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Backtesting AI Strategies: The Complete Framework Your Backtest Showing 1,000% Returns Is Probably Lying to You In the age of AI tools and instant backtests, it's never been easier to generate beautiful equity curves. It's also never been easier to fool yourself. Backtesting isn't about proving your genius. It's about trying as hard as possible to break your idea before the market does. What Backtesting Is Really For Backtesting should answer boring, critical questions: Does this logic have any edge beyond randomness? How ugly do the drawdowns get when things go wrong? Does it survive different market regimes, or only one lucky period? What happens after costs, slippage, and realistic execution? In the AI era, you can run thousands of tests in minutes. That doesn't mean you should trust the first curve that looks good. The Classic Sins (Supercharged by AI) AI makes it easy to commit every backtesting error faster: Overfitting – Adding parameters and filters until the past looks perfect. Look‑ahead bias – Accidentally using data that wouldn't have been known at the time. Ignoring costs – Forgetting that spreads, fees, and slippage eat high‑frequency edges alive. Data snooping – Testing hundreds of variants and only remembering the winners. Each mistake quietly turns your "edge" into noise dressed up as science. A Clean, Honest Testing Framework You don't need a PhD. You need structure. Write the Hypothesis First "I think momentum in high‑volume stocks persists for 5–20 days." Document the why before you see the results. Split Your Data Training: where you rough in the idea. Validation: where you tune it. Test: a final, untouched slice you only use once. Compare Against Baselines Buy‑and‑hold. Random entries with similar risk rules. Walk Forward Train on past → test on the next chunk → roll forward. Mimic how you'd actually update the system in real time. Stress It High vol vs low vol. Trends, ranges, crashes. Key Metrics That Actually Matter Skip the exotic stats. Focus on: Max Drawdown – Can you survive it psychologically and financially? Expectancy – Average profit per trade after costs. Profit Factor – Gross profits / gross losses. Win Rate + Win/Loss Size – How often you win, and how big wins vs losses are. Monthly Consistency – How many months are red vs green. These tell you if the system is tradable, not just impressive. AI's Role: Helper, Not Judge AI can: Generate variations you wouldn't think of Run large test grids quickly Estimate parameter sensitivity But you still have to: Define what "good" looks like Reject fragile, curve‑fit solutions Decide when a system has truly failed and needs to be retired In other words, AI gives you the lab. You still have to be the scientist.
CME_MINI:NQ1!Education
by officialjackofalltrades
Neural Networks in Trading: Separating Hype from Reality "99% Accurate AI" Sounds Great — Until You See the Equity Curve If you've been around markets lately, you've seen the pitch: Our revolutionary AI uses deep neural networks to predict the market with 99% accuracy. In the era of big models and buzzwords, it's easy to get hypnotized by charts that go straight up. The problem isn't that neural networks are useless — it's that most people use them (and sell them) in ways that have nothing to do with real trading. What Neural Networks Actually Do Underneath the hype, a neural network is just a flexible function approximator: You feed it inputs (price, volume, indicators, sentiment, etc.) It learns internal weights that map those inputs to outputs It adjusts those weights to reduce error on past data They are powerful because they can model complex, non‑linear relationships. But that power is a double‑edged sword: they can also memorize noise and call it "pattern". The Big Myths (and the Boring Reality) Myth: "AI predicts direction with high accuracy" Reality: Markets are adaptive. High "accuracy" often means tiny moves or rare trades. A model that wins 90% of the time by making 0.1% might still blow up on the 10% it loses. Myth: "Deeper = Better" Reality: Extra layers don't magically create edge. Often, simple models with clear logic survive regimes better than giant black boxes. Myth: "The AI will find hidden alpha humans can't" Reality: It can only find what exists in the data you give it . Garbage in, overfit magic out. The AI revolution doesn't remove the need for market understanding — it punishes the lack of it faster. Where Neural Nets Make Sense in Trading In the AI era, the realistic edge isn't "my network predicts the next candle". It's using ML for jobs humans are bad at: Sentiment and Text – Classifying news and social feeds as bullish/bearish/neutral. Regime Detection – Clustering periods into "trend", "range", "crisis", etc. Feature Extraction – Turning raw data into useful signals that simpler rules can trade. Execution Optimization – Deciding how to slice orders to minimize impact and cost. In all of these, the network is a component of your system, not the entire strategy. The Overfitting Trap (Where Most AI Traders Die) Neural networks are overfitting machines if you don't constrain them. Signs you're in trouble: Almost perfect backtest equity curve Hundreds of parameters and indicators in the input Performance collapses when you shift the date range or symbol A few trades account for most of the profit Remember: the network is trying to minimize past error, not maximize future robustness. Practical Guidelines for Using Neural Nets in the AI Era Start With the Problem, Not the Model "I want to forecast tomorrow's close" is vague. "I want to classify if we're in a high‑volatility regime" is concrete. Keep Inputs Honest No look‑ahead data. Use realistic, survivorship‑aware histories. Hold Out Real Out‑of‑Sample Data Data the model never touches during training. Use it once as a final exam, not 20 times as another tuning set. Prefer Simple Uses Over "Magic" Use nets to rank or score, not to call exact highs and lows. Combine ML outputs with transparent risk rules. AI Is a Tool, Not a Free Lunch Neural networks are part of the AI trading toolkit — not the holy grail. In this era, the traders who win are the ones who can: Ask precise questions Understand what their models are actually doing Say "no" to beautiful but fragile backtests Use AI to extend your edge, not to replace thinking.
Education
by officialjackofalltrades
The Psychology of Letting AI Trade for YouThe Hardest Part of AI Trading Isn't the Code - It's Letting Go You can spend months building the perfect system. You backtest it. Tweak it. Optimize it. And then, the first time it takes three losses in a row, you override it. In the era of AI and automation, the battlefield has shifted. The challenge is no longer just "Can I build a system?" — it's "Can I trust it enough to let it work?" The New Psychological Game: Humans vs Their Own Bots We tell ourselves we want robots to remove emotion. What actually happens is more subtle: We stop being emotional about individual trades We start being emotional about the system itself Instead of: "Should I exit this trade?" you think: "Is the bot broken?" "Should I turn it off?" "Why did it take this trade? I wouldn't have." The emotions don't vanish. They just move up a level. The 5 Stages of AI Trading Psychology Euphoria – Early wins, "this thing is a money printer." Doubt – First real drawdown, "maybe it's not as good as I thought." Intervention – You start skipping signals, closing early, or adding your own trades. Confusion – You can no longer tell if results are from the system or from your meddling. Integration (or Abandonment) – Either you learn your role vs the system… or you conclude "AI doesn't work" and go back to pure manual trading. Most traders get stuck between stages 2–4. The goal is to move to stage 5 with eyes open . Calibrated Trust: Between Blind Faith and Total Control Two extremes kill AI trading: Blind Trust – "The bot knows best, I'll never question it." Zero Trust – "I'll override whenever I feel like it." You want calibrated trust : You understand how the system makes decisions You know its expected win rate, drawdown, and losing streaks You have written rules for when you will and won't intervene Think of it as a partnership: the AI follows the rules; you manage the environment and the risk. Designing Your Role Before You Turn the Bot On Before you ever hit "start", write down: Which signals you will take without second‑guessing Which situations require human review (major news, tech issues, extreme volatility) Your hard stop conditions: Max daily loss Max drawdown Max number of consecutive losses Your review schedule (weekly, monthly) for performance and logic If your rules only live in your head, your emotions will rewrite them in real time. Emotional Hacks for the AI Era Trade Smaller Than You Think You Should If you can't sleep, size is too big. No psychology trick beats position sizing. Check Less Often Every peek at P&L triggers a reaction. Schedule times to review, rather than watching every tick. Journal Your Urges, Not Just Your Trades Write down: "Wanted to stop the bot after 3 losses, didn't." Or: "Overrode this signal, why?" Separate Process From Outcome Good process + bad short‑term outcome is still a win . Bad process + good short‑term outcome is a landmine. Your Mind Is Still the Edge AI can: Scan faster Execute cleaner Track more variables than you ever could But only you can decide: What risk you are truly willing to take When a drawdown is "normal" vs unacceptable Whether the system still makes sense in the current regime In the AI trading era, the real edge is a calm, knowledgeable person who knows when to trust the system - and when to step back.
TVC:GOLDEducation
by officialjackofalltrades
The 5 Types of Trading Bots Every Trader Should Know Not All Trading Bots Are the Same - Some Amplify Your Edge, Some Amplify Your Pain Saying "I want a bot" is like saying "I want a vehicle": A Formula 1 car A delivery truck A scooter A helicopter All are vehicles. None are interchangeable. In the era of AI‑assisted trading, bots are execution engines for your ideas. This post breaks down five major bot archetypes so you can stop hunting for "the best bot" and start matching the right structure to your market, risk profile, and skill set. First Cold Truth: Bots Don't Create Edge — They Scale It Before we talk types, it’s worth being brutally honest: If your strategy has no edge, a bot just lets you lose money faster, more consistently, and with perfect discipline. Bots are about discipline , speed , and scalability . The edge still has to come from your logic, testing, and risk framework. Quick Map of the 5 Bot Types Trend‑Following: Ride directional moves, ignore the noise. Mean‑Reversion: Fade extremes, bet on snap‑backs. Grid: Harvest volatility inside a range. Signal‑Based: Turn ideas/alerts into consistent execution. Arbitrage: Exploit price differences between related markets. From here, you want to ask two things: What structure is the market in? and What structure is my brain comfortable with? Type 1 – Trend‑Following Bots These bots try to behave like a disciplined trend trader that never hesitates and never gets emotional. Core idea: Buy strength in uptrends, sell weakness in downtrends. Typical tools: Moving‑average crossovers (fast vs slow) Breakouts above recent highs or below recent lows Momentum filters (e.g., ADX, rate of change, volatility filters) Shine in: Clean, directional markets where pullbacks are shallow. Struggle in: Sideways chop where price crosses the same levels repeatedly. Main risk: A long sequence of small whipsaw losses when there is no real trend. In the AI era, you can use models to classify regimes (trending vs ranging) and only let the trend bot run when the environment actually supports it. //@version=6 indicator("Simple Trend Filter", overlay=true) fast = ta.ema(close, 20) slow = ta.ema(close, 50) trendUp = fast > slow trendDown = fast < slow // Simple visual trend filter plot(fast, color=color.teal) plot(slow, color=color.orange) bgcolor(trendUp ? color.new(color.teal, 90) : trendDown ? color.new(color.orange, 90) : na) This kind of logic is usually just one piece of a full bot, but it shows how a trend‑following engine "sees" the market. Type 2 – Mean‑Reversion Bots Mean‑reversion bots assume that, most of the time, price doesn't drift off to infinity — it oscillates around some reference value. Core idea: Fade overextended moves and bet on a return to the mean. Typical tools: RSI or Stochastic extremes ("overbought" / "oversold") Touches or pierces of Bollinger Bands Deviation from a moving average (z‑score, % distance) Shine in: Ranging markets, stable channels, and mean‑reverting pairs. Struggle in: Strong trends where "oversold" keeps getting more oversold. Main risk: One big runaway move can erase many small wins if sizing and stops are not controlled. These bots can feel smooth until they don't. AI can help here by measuring when volatility/range structure changes and cutting exposure before that "one big trend" shows up. Type 3 – Grid Bots Grid bots are volatility harvesters. They care less about direction and more about price oscillating through pre‑defined levels. Core idea: Place a ladder of buy and sell orders above and below price. Profit engine: As price bounces through the grid, the bot systematically buys lower, sells higher, and repeats. Shine in: Sideways but active markets that revisit levels frequently. Struggle in: Strong one‑way moves that blow through the grid and never mean‑revert. Main risk: Deep, unrealized drawdowns if price trends hard against the grid without a safety mechanism. Smart grid design in the AI era often includes: Dynamic grid width that widens or tightens based on volatility Max drawdown or margin‑usage limits that trigger a partial or full shutdown Regime filters that turn the grid off when a strong trend is detected Type 4 – Signal‑Based Bots Signal bots don't "think" on their own – they are pure executors. Their job is to turn a human or model‑generated signal into consistent, rules‑based action. Core idea: Separate idea generation from order execution . Signal sources can include: Multi‑indicator confluence (trend + volume + volatility) Pattern recognition (breakouts, candle patterns, structures) Order‑flow or whale‑tracking alerts On‑chain, macro, or sentiment data for crypto and indices Shine in: Any market where the underlying signal logic has been tested and proven. Struggle in: Environments where the signal is over‑fitted, delayed, or not monitored. Main risk: Blind faith in a black‑box signal without understanding its limits. This is where AI often plugs in directly – models generate scores or labels, and the bot simply acts when the score crosses a threshold. Type 5 – Arbitrage Bots Arbitrage bots focus on relationships instead of single charts. They look for small, temporary mispricings and try to lock them in. Core idea: Buy where something is cheap and sell where it's expensive, as close to simultaneously as possible. Common approaches: Same asset, different exchanges (spot vs spot or spot vs perp) Triangular FX arbitrage between three currency pairs Statistical arbitrage between correlated assets that have diverged Shine in: Fragmented, less efficient markets with occasional big gaps. Struggle in: Highly efficient markets where spreads and latency competition eat the edge. Main risk: Execution risk – slippage, fees, and delays can flip a theoretical "risk‑free" trade into a losing one. These are the most infrastructure‑heavy bots. Latency, connectivity, fee structure, and capital sizing matter as much as the model itself. Choosing Your Bot in the AI Era Instead of asking "Which bot makes the most?", ask: What market structure am I actually trading most of the time? How much drawdown and variance am I truly comfortable with? Am I more aligned with riding trends or fading extremes? What is my technical and infrastructure level right now? Where can AI realistically help me – signal quality, risk controls, or execution? AI can support you by: Classifying regimes (trend vs range) and routing orders to the right bot type Monitoring portfolio‑level risk across multiple bots and symbols Detecting when performance degrades and suggesting parameter reviews But the decision of which bot to run, when to turn it off, and how to size it is still your responsibility. Your Turn Which of these five bot types actually fits your temperament and the markets you trade right now? If you had to upgrade one layer of your automation with AI today - signal generation, risk management, or execution - which one would move the needle the most for you? Share it below. The clearer you are about what kind of bot you’re running and why , the less you’ll ever have to blame "the bot" when the outcome doesn’t match the plan.
TVC:GOLDEducation
by officialjackofalltrades
Pine Script v6: The AI-Assisted Coding RevolutionAI Isn't Replacing Pine Script Developers, It's Creating More of Them For years, if you wanted custom tools on $TRADINGVIEW, you had two options: Spend months learning to code, or Settle for whatever public indicators were available The era of AI assisted Pine Script changes that. You don't have to choose between "coder" and "trader" anymore, you can be both, with AI as your quiet co‑pilot. Why Pine Script + AI Is a Big Deal In the new AI trading era, edge comes from: Being able to test ideas quickly Turning those ideas into rules Automating those rules in a language the platform understands AI can't give you edge by itself. But it can remove almost all of the friction between the idea in your head and a working NYSE:PINE script on your chart. Instead of: Googling syntax Copy‑pasting random snippets Debugging mysterious errors at 2am you can describe your logic in plain language and let AI handle the boilerplate, while you stay in control of the trading logic. The Modern Pine Script Workflow (AI Edition) Old workflow: Learn programming basics from scratch Read documentation line‑by‑line Write every line of code yourself Fix every typo and bug manually New workflow: Define the strategy in plain English Ask AI to draft the first version in Pine Script v6 Review and understand what it wrote Refine, test, and harden it on your charts The difference isn't "AI does everything" it's AI accelerates everything . You move from "How do I code this?" to "Is this idea actually good?" much faster. What AI Is Great At in Pine Script Syntax and Structure - Getting the small details right: `indicator()` declarations `strategy()` settings Inputs, colors, line styles Common functions like `ta.sma`, `ta.rsi`, `ta.crossover` Boilerplate Code - The parts that repeat across almost every script: Input sections Plotting logic Alert conditions Explaining Code Back to You - You can paste a snippet and ask: "What does this variable do?" "Why is this `if` statement here?" "Can you rewrite this more clearly?" This is how you learn Pine Script by doing , instead of from a dry textbook. What AI Is NOT Good At (If You Rely on It Blindly) Designing Your Edge - AI doesn't know your risk tolerance, timeframe, or style. You still have to define the actual trading idea. Protecting You From Over‑Optimization Ask it to "improve" a strategy and it may add 20 inputs that look perfect on past data and fail live. Understanding Market Context - It can code the rules, but it doesn't "feel" what a trend, rotation, or macro regime shift means to you. Use AI as a smart assistant, not an oracle. Core Pine Script Concepts You Still Need Even in the AI era, a few fundamentals are non‑negotiable. Think of them as the alphabet you must know, even if AI writes the sentences: 1. Data Types float // prices, indicator values int // bar counts, lengths bool // conditions (true/false) string // labels, messages color // styling 2. Series Logic Every variable in Pine is a time series . You don't just have `close`, you have `close `, `close `, etc. close // current bar close close // previous bar close high // high from 5 bars ago 3. Built‑In Indicator Functions You don't need to reinvent moving averages and RSI: ma = ta.sma(close, 20) rsi = ta.rsi(close, 14) longCondition = ta.crossover(close, ma) If you understand what these do, AI can handle how to wire them together. A Clean AI‑Assisted Workflow to Build Your Next Indicator Write the idea in plain language "I want a trend filter that only shows long signals when price is above a 200‑period MA and volatility is not extreme." Ask AI for a first draft in Pine Script v6 Specify: overlay or separate pane, inputs you want, and what should be plotted. Read every line Use AI as a teacher: "Explain this variable", "Explain this block". Test on multiple markets and timeframes Does it behave the way you expect on CRYPTOCAP:FOREX , $CRYPTO, and stocks? Does it break on higher timeframes or very illiquid symbols? Iterate, don't chase perfection Tweak one idea at a time. Avoid adding endless inputs just to fix old trades. The Bigger Picture: Coders, Traders, and the AI Era The old split was: "Coders" who could build things but didn't trade "Traders" who had ideas but couldn't code them In the AI era, that wall disappears. The trader who can: Describe ideas clearly Use AI to generate Pine code Understand enough to test and refine …gets a massive edge over both pure coders and pure discretionary traders. You don't need to be perfect. You just need to be dangerous, one well‑tested script at a time. Your Turn If you could build one custom tool this month with AI's help, what would it be? An entry signal? A dashboard? A risk overlay? Drop your idea below and consider this your sign to finally turn it into code.
TVC:GOLDEducation
by officialjackofalltrades
AI Trading: The Revolution You Can't IgnoreThe Era of AI Trading Has Arrived And It's Only Getting Started Forget the movie version of AI glowing red eyes flawlessly predicting every tick. The real story is colder, quieter, and way more powerful: We are moving from a world where humans look at charts to a world where machines digest every tick, every candle, every flow of data… and feed you the edge you couldn't see on your own. Right now, as you read this, AI is already sitting inside: Execution algorithms routing institutional orders across venues Risk engines stress testing portfolios in milliseconds News and sentiment scanners parsing thousands of headlines a minute Retail tools that turn a paragraph of English into working NYSE:PINE Script This isn't science fiction. This is the baseline. And the baseline is rising. The traders who survive this decade won't be the ones fighting AI. They'll be the ones partnering with it. So What Exactly Is "AI Trading"? At its core, AI trading is simply using algorithms that can learn from data to make parts of the trading process smarter. That can mean anything from a small script that filters charts for you, all the way up to full stack systems managing billions. The spectrum looks like this: AI Assisted Analysis You still click the buttons, but AI does the heavy lifting. Pattern recognition on charts (trend, ranges, breakouts) Scanning hundreds of symbols for your exact conditions Sentiment analysis on news and earnings headlines Idea generation: "Show me all large‑cap stocks breaking out with above‑average volume" AI‑Generated Signals The machine tells you what it would do; you decide whether to listen. Multi‑indicator models that output clear long/short/flat signals Quant models that score each asset from 0-100 based on your rules Bots that push alerts when high‑probability setups appear Fully Automated Trading The system trades end‑to‑end while you supervise. Execution from signal → order → risk control with no manual clicks Self adjusting position sizing and risk controls Strategies that re‑train on fresh data as regimes shift Wherever you are on that spectrum, you're already in the AI game. The question isn't "Will I use AI?" it's "How deeply will I let it into my process?" How AI Actually "Sees" the Market Humans see a chart. AI sees a dataset. Human view: One instrument at a time A couple of timeframes A handful of indicators you like Heavily filtered through emotion and bias AI view: Thousands of symbols at once Dozens of timeframes and derived features Years of historical data compressed into patterns Zero fear, zero FOMO, zero boredom Feed a model clean data and it can uncover: Regimes you feel but can't quantify (trend, chop, grind, panic) Relationships between assets that hold statistically Behavioral patterns like "late‑day reversals after gap‑up opens" Execution patterns in the order book around key levels But here's the crazy part: AI is completely unforgiving about your assumptions. If the data is noisy, biased, or poorly structured, the model will happily learn the wrong thing and apply it with perfect discipline. "Garbage in, garbage out" gets amplified at machine speed. The Era We're Entering: Human + AI, Not Human vs AI Over the next decade, expect three shifts to accelerate: From Intuition First → Data‑First Traders will still have hunches, but they'll validate them against hard data. Instead of "this looks extended", you'll ask the system: "How often do moves like this actually continue?" and get an answer in seconds. From Single‑Indicator Thinking → Multi‑Signal Models No more worshiping one magic oscillator. AI will blend technicals, fundamentals, flows, and sentiment into a unified view. From Static Systems → Adaptive Systems Instead of one set of parameters forever, models will adapt as volatility, liquidity, and structure change. Think of it as a trading playbook that rewrites itself when the game changes. Technologies like larger language models, specialized chips, and eventually quantum‑accelerated optimization won't magically "solve" markets, but they will make it cheaper and faster to test ideas, build systems, and manage risk. The edge shifts from "Can I code this?" to "Can I ask the right questions, define the right constraints, and manage the risk around what the models tell me?" Where You Fit In As a Trader In the era of AI, your job becomes less about staring at every tick, and more about designing the rules of the game your tools play. You define what "good" trades look like. You choose which markets, timeframes, and risks matter. You decide when a model is behaving, and when it's time to shut it off. AI gives you: Speed: scanning what you could never cover alone Consistency: executing the plan without emotional drift Feedback: showing you what really works in your own data You bring: Context: macro, narrative, and common sense Values: what risks you refuse to take Adaptability: knowing when to step back or switch regimes Put together, that's where the edge lives. Getting Started in the AI Era (Without Getting Overwhelmed) You don't need a PhD, a server rack, or a lab full of quants. You can start small and intelligent: Audit Your Current Process Where are you slow? (Scanning, journaling, testing?) Where are you emotional? (Entries, exits, sizing?) Those are prime targets for AI assistance. Add One AI Tool at a Time Maybe it's an AI screener. Maybe it's an NYSE:PINE Script assistant that helps you code and backtest. Maybe it's a journaling tool that tags your trades automatically. Learn to Read the Data Behind the Magic Look at win rate, drawdown, expectancy. Compare AI filtered setups to your old ones. Keep what clearly improves your edge; drop the rest. Respect the Risks Over‑fitted models that look perfect on the past. Black‑box systems you can't explain. Over‑reliance on automation with no kill switch. Is AI Going to Take Over Trading Completely? In some corners of the market, AI and automation already dominate . High‑frequency execution, index rebalancing, options market‑making, these domains are machine territory. But markets are more than math. They are human fear, greed, regulation, politics, liquidity constraints, structural changes and unexpected shocks. That messy mix is exactly where human oversight still matters. The most realistic future isn't "AI replaces traders" it's AI replaces undisciplined, unstructured traders who bring nothing but guesses to the table. Traders who can think in systems, understand risk, and collaborate with machines? They don't get replaced. They get leverage. Your Turn Where are you right now in this evolution? Still fully manual, doing everything by hand? Using a few AI assisted tools but not trusting them yet? Already running bots and systematic strategies? What part of AI trading are you most curious or skeptical about? And the big question: Do you think the future of trading belongs to AI, or to traders who know how to use it? Drop your thoughts in the comments this era is just beginning.
CME_MINI:NQ1!Education
by officialjackofalltrades
The market isn’t random. It’s driven by algorithms.The market is not arbitrary. It is powered by algorithms that essentially accomplish just two tasks: either push the price in the direction of the next liquidity pool or pull it back to fill the orders they missed en route, such as leftover blocks, imbalances, and unfulfilled orders. Understanding that basic behavior is the foundation of everything I trade. Since it indicates where the algorithm is attempting to go next, I begin with the higher-timeframe trend. Then, in order to determine which side is in control, I wait for a powerful push, a distinct, quick displacement. The algorithm nearly always retraces slowly after that push because it must return to correct imbalances and complete the orders it overlooked. Additionally, that gradual decline indicates that the trend is still going strong. A quick or forceful pullback indicates that the algorithm is probably changing course because it is creating new imbalances rather than going back to correct the previous ones. I therefore only accept trades when the price gradually returns to my order blocks, imbalances, or prior liquidity areas before moving on to the next pool of liquidity. I don't forecast highs or lows. I do not oppose the market. All I'm doing is following the algorithm as it shifts from one liquidity pool to the next, making any necessary corrections before moving on.
Education
by GreenhillFX
#ALGO/USDT - this will go up#ALGO The price is moving within an ascending channel on the 1-hour timeframe and is adhering to it well. It is poised to break out strongly and retest the channel. We have a downtrend line on the RSI indicator that is about to break and retest, which supports the upward move. There is a key support zone in green at 0.1764, representing a strong support point. We have a trend of consolidation above the 100-period moving average. Entry price: 0.1784 First target: 0.1811 Second target: 0.1840 Third target: 0.1870 Don't forget a simple money management rule: Place your stop-loss order below the green support zone. Once you reach the first target, save some money and then change your stop-loss order to an entry order. For any questions, please leave a comment. Thank you.
Long
by CryptoAnalystSignal
A few important steps for creating robust and winning StrategiesAs the title says, I want to share knowledge & important insights into the best practices for creating robust, trustworthy and profitable trading Strategies here on TradingView. These bits of information that my team I have gathered throughout the years and have managed to learn through mostly trial and error. Costly errors too . Many of these points more professional traders know, however, there are some that are quite innovative for all levels of experience in my opinion. Please, feel free to correct me or add more in the comments. There are a few strategic and tactical changes to our process that made a noticeable difference in the quality of Strategies and Indicators immediately. Firstly and most importantly, we have all heard about it, but it is having the most data available. A good algorithm, when being built NEEDS to have as many market situations in its training data as possible. Choppy markets, uptrends, downtrends, fakeouts, manipulations - all of these are necessary for the strategy to learn the possible market conditions as much as possible and be prepared for trading on unknown data. Many may have heard the phrase "History doesn't repeat itself but rhymes well" - you need to have the whole dictionary of price movements to be able to spot when it rhymes and act accordingly. The TradingView Ultimate plan offers the most data in terms of historical candles and is best suited for creating robust strategies. ___ Secondly, of course, robustness tests. Your algorithm can perform amazingly on training data, but start losing immediately in real time, even if you have trained it on decades of data. These include Monte-carlo simulations to see best and worst scenarios during the training period. Tests also include the fundamentally important out-of-sample checks . For those who aren’t familiar - this means that you should separate data into training sets and testing sets. You should train your algorithm on some data, then perform a test on unknown to the optimization process data. It's common practice to separate data as 20% training / 20% unknown / 20% training etc. to build a data set that will show how your algorithm performs on unknown to it market movements. Out of sample tests are crucial and you can never trust a strategy that has not been through them. Walk-forward simulations are similar - you train your algorithm on X amount of data and simulate real-time price feeds and monitor how it performs. You can use the Replay function of TradingView to do walk-forward tests! When you are doing robustness tests, we have found that a stable strategy performs around 90% similarly in terms of win rate and Sortino ratio compared to training data. The higher the correlation between training performance and out of sample performance, the more risk you can allocate to this algorithm. ___ Now lets move onto some more niche details. Markets don’t behave the same when they are trending downward and when they are trending upwards. We have found that separating parameters for optimization into two - for long and for short - independent of each other, has greatly improved performance and also stability. Logically it is obvious when you look at market movements. In our case, with cryptocurrencies, there is a clear difference between the duration and intensity of “dumps” and “pumps”. This is normal, since the psychology of traders is different during bearish and bullish periods. Yes, introducing double the amount of parameters into an algorithm, once for long, once for short, can carry the risk of overfitting since the better the optimizer (manual or not), the better the values will be adjusted to fit training data. But if you apply the robustness tests mentioned above, you will find that performance is greatly increased by simply splitting trade logic between long and short. Same goes for indicators. Some indicators are great for uptrends but not for downtrends. Why have conditions for short positions that include indicators that are great for longs but suck at shorting, when you can use ones that perform better in the given context? ___ Moving on - while overfitting is the main worry when making an algorithm, underoptimization as a result of fear of overfitting is a big threat too . You need to find the right balance by using robustness tests. In the beginning, we had limited access to software to test our strategies out of sample and we found out that we were underoptimizing because we were scared of overfitting, while in reality we were just holding back the performance out of fear. Whats worse is we attributed the losses in live trading to what we thought was overfitting, while in reality we were handicapping the algorithm out of fear. ___ Finally, and this relates to trading in general too, we put in place very strict rules and guidelines on what indicators to use in combination with others and what their parameter range is. We went right to theory and capped the values for each indicator to be within the predefined limits. A simple example is MACD . Your optimizer might make a condition that includes MACD with a fast length of 200, slow length of 160 and signal length of 100. This may look amazing on backtesting and may work for a bit on live testing, but these values are FUNDAMENTALLY wrong (Investopedia, MACD). You must know what each indicator does and how it calculates its values. Having a fast length bigger than the slow one is completely backwards, but the results may show otherwise. When you optimize any strategy, manually or with the help of a software, be mindful of the theory. Mathematical formulas don’t care about the indicator’s logic, only about the best combination of numbers to reach the goal you are optimizing for - be it % Return, Profit Factor or other. Parabolic SAR is another one - you can optimize values like 0.267; 0.001; 0.7899 or the sort and have great performance on backtesting. This, however, is completely wrong when you look into the indicator and it’s default values (Investopedia, Parabolic SAR). To prevent overfitting and ensure a stable profitability over time, make sure that all parameters are within their theoretical limits and constraints, ideally very close to their default values. Thank you for reading this long essay and I hope that at least some of our experience will help you in the future. We have suffered greatly due to things like not following trading theory and leaving it all up to pure mathematical optimization, which is ignorant of the principles of the indicators. The separation between Long / Short logic was also an amazing instant improvement. View the linked idea where we explain the psychology of risk management and suggest a few great ways to calculate and manage your risk when trading - just as important as the strategy itself! What do you think? Do you use any of these methods; Or better ones? Let us know in the comments.
NASDAQ:AAPLEducation
by sofex
HOW TO Master Algo Trading: Essential Skills for Modern Trading🤖 Algo trading isn’t just about letting robots do the heavy lifting. It’s also not letting a machine take over your trading. Algo trading uses computer programs to help you to automate buying and selling in financial markets based on set rules. So if you have a mechanical system with a track record, you’re on your way of becoming an algo trader. BUT… There are always ways to improve your trading and there are elements you can use to become a more proficient algo trader. Let’s get into them. 🔢 Element #1: Experience with Database Management and Data Analysis Data is your best friend when it comes to algo trading. You need to know the trading game plan before you take your first trade. It’s like building your city with an end goal. You need a map, you need the tools, you need a worst-case scenario plan etc… Data analysis, on the other hand, allows you to extract meaningful insights from this data. You need to know how back, forward and real test your system, strategy and results. The more data you have, the more significant edge you’ll have over those who rely on gut feeling alone. 📊 Element #2: Knowledge of Statistical Analysis Statistical analysis and machine learning are the backbone of successful algo trading. They empower you to create models that predict market movements and optimize trading strategies. This is where your important rules, criteria and decisions come. E.g. When do you halt trading after a drawdown. When do you consider a medium and high probability trade. When do you consider a medium to high probability day. What do you consider high, medium and low probability markets. Do you know how to handle Pre-market movers? Remember, markets are influenced by countless factors, and understanding these relationships requires robust statistical tools. 💹 Element #3: Understand Financial Markets and Trading Strategies While technology drives algo trading, understanding the financial markets is crucial. You need to grasp how different markets operate, from stocks, indices, commodities and Forex with their unique characteristics of each. Each market has it’s own personality and demeanor. For example, for the life of me my system does NOT work with the EUR/USD – The most popular currency of all time. And I’ve accepted that. Without this understanding, you might as well be throwing darts at a board while blindfolded. 🕵️ Element #4: Strong Analytical and Problem-Solving Skills Markets are unpredictable. They are also random and uncertain. They throw curveballs when you least expect them. Your winning streaks can last longer than you think. But so can your drawdowns. And that period where the market moves sideways, can make a trader go crazy. That’s why strong analytical and problem-solving skills are vital. When an algorithm isn’t performing as expected, you need to diagnose the issue swiftly and effectively. Think of it like being a detective in the trading world. You need to analyze patterns, identify anomalies, and adjust your strategies to stay on top. This requires a sharp, analytical mind and a knack for solving complex problems under pressure. 🧠 Element #5: Attention to Detail and Ability to Work Under Pressure In algo trading, the devil is in the details. One small error in your system can lead to significant financial losses. One wrong parameter in your moving average or indicator, and it could determine a failed strategy. Therefore, meticulous attention to detail is non-negotiable. And you need to adapt like a robot because trading is definitely working under pressure. This is a skill that we are NOT born with but one must learn through sheer will and hard experience. Financial markets operate at lightning speed, and decisions often need to be made in real-time. The ability to stay calm and focused in such an environment can make or break your trading success. Final words: Mastering algo trading requires a blend of technical skills, market knowledge, and the right tools. Let’s sum up what it is and what you need to master the skills. Algo trading, or algorithmic trading, involves using computer algorithms to automate trading decisions based on predefined criteria and market data analysis. It aims to execute trades at optimal speeds and prices, leveraging technology to minimize human error and emotional bias. The skills you need to master are: Element #1: Experience with Database Management and Data Analysis Element #2: Knowledge of Statistical Analysis Element #3: Understand Financial Markets and Trading Strategies Element #4: Strong Analytical and Problem-Solving Skills Element #5: Attention to Detail and Ability to Work Under Pressure
JSE:J200Education
by Timonrosso
ALGO/USDT - Swing Setup | Low-Risk Long Targeting +273%🚀 Trade Setup Details: 🕯 #ALGO/USDT 🔼 Buy | Long 🔼 ⌛️ TimeFrame: 1D -------------------- 🛡 Risk Management (Example): 🛡 Based on $10,000 Balance 🛡 Loss-Limit: 1% (Conservative) 🛡 The Signal Margin: $409.84 -------------------- ☄️ En1: 0.2258 (Amount: $40.98) ☄️ En2: 0.2056 (Amount: $143.44) ☄️ En3: 0.1924 (Amount: $184.43) ☄️ En4: 0.1799 (Amount: $40.98) -------------------- ☄️ If All Entries Are Activated, Then: ☄️ Average.En: 0.1992 ($409.84) -------------------- ☑️ TP1: 0.2835 (+42.32%) (RR:1.73) ☑️ TP2: 0.336 (+68.67%) (RR:2.81) ☑️ TP3: 0.4171 (+109.39%) (RR:4.48) ☑️ TP4: 0.549 (+175.6%) (RR:7.2) ☑️ TP5: 0.7437 (+273.34%) (RR:11.2) ☑️ TP6: Open 🔝 -------------------- ❌ SL: 0.1506 (-24.4%) (-$100) -------------------- 💯 Maximum.Lev: 2X ⌛️ Trading Type: Swing Trading ‼️ Signal Risk: 🙂 Low-Risk! 🙂 -------------------- 🔗 www.tradingview.com ❤️ Your Like & Comments are valuable to us ❤️
Long
by MergicNet
2
ALGOUSDT Forming Bullish WaveALGOUSDT is currently demonstrating a Bullish Wave Pattern, a strong technical signal that often precedes a significant upward price movement. This pattern typically forms in trending markets, suggesting a series of higher highs and higher lows that indicate sustained buying interest. With this wave structure unfolding, the market appears to be favoring a continuation of the current uptrend, making ALGO an appealing candidate for mid-term gains. The volume profile is showing a steady increase, which supports the pattern’s validity and hints at rising momentum. A strong volume base during the formation of bullish wave patterns is often an early indication of institutional accumulation or renewed retail participation. With key support levels holding firm and resistance levels gradually weakening, the setup points to a potential price surge of 50% to 60% or more if bullish momentum continues. Algorand’s fundamentals are also contributing to growing investor interest. Known for its scalable blockchain technology and low transaction fees, ALGO has seen increasing adoption in DeFi and enterprise-level blockchain solutions. This growing utility, combined with the current bullish technical setup, enhances the coin’s attractiveness for both swing traders and long-term holders. In summary, ALGOUSDT’s bullish wave pattern coupled with rising volume and positive sentiment could signal the start of a powerful upward move. Traders should keep an eye on breakout zones and confirmation candles to capitalize on this emerging opportunity. ✅ Show your support by hitting the like button and ✅ Leaving a comment below! (What is You opinion about this Coin) Your feedback and engagement keep me inspired to share more insightful market analysis with you!
Long
by Alpha-GoldFX
ETH: Huge Reversal or Correction Still in the Horizon?The recent price action in Ethereum (ETH) has left market participants questioning its next major move. With volatility high and sentiment shifting, is ETH headed for a massive reversal, or is a correction still looming on the horizon? Key Points to Consider Macro Environment: Global markets are facing uncertainty from shifting interest rates and regulatory developments in crypto. These factors may spark continued volatility for ETH in the near term. Technical Analysis: ETH has many analysts watching key support and resistance areas. A clean break above resistance could signal a reversal, while a failure to hold recent gains may suggest a correction is not over yet. On-Chain Data: Activity on the Ethereum network, including DeFi usage and staking patterns, can offer clues as to whether accumulation or distribution is taking place. Market Sentiment: Traders remain divided, with some calling the recent bounce a bull trap, and others anticipating renewed upside momentum. My Take While the case for a reversal is gaining strength, the possibility of a broader correction can’t be dismissed. It’s crucial for investors to stay alert, watch the charts, and position size accordingly. I'm not longing any crypto in the short run...As for the long run, extremely BULLISH! *not investment advice* #Ethereum #Crypto #Investing #MarketAnalysis #Web3 #crypto #bitcoin #trading
by signalgo
ALGO: Surprise Rally Ahead?Yello Paradisers, did you spot that breakout from the descending channel in time or are you still waiting for confirmation that already happened? 💎#ALGO/USDT has officially broken out of its multi-week descending channel after a textbook Break of Structure (BoS). Price action gave us a beautiful retrace into the demand zone, and from there boom momentum began shifting. This move is now showing early signs of bullish continuation, but as always, the majority will only realize it once the big move is already done. 💎#ALGO had been trading within a clearly defined descending channel, with price reacting precisely between the descending resistance and descending support lines. The breakout occurred after a strong candle pierced the descending resistance, and more importantly, we got a BoS followed by a clean retest of the demand zone around $0.165–$0.175. That reaction was sharp and decisive, indicating strong interest from buyers stepping in. 💎As of now, it is trading near $0.186 and forming higher lows, which supports the idea of an ongoing trend reversal. The structure has changed, and bulls are slowly regaining control. If price sustains above $0.190, the doors open for further upside. The first key level that could slow momentum is minor resistance near $0.210. If that level breaks with volume, moderate resistance around $0.230 becomes the next critical zone. Above that, the major target sits at $0.260, a strong resistance area where a lot of liquidity could be triggered. 💎On the flip side, the strong support zone between $0.145 and $0.155 remains our invalidation area. If price drops below this range, the bullish setup collapses, and we reassess the entire structure. But unless that happens, the bias remains cautiously bullish. Trade Smart. Stay Patient. Be Consistent. MyCryptoParadise iFeel the success🌴
Long
by MyCryptoParadise
7