is selected).
🔍 Max Bearish Trendlines
🔍Max Trendlines Bearish: Set the maximum number of bearish trendlines to display.
What it is: A dropdown to select the maximum number of bearish trendlines.
What it does: Limits the number of bearish trendlines shown on the chart.
How to use it: Choose a value from the dropdown (2-20).
🟩 Bearish Trendline Color
Bearish Trendline Color: Set the color for bearish trendlines.
What it is: A color picker to set the color of bearish trendlines.
What it does: Changes the color of bearish trendlines on the chart.
How to use it: Select a color from the color picker.
Additional Info: Adjust to control how many bearish trendlines are displayed.
🔍 Max Bullish Trendlines
🔍Max Trendlines Bullish: Set the maximum number of bullish trendlines to display.
What it is: A dropdown to select the maximum number of bullish trendlines.
What it does: Limits the number of bullish trendlines shown on the chart.
How to use it: Choose a value from the dropdown (2-20).
🟥 Bullish Trendline Color
Bullish Trendline Color: Set the color for bullish trendlines.
What it is: A color picker to set the color of bullish trendlines.
What it does: Changes the color of bullish trendlines on the chart.
How to use it: Select a color from the color picker.
Additional Info: Adjust to control how many bullish trendlines are displayed.
📐 Degrees Text
📐Degrees ° (💬 Size): Enable or disable degrees text and set its size and color.
What it is: A boolean input to show or hide the degrees text for trendlines.
What it does: Displays the degrees text for trendlines.
How to use it: Check or uncheck the box to enable or disable.
📏 Text Size for Degrees
Text Size: Set the text size for degrees on trendlines.
What it is: A dropdown to select the size of the degrees text.
What it does: Changes the size of the degrees text displayed for trendlines.
How to use it: Choose a size from the dropdown (XS, S, M, L, XL).
🎨 Degrees Text Color
Degrees Text Color: Set the color for the degrees text on trendlines.
What it is: A color picker to set the color of the degrees text.
What it does: Changes the color of the degrees text on the chart.
How to use it: Select a color from the color picker.
♻ Filter Degrees
♻ Filter Degrees °: Enable or disable angle filtering and set the angle range.
What it is: A boolean input to filter trendlines by their angle.
What it does: Shows only trendlines within a specified angle range.
How to use it: Check or uncheck the box to enable or disable.
Additional Info: Angles outside this range will be filtered out.
🔢 Angle Range
Angle Range: Set the angle range for filtering trendlines.
What it is: Two float inputs to set the minimum and maximum angle for trendlines.
What it does: Defines the range of angles for which trendlines will be shown.
How to use it: Enter values for the minimum and maximum angles.
➖ Line Style
➖Style #1 & #2: Select the style of the primary and secondary trendlines.
What it is: Two dropdowns to choose between Solid, Dashed, or Dotted for the trendlines.
What it does: Sets the style of the primary and secondary trendlines.
How to use it: Choose a style from each dropdown.
📏 Line Thickness
: Set the thickness for the trendlines.
What it is: An integer input to set the thickness of the trendlines.
What it does: Adjusts the thickness of the trendlines displayed on the chart.
How to use it: Enter a value between 1 and 5.
Additional Information
Trendline Percentage Difference: Setting a percentage difference helps in analyzing the relative position and angle of trendlines.
Filtering by Angle: This feature allows focusing on trendlines within a specific angle range, enhancing the clarity of trend analysis.
BOS & CHOCH Market Structure visuals:
📊 BOS & CHOCH Market Structure Input Settings
📏 Market Structure Length Option
📏Market Structure: Select the market structure length option.
What it is: A dropdown to choose between INTERNAL, EXTERNAL, ALL, CUSTOM, or NONE.
What it does: Sets the type of market structure to be displayed.
How to use it: Choose an option from the dropdown.
Additional Info:
INTERNAL: Only internal structure.
EXTERNAL: Only external structure.
ALL: Both internal and external structures.
CUSTOM: Custom lengths.
NONE: No structure.
🔧 Custom Internal Length
🔧Custom Internal: Specify a custom length for internal market structure.
What it is: An integer input for setting a custom internal length.
What it does: Defines the length of internal market structures if CUSTOM is selected.
How to use it: Enter a custom integer value (only shown when CUSTOM is selected).
💬 Internal Label Size
💬Internal Label Size: Set the label size for internal market structures.
What it is: A dropdown to select the size of the labels.
What it does: Changes the size of the labels for internal market structures.
How to use it: Choose a size from the dropdown (XS, S, M, L, XL).
🟩 Internal Bullish Color
Internal Bullish Color: Set the color for bullish internal market structures.
What it is: A color picker to set the color of bullish internal market structures.
What it does: Changes the color of bullish internal market structures on the chart.
How to use it: Select a color from the color picker.
🟥 Internal Bearish Color
Internal Bearish Color: Set the color for bearish internal market structures.
What it is: A color picker to set the color of bearish internal market structures.
What it does: Changes the color of bearish internal market structures on the chart.
How to use it: Select a color from the color picker.
🔧 Custom External Length
🔧Custom External: Specify a custom length for external market structure.
What it is: An integer input for setting a custom external length.
What it does: Defines the length of external market structures if CUSTOM is selected.
How to use it: Enter a custom integer value (only shown when CUSTOM is selected).
💬 External Label Size
💬External Label Size: Set the label size for external market structures.
What it is: A dropdown to select the size of the labels.
What it does: Changes the size of the labels for external market structures.
How to use it: Choose a size from the dropdown (XS, S, M, L, XL).
🟩 External Bullish Color
External Bullish Color: Set the color for bullish external market structures.
What it is: A color picker to set the color of bullish external market structures.
What it does: Changes the color of bullish external market structures on the chart.
How to use it: Select a color from the color picker.
🟥 External Bearish Color
External Bearish Color: Set the color for bearish external market structures.
What it is: A color picker to set the color of bearish external market structures.
What it does: Changes the color of bearish external market structures on the chart.
How to use it: Select a color from the color picker.
📐 Show Equal Highs and Lows
EQL & EQH📐: Toggle visibility for equal highs and lows.
What it is: A boolean input to show or hide equal highs and lows.
What it does: Displays or hides equal highs and lows on the chart.
How to use it: Check or uncheck the box to enable or disable.
📏 Equal Highs and Lows Threshold
Equal Highs and Lows Threshold: Set the threshold for equal highs and lows.
What it is: A float input to set the threshold for equal highs and lows.
What it does: Defines the range within which highs and lows are considered equal.
How to use it: Enter a value between 0 and 10.
💬 Label Size for Equal Highs and Lows
💬Label Size for Equal Highs and Lows: Set the label size for equal highs and lows.
What it is: A dropdown to select the size of the labels.
What it does: Changes the size of the labels for equal highs and lows.
How to use it: Choose a size from the dropdown (XS, S, M, L, XL).
🟩 Bullish Color for Equal Highs and Lows
Bullish Color for Equal Highs and Lows: Set the color for bullish equal highs and lows.
What it is: A color picker to set the color of bullish equal highs and lows.
What it does: Changes the color of bullish equal highs and lows on the chart.
How to use it: Select a color from the color picker.
🟥 Bearish Color for Equal Highs and Lows
Bearish Color for Equal Highs and Lows: Set the color for bearish equal highs and lows.
What it is: A color picker to set the color of bearish equal highs and lows.
What it does: Changes the color of bearish equal highs and lows on the chart.
How to use it: Select a color from the color picker.
📏 Show Swing Points
Swing Points📏: Toggle visibility for swing points.
What it is: A boolean input to show or hide swing points.
What it does: Displays or hides swing points on the chart.
How to use it: Check or uncheck the box to enable or disable.
📏 Swing Points Length Option
Swing Points Length Option: Select the length for swing points.
What it is: A dropdown to choose between SHORT, MID, LONG, or CUSTOM.
What it does: Sets the length of swing points.
How to use it: Choose an option from the dropdown.
Additional Info: Default lengths are SHORT=10, MID=28, LONG=50.
💬 Swing Points Label Size
💬Swing Points Label Size: Set the label size for swing points.
What it is: A dropdown to select the size of the labels.
What it does: Changes the size of the labels for swing points.
How to use it: Choose a size from the dropdown (XS, S, M, L, XL).
🎨 Swing Points Color
Swing Points Color: Set the color for swing points.
What it is: A color picker to set the color of swing points.
What it does: Changes the color of swing points on the chart.
How to use it: Select a color from the color picker.
🔧 Custom Swing Points Length
🔧Custom Swings: Specify a custom length for swing points.
What it is: An integer input for setting a custom length for swing points.
What it does: Defines the length of swing points if CUSTOM is selected.
How to use it: Enter a custom integer value (only shown when CUSTOM is selected).
Additional Information
Market Structure Types: Understanding internal and external structures helps in analyzing different market behaviors.
Equal Highs and Lows: This feature identifies areas where price action is balanced, which can be significant for trading strategies.
Swing Points: Highlighting swing points aids in recognizing significant market reversals or continuations.
Benefits
Enhance your trading strategy by visualizing smart money's influence on price movements.
Make informed decisions with real-time data on significant market structures.
Reduce manual analysis with automated detection of key trading signals.
Ideal For
Traders looking for an edge in forex, equities, and cryptocurrency markets by understanding the underlying forces driving market dynamics.
Acknowledgements
Special thanks to these amazing creators for inspiration and their creations:
I want to thank these amazing creators for creating there amazing indicators , that inspired me and also gave me a head start by making this indicator! Without their amazing indicators it wouldn't be possible!
Flux Charts: Volumized Order Blocks
LuxAlgo: Trend Lines
UAlgo: Fair Value Gaps (FVG)
By Leviathan: Market Structure
Sonarlab: Liquidity Levels
Note
Remember to always backtest the indicator first before integrating it into your strategy! For any questions about the indicator, please feel free to ask for assistance.
[MAD] Custom Session VWAP BandsOverview
This indicator helps visualize the Volume Weighted Average Price (VWAP) and its associated standard deviation bands over specified time periods, providing traders with a clear understanding of price trends, volatility, and potential support/resistance levels.
Inputs
Deviation
StDev mult 1: Multiplier for the first standard deviation band (Default: 1.0)
StDev mult 2: Multiplier for the second standard deviation band (Default: 2.0)
StDev mult 3: Multiplier for the third standard deviation band (Default: 3.0)
StDev mult 4: Multiplier for the fourth standard deviation band (Default: 4.0)
Line width: Width of the lines for the bands (Default: 2)
Custom Vwap session reset settings
Many different options are considered when a session is going to be reset.
Plot and Fill Options
Enable Fills: Enable/disable filling between bands.
Plot +4: Enable/disable plotting the +4 standard deviation band.
Plot +3: Enable/disable plotting the +3 standard deviation band.
Plot +2: Enable/disable plotting the +2 standard deviation band.
Plot +1: Enable/disable plotting the +1 standard deviation band.
Plot VWAP: Enable/disable plotting the VWAP line.
Plot -1: Enable/disable plotting the -1 standard deviation band.
Plot -2: Enable/disable plotting the -2 standard deviation band.
Plot -3: Enable/disable plotting the -3 standard deviation band.
Plot -4: Enable/disable plotting the -4 standard deviation band.
How to Use the Indicator
Adding the Indicator
Add the indicator to your chart through your trading platform's indicator menu.
Configuring the VWAP Reset
Specify reset intervals based on time, days of the week, or specific dates.
Adjust the time zone if necessary.
Customizing Standard Deviation Bands
Set the multipliers for the standard deviation bands.
Choose line width for better visualization.
Enabling Plots and Fills
Select which bands to display.
Enable or disable fills between the bands.
Practical Application of VWAP Bands
Understanding VWAP
VWAP is a trading benchmark that calculates the average price a security has traded at throughout the day based on volume and price. It is primarily used for intraday trading but can also offer insights during end-of-day reviews.
Using VWAP for Trading
Intraday Trading
Entry and Exit Points: VWAP can help identify optimal buy and sell points. Buy when the price is above VWAP and sell when it's below.
Support and Resistance: VWAP often acts as a dynamic support/resistance level. Prices tend to revert to VWAP, making it a crucial level for intraday traders.
Trend Confirmation
Uptrends and Downtrends: In an uptrend, the price will generally stay above VWAP. Conversely, in a downtrend, it will stay below. Use this to confirm market direction.
Combining with Other Indicators
Moving Averages and Bollinger Bands: Combining VWAP with these indicators can provide a more robust trading signal, confirming trends and potential reversals.
Setting Stop-Loss and Profit Targets
Conservative Stop Orders: Place stop orders at recent lows for pullback trades.
Profit Targets: Use daily highs or Fibonacci extension levels to set profit targets.
Strategies for Using VWAP
Pullback Strategy
Buy during pullbacks to VWAP in an uptrend, and sell during rallies to VWAP in a downtrend.
Breakout Strategy
Look for breakouts above/below VWAP after the market open to capitalize on new trends.
Momentum Trading
Use VWAP to confirm the strength of a trend. Buy when the price is consistently above VWAP and sell when it's consistently below.
Institutional Strategies
Institutional traders use VWAP to execute large orders without causing significant market impact, ensuring trades are made around the average price.
By incorporating these strategies, traders can better understand market dynamics, make informed trading decisions, and manage their risk effectively.
Some setup possibilities
Institutional Supply and Demand ZonesThis indicator aims to identify price levels where institutional investors have positioned their buy or sell orders. These buy orders establish "demand zones," while sell orders create "supply zones." Identifying these zones enables us to anticipate potential reversals in price trends, allowing us to profitably engage in these significant market movements alongside major institutions. These zones are formed when price action goes from balanced to imbalanced. These zones are based on orders. Unlike standard support and resistance levels, when price breaks below a demand zone or above a supply zone, these zones disappear from the chart.
Supply is formed by a green candle followed by a major red candle that is at least double the size of previous green candle. The zone is then charted from the open of the green candle to the highest point in the candle. Vice versa for a demand zone (red into green).
These zones are traded by:
1. Look for a volume spike in a zone
2. A trend/trendline break out of the zone
Backtesting ModuleDo you often find yourself creating new 'strategy()' scripts for each trading system? Are you unable to focus on generating new systems due to fatigue and time loss incurred in the process? Here's a potential solution: the 'Backtesting Module' :)
INTRODUCTION
Every trading system is based on four basic conditions: long entry, long exit, short entry and short exit (which are typically defined as boolean series in Pine Script).
If you can define the conditions generated by your trading system as a series of integers, it becomes possible to use these variables in different scripts in efficient ways. (Pine Script is a convenient language that allows you to use the integer output of one indicator as a source in another.)
The 'Backtesting Module' is a dynamic strategy script designed to adapt to your signals. It boasts two notable features:
⮞ It produces a backtest report using the entry and exit variables you define.
⮞ It not only serves for system testing but also to combine independent signals into a single system. (This functionality enables to create complex strategies and report on their success!)
The module tests Golden and Death cross signals by default, when you enter your own conditions the default signals will be neutralized. The methodology is described below.
PREPARATION
There are three simple steps to connect your own indicator to the Module.
STEP 1
Firstly, you must define entry and exit variables in your own script. Let's elucidate it with a straightforward example. Consider a system generating long and short signals based on the intersections of two moving averages. Consequently, our conditions would be as follows:
// Signals
long = ta.crossover(ta.sma(close, 14), ta.sma(close, 28))
short = ta.crossunder(ta.sma(close, 14), ta.sma(close, 28))
Now, the question is: How can we convert boolean variables into integer variables? The answer is conditional ternary block, defined as follows:
// Entry & Exit
long_entry = long ? 1 : 0
long_exit = short ? 1 : 0
short_entry = short ? 1 : 0
short_exit = long ? 1 : 0
The mechanics of the Entry & Exit variables are simple. The variable takes on a value of 1 when your trading system generates the signal and if your system does not produce any signal, variable returns 0. In this example, you see how exit signals can be generated in a trading system that only contains entry signals. If you have a system with original exit signals, you can also use them directly. (Please mind the NOTES section below).
STEP 2
To utilize the Entry & Exit variables as source in another script, they must be plotted on the chart. Therefore, the final detail to include in the script containing your trading system would be as follows:
// Plot The Output
plot(long_entry, "Long Entry", display=display.data_window, editable=false)
plot(long_exit, "Long Exit", display=display.data_window, editable=false)
plot(short_entry, "Short Entry", display=display.data_window, editable=false)
plot(short_exit, "Short Exit", display=display.data_window, editable=false)
STEP 3
Now, we are ready to test the system! Load the Backtesting Module indicator onto the chart along with your trading system/indicator. Then set the outputs of your system (Long Entry, Long Exit, Short Entry, Short Exit) as source in the module. That's it.
FEATURES & ORIGINALITY
⮞ Primarily, this script has been created to provide you with an easy and practical method when testing your trading system.
⮞ I thought it might be nice to visualize a few useful results. The Backtesting Module provides insights into the outcomes of both long and short trades by computing the number of trades and the success percentage.
⮞ Through the 'Trade' parameter, users can specify the market direction in which the indicator is permitted to initiate positions.
⮞ Users have the flexibility to define the date range for the test.
⮞ There are optional features allowing users to plot entry prices on the chart and customize bar colors.
⮞ The report and the test date range are presented in a table on the chart screen. The entry price can be monitored in the data window.
⮞ Note that results are based on realized returns, and the open trade is not included in the displayed results. (The only exception is the 'Unrealized PNL' result in the table.)
STRATEGY SETTINGS
The default parameters are as follows:
⮞ Initial Balance : 10000 (in units of currency)
⮞ Quantity : 10% of equity
⮞ Commission : 0.04%
⮞ Slippage : 0
⮞ Dataset : All bars in the chart
For a realistic backtest result, you should size trades to only risk sustainable amounts of equity. Do not risk more than 5-10% on a trade. And ALWAYS configure your commission and slippage parameters according to pessimistic scenarios!
NOTES
⮞ This script is intended solely for development purposes. And it'll will be available for all the indicators I publish.
⮞ In this version of the module, all order types are designed as market orders. The exit size is the sum of the entry size.
⮞ As your trading conditions grow more intricate, you might need to define the outputs of your system in alternative ways. The method outlined in this description is tailored for straightforward signal structures.
⮞ Additionally, depending on the structure of your trading system, the backtest module may require further development. This encompasses stop-loss, take-profit, specific exit orders, quantity, margin and risk management calculations. I am considering releasing improvements that consider these options in future versions.
⮞ An example of how complex trading signals can be generated is the OTT Collection. If you're interested in seeing how the signals are constructed, you can use the link below.
THANKS
Special thanks to PineCoders for their valuable moderation efforts.
I hope this will be a useful example for the TradingView community...
DISCLAIMER
This is just an indicator, nothing more. It is provided for informational and educational purposes exclusively. The utilization of this script does not constitute professional or financial advice. The user solely bears the responsibility for risks associated with script usage. Do not forget to manage your risk. And trade as safely as possible. Best of luck!
Tape (Time and Sales)OVERVIEW
This indicator is a synthesized "Tape" (aka. Time and Sales) from real time market data. It's specifically designed to be performant, expediting trading insights and decisions.
The table contains color-coded price action, volume size, and a timestamp data for each chart update. Because chart updates are independent of exchange orders, 1 chart update may combine more than 1 exchange and/or order. Even so, you're able to see very small and fast order flow changes, made possible by measuring real time volume differentials, and correlating them with price action.
Real time volume differentials are required for this indicator to be most useful. This is not ideal for historical analysis or TradingViews Replay feature.
INPUTS
You can can configure:
Table Position and Text Size
The Timestamp (visibility, format, timezone)
The number of lines to print
Volume Parameters (minimum size, large sizes, decimal precision)
Highlighting and Enlarging large sized prints
All the colors
DEV NOTES
This script illustrates:
The complimentary nature of loops and arrays
A method for iterative table management
Markdown: The Pine Editor's Hidden Gem💬 Markdown, a markup language
Markdown is a portable, lightweight markup language that can be used for everything whether you're building a website, documentation, or even presentations.
Platforms like Discord, Reddit, and GitHub support Markdown and is the widely go-to option for text formatting due to its simplicity. Pine Script is a language that also utilizes Markdown, specifically in the Pine Editor where it can really be used to some extent.
Since the release of libraries, user-defined types, and methods, Pine Script is entering an age where developers will be highly dependent on libraries due to the capabilities Pine has inherited recently. It would be no surprise if a few people got together and took their time to thoroughly develop an entire project/library centered around improving Pine Script's built-in functions and providing developers with easier ways of achieving things than they thought they could.
As you're all aware, hovering over functions (and more) in the editor pops up a prompt that specifies the parameters, types, and what the function returns. Pine Script uses Markdown for that, so I figured we could go ahead and push that feature to its limits and see what we can do.
Today we'll go over how we can utilize Markdown in Pine Script, and how you can make your library's built-in functions stand out more than they did previously.
For more information, visit www.markdownguide.org
📕 General Notes
Markdown syntax only works on functions and methods.
Using arrays as parameters as of 2/21/2023 breaks the Markdown system.
The prompt window holds a max of 166 characters on one line before overflowing.
There is no limit on how long the prompt window can be.
🔽 Getting Started 🔽
▶️ Headings
If you have experience in HTML, Markdown, or even Microsoft Word then you already have a grasp of how headings work and look.
To simplify it, headings make the given text either massive or tiny depending on how many number symbols are provided.
When defining headings, you must have a space between the number (#) symbol, and the text. This is typical syntax throughout the language.
Pine Script uses bold text by applying (**) for their titles on their built-ins (e.g. @returns) but you could also use heading level 4 (####) and have it look the same.
▶️ Paragraphs & Line Breaks
You may want to provide extensive details and examples relating to one function, in this case, you could create line breaks. Creating line breaks skips to the next line so you can keep things organized as a result.
To achieve a valid line break and create a new paragraph, you must end the line with two or more spaces.
If you want to have an empty line in between, apply a backslash (\).
Backslashes (\) are generally not recommended for every line break. In this case, I only recommend using them for empty lines.
▶️ Text Formatting
Markdown provides text formatting such as bold, italics, and strikethrough.
For bolding text, you can apply open and close (**) or (__).
For italicizing text, you can apply open and close (*) or (_).
For bolding and italicizing text, you can apply open and close (***) or (___).
For s̶t̶r̶i̶k̶e̶t̶h̶r̶o̶u̶g̶h̶, you need to apply open and close (~~).
This was mentioned in the Headers section, but Pine Script's main titles (e.g. @returns or @syntax) use bold (**) by default.
▶️ Blockquotes
Blockquotes in Pine Script can be visualized as a built-in indentation system.
They are declared using greater than (>) and everything will be auto-aligned and indented until closed.
By convention you generally want to include the greater than (>) on every line that's included in the block quote. Even when not needed.
If you would like to indent even more (nested blockquotes), you can apply multiple greater than symbols (>). For example, (>>)
Blockquotes can be closed by ending the next line with only one greater than (>) symbol, or by using a horizontal rule.
▶️ Horizontal Rules
Horizontal rules in Pine Script are what you see at the very top of the prompt in built-ins.
When hovering, you can see the top of the prompt provides a line, and we can actually reproduce these lines.
These are extremely useful for separating information into their own parts and are accessed by applying 3 underscores (___), or 3 asterisks (***).
Horizontal rules were mentioned above, when we were discussing block quotes. These can also be used to close blockquotes as well.
Horizontal rules require a minimum of 3 underscores (___) or 3 asterisks (***).
▶️ Lists
Lists give us a way to structure data in a somewhat neat way. There are multiple ways to start a list, such as
1. First Item (number followed by a period)
- First Item (dash)
+ First Item (plus sign)
* First Item (asterisk)
Using number-based lists provide an ordered list, whereas using (-), (+), or (*) will provide an unordered list (bullet points).
If you want to begin an unordered list with a number that ends with a period, you must use an escape sequence (\) after the number.
Standard indentation (tab-width) list detection isn't supported, so to nest lists you have to use blockquotes (>) which may not look as appealing.
▶️ Code Blocks
Using code blocks allows you to write actual Pine Script code inside the prompt.
It's a game changer that can potentially help people understand how to execute functions quickly.
To use code blocks, apply three 3 open and close backquotes (```). Built-in's use (```pine) but there's no difference when we apply it.
Considering that tab-width indentation isn't detected properly, we can make use of the blockquotes mentioned above.
▶️ Denotation
Denoting can also be seen as highlighting a background layer behind text. They're basically code blocks, but without the "block".
Similar to how code blocks work, we apply one backquote open and close (`).
Make sure to only use this on important keywords. There really isn't a conventional way of applying this.
It's up to you to decide what people should have their eyes tracked onto when they hover over your functions.
If needed, look at how Pine Script's built-in variables and functions utilize this.
▶️ Tables
Tables are possible in Markdown, although they may look a bit different in the Pine Editor.
They are made by separating text with vertical bars (|).
The headers are detected when there is a minimum of one hyphen (-) below them.
You can align text by using a colon as I do in the photo. Hyphens must be connected to the colon in order to display correctly.
Tables aren't ideal to use in the editor but are there if anyone wants to give it a go.
▶️ Links & Images
Markdown supports images and hyperlinks, which means we can also do that here in the Pine Editor. Cool right?
If you want to create a hyperlink, surround the displayed text in open and close brackets .
If you want to load a photo into your prompt, it's the same syntax as the hyperlink, except it uses a (!)
See syntax list below.
Here are realistic usage examples. (Snippets from code below)
These follow the same syntax as the built-ins.
I'm not using horizontal rules here, but it's entirely up to you.
▶️ Syntax List
Headings
Level 1: #
Level 2: ##
Level 3: ###
Level 4: ####
Level 5: #####
Level 6: ######
Line Breaks
Text (two spaces)
Text\ (backslash)
Text Formatting
Bold (**)
Italic (**)
Strikethrough (~~)
Blockquotes
Indent (>)
Double Indent (>>)
Triple Indent (>>>) and so on.
Horizontal Rules
(___) or (***)
Lists
Ordered List (1.)
Unordered List (-) or (+) or (*)
Code Blocks
(```) or (```pine)
Denotation
(`)
Tables
(|) and (-) and (:)
Hyperlinks
(URL)
Images
! (URL)
Hope this helps. 👍
Grid Settings & MMThis script is designed to help you plan your grid trading or when averaging your position in the spot market.
The script has a small error (due to the simplification of the code), it does not take into account the size of the commission.
You can set any values on all parameters on any timeframe, except for the number of orders in the grid (from 2 to 5).
The usage algorithm is quite simple:
1. Connect the script
2. Install a Fibo grid on the chart - optional (settings at the bottom of the description)
3.On the selected pair, determine the HighPrice & LowPrice levels and insert their values
4.Evaluate grid data (levels, estimated profit ’%’, possible profit ‘$’...)
And it's all)
Block of variables for calculating grid and MM parameters
Variables used regularly
--- HighPrice and LowPrice - constant update when changing pairs
--- Deposit - deposit amount - periodically set the actual amount
Variables that do not require permanent changes
--- Grids - set the planned number of grids, default 5
--- Steps - the planned number of orders in the grid, by default 5
--- C_Order - coefficient of increasing the size of orders in the base coin, by default 1.2
--- C_Price - trading levels offset coefficient, default 1.1
--- FirstLevel - location of the first buy level, default 0.5
--- Back_HL - number of candles back, default 150
*** For C_Order and C_Price variables, the value 1 means the same order size and the same distance between buy levels.
The fibo grid is used for visualization, you can do without it, ! it is not tied to the script code !
You can calculate the levels of the Fibo grid using the formula:
(level price - minimum price) / (maximum price - minimum price)
For default values, grid levels are as follows:
1 ... 0.5
2...0.359
3 ... 0.211
4...0.0564
5...-0.1043
Short description:
in the upper right corner
--- indicator of the price movement for the last 150 candles, in % !!! there is no task here to "catch" the peak values - only a relative estimate.
in the upper left corner
--- total amount of the deposit
--- the planned number of grids
--- “cost” of one grid
--- the size of the estimated profit depending on the specified HighPrice & LowPrice
in the lower left corner
--- Buy - price levels for buy orders
--- Amount - the number of purchased coins in the corresponding order
--- Sell - levels of profit taking by the sum of market orders in the grid
--- $$$ - the sum of all orders in the grid, taking into account the last active order
--- TP - profit amount by the amount of orders in the grid
Bjorgum Double Tap█ OVERVIEW
Double Tap is a pattern recognition script aimed at detecting Double Tops and Double Bottoms. Double Tap can be applied to the broker emulator to observe historical results, run as a trading bot for live trade alerts in real time with entry signals, take profit, and stop orders, or to simply detect patterns.
█ CONCEPTS
How Is A Pattern Defined?
Doubles are technical formations that are both reversal patterns and breakout patterns. These formations typically have a distinctive “M” or a “W” shape with price action breaking beyond the neckline formed by the center of the pattern. They can be recognized when a pivot fails to break when tested for a second time and the retracement that follows breaks beyond the key level opposite. This can trap entrants that were playing in the direction of the prior trend. Entries are made on the breakout with a target projected beyond the neckline equal to the height of the pattern.
Pattern Recognition
Patterns are recognized through the use of zig-zag; a method of filtering price action by connecting swing highs and lows in an alternating fashion to establish trend, support and resistance, or derive shapes from price action. The script looks for the highest or lowest point in a given number of bars and updates a list with the values as they form. If the levels are exceeded, the values are updated. If the direction changes and a new significant point is made, a new point is added to the list and the process starts again. Meanwhile, we scan the list of values looking for the distinctive shape to form as previously described.
█ STRATEGY RESULTS
Back Testing
Historical back testing is the most common method to test a strategy due in part to the general ease of gathering quick results. The underlying theory is that any strategy that worked well in the past is likely to work well in the future, and conversely, any strategy that performed poorly in the past is likely to perform poorly in the future. It is easy to poke holes in this theory, however, as for one to accept it as gospel, one would have to assume that future results will match what has come to pass. The randomness of markets may see to it otherwise, so it is important to scrutinize results. Some commonly used methods are to compare to other markets or benchmarks, perform statistical analysis on the results over many iterations and on differing datasets, walk-forward testing, out-of-sample analysis, or a variety of other techniques. There are many ways to interpret the results, so it is important to do research and gain knowledge in the field prior to taking meaningful conclusions from them.
👉 In short, it would be naive to place trust in one good backtest and expect positive results to continue. For this reason, results have been omitted from this publication.
Repainting
Repainting is simply the difference in behaviour of a strategy in real time vs the results calculated on the historical dataset. The strategy, by default, will wait for confirmed signals and is thus designed to not repaint. Waiting for bar close for entires aligns results in the real time data feed to those calculated on historical bars, which contain far less data. By doing this we align the behaviour of the strategy on the 2 data types, which brings significance to the calculated results. To override this behaviour and introduce repainting one can select "Recalculate on every tick" from the properties tab. It is important to note that by doing this alerts may not align with results seen in the strategy tester when the chart is reloaded, and thus to do so is to forgo backtesting and restricts a strategy to forward testing only.
👉 It is possible to use this script as an indicator as opposed to a full strategy by disabling "Use Strategy" in the "Inputs" tab. Basic alerts for detection will be sent when patterns are detected as opposed to complex order syntax. For alerts mid-bar enable "Recalculate on every tick" , and for confirmed signals ensure it is disabled.
█ EXIT ORDERS
Limit and Stop Orders
By default, the strategy will place a stop loss at the invalidation point of the pattern. This point is beyond the pattern high in the case of Double Tops, or beneath the pattern low in the case of Double Bottoms. The target or take profit point is an equal-legs measurement, or 100% of the pattern height in the direction of the pattern bias. Both the stop and the limit level can be adjusted from the user menu as a percentage of the pattern height.
Trailing Stops
Optional from the menu is the implementation of an ATR based trailing stop. The trailing stop is designed to begin when the target projection is reached. From there, the script looks back a user-defined number of bars for the highest or lowest point +/- the ATR value. For tighter stops the user can look back a lesser number of bars, or decrease the ATR multiple. When using either Alertatron or Trading Connector, each change in the trail value will trigger an alert to update the stop order on the exchange to reflect the new trail price. This reduces latency and slippage that can occur when relying on alerts only as real exchange orders fill faster and remain in place in the event of a disruption in communication between your strategy and the exchange, which ensures a higher level of safety.
👉 It is important to note that in the case the trailing stop is enabled, limit orders are excluded from the exit criteria. Rather, the point in time that the limit value is exceeded is the point that the trail begins. As such, this method will exit by stop loss only.
█ ALERTS
Five Built-in 3rd Party Destinations
The following are five options for delivering alerts from Double Tap to live trade execution via third party API solutions or chat bots to share your trades on social media. These destinations can be selected from the input menu and alert syntax will automatically configure in alerts appropriately to manage trades.
Custom JSON
JSON, or JavaScript Object Notation, is a readable format for structuring data. It is used primarily to transmit data between a server and a web application. In regards to this script, this may be a custom intermediary web application designed to catch alerts and interface with an exchange API. The JSON message is a trade map for an application to read equipped with where its been, where its going, targets, stops, quantity; a full diagnostic of the current state and its previous state. A web application could be configured to follow the messages sent in this format and conduct trades in sync with alerts running on the TV server.
Below is an example of a rendered JSON alert:
{
"passphrase": "1234",
"time": "2022-05-01T17:50:05Z",
"ticker": "ETHUSDTPERP",
"plot": {
"stop_price": 2600.15,
"limit_price": 3100.45
},
"strategy": {
"position_size": 0.1,
"order_action": "buy",
"market_position": "long",
"market_position_size": 0,
"prev_market_position": "flat",
"prev_market_position_size": 0
}
}
Trading Connector
Trading Connector is a third party fully autonomous Chrome extension designed to catch alert webhooks from TradingView and interface with MT4/MT5 to execute live trades from your machine. Alerts to Trading Connector are simple; just select the destination from the input drop down menu, set your ticker in the "TC Ticker" box in the "Alert Strings" section and enter your URL in the alert window when configuring your alert.
Alertatron
Alertatron is an automated algo platform for cryptocurrency trading that is designed to automate your trading strategies. Although the platform is currently restricted to crypto, it offers a versatile interface with high flexibility syntax for complex market orders and conditions. To direct alerts to Alertatron, select the platform from the 3rd party drop down, configure your API key in the ”Alertatron Key” box and add your URL in the alert message box when making alerts.
3 Commas
3 Commas is an easy and quick to use click-and-go third party crypto API solution. Alerts are simple without overly complex syntax. Messages are simply pasted into alerts and executed as alerts are triggered. There are 4 boxes at the bottom of the "Inputs" tab where the appropriate messages to be placed. These messages can be copied from 3 Commas after the bots are set up and pasted directly into the settings menu. Remember to select 3 Commas as a destination from the third party drop down and place the appropriate URL in the alert message window.
Discord
Some may wish to share their trades with their friends in a Discord chat via webhook chat bot. Messages are configured to notify of the pattern type with targets and stop values. A bot can be configured through the integration menu in a Discord chat to which you have appropriate access. Select Discord from the 3rd party drop down menu and place your chat bot URL in the alert message window when configuring alerts.
👉 For further information regarding alert setup, refer to the platform specific instructions given by the chosen third party provider.
█ IMPORTANT NOTES
Setting Alerts
For alert messages to be properly delivered on order fills it is necessary to place the following placeholder in the alert message box when creating an alert.
{{strategy.order.alert_message}}
This placeholder will auto-populate the alert message with the appropriate syntax that is designated for the 3rd party selected in the user menu.
Order Sizing and Commissions
The values that are sent in alert messages are populated from live metrics calculated by the strategy. This means that the actual values in the "Properties" tab are used and must be set by the user. The initial capital, order size, commission, etc. are all used in the calculations, so it is important to set these prior to executing live trades. Be sure to set the commission to the values used by the exchange as well.
👉 It is important to understand that the calculations on the account size take place from the beginning of the price history of the strategy. This means that if historical results have inflated or depleted the account size from the beginning of trade history until now, the values sent in alerts will reflect the calculated size based on the inputs in the "Properties" tab. To start fresh, the user must set the date in the "Inputs" tab to the current date as to remove trades from the trade history. Failure to follow this instruction can result in an unexpected order size being sent in the alert.
█ FOR PINECODERS
• With the recent introduction of matrices in Pine, the script utilizes a matrix to track pivot points with the bars they occurred on, while tracking if that pivot has been traded against to prevent duplicate detections after a trade is exited.
• Alert messages are populated with placeholders ; capability that previously was only possible in alertcondition() , but has recently been extended to `strategy.*()` functions for use in the `alert_message` argument. This allows delivery of live trade values to populate in strategy alert messages.
• New arguments have been added to strategy.exit() , which allow differentiated messages to be sent based on whether the exit occurred at the stop or the limit. The new arguments used in this script are `alert_profit` and `alert_loss` to send messages to Discord
Risk Management & Position Size DashboardRisk Management and Position Size Calculator
This indicator is designed to assist traders by instantly providing the necessary information to execute orders based on defined risk parameters. This is not an indicator that tells you WHEN to buy or sell, but HOW MUCH to buy or sell based on your risk appetite. The indicator is compatible with any strategy and any instrument
The indicator uses some great pinescript features, allowing you to drag and drop your levels directly on the chart for instant feedback from the risk management dashboard. Huge timesaver.
Instructions:
1. The first time you add the indicator to your chart, you will be asked to identify your Entry, Stop and Target levels by clicking on them directly on the chart. You can at any point adjust these levels directly on the chart, or you can manually enter them in settings
2. Setup your risk profile in the indicator settings:
(a) Current Capital: Enter the current value of your trading account
(b) Max Risk Per Trade: Enter the maximum % of your Capital you want to risk per trade
(c) Max Leverage: Enter the maximum leverage you want applied to your account. This comes in handy when you trade on very low timeframes where position sizes can become very large. No matter what you enter here, position size will never exceed Max Risk Per Trade
(d) Stop Ticks: Number of ticks to use above/below the identified high/low for stops
(e) Use Current Price as Entry (Market Orders): By default, the system will use the entry level you have selected for limit orders. If you want to use the system to display data based on current price for market orders, select “Use Current Price as Entry (Market Orders)”
3. Based on your stop and target levels, the system will automatically display the dashboard for Longs (bottom of screen) or Shorts (top of screen). The levels will also be displayed to the right of the current price bar
4. Display Free Trade Level: toggle this on to display the level you should exit 50% of your position to make the trade risk free
5. Dashboard / Text size and level colours and positions are customisable
The Dashboard displays the following information at all times:
1. Entry
2. Stop
3. Trade Risk (%): Percentage price difference between Entry and Stop
4. Max Risk ($): Currency value of loss if the trade is stopped out
5. Size: Position Size to execute your trade based on your risk profile/appetite
6. Size($): Position Size in currency value
7. P&L($): Potential profit to your Capital in currency value
8. P&L(%): Potential profit to your Capital in %
9. R:R: Risk to Reward Ratio
10. % to Target: Percent price needs to move to achieve your target
Hope this helps you with your trading. I built this as I was tired of wasting time opening up spreadsheets or calculators to execute trades - especially on lower timeframes. Making the code open source so if you do make improvements to this I'd love to hear from you. Also open to suggestions.
Happy Trading!
