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Building a Trading System: From Idea to ExecutionEvery trader starts with an idea — a setup, a pattern, a theory that seems to work. But until that idea becomes a structured system, it’s just intuition. A trading system gives your ideas rules, logic, and repeatability. That’s the difference between a trader who hopes, and a trader who executes. Define the Core Idea Every system begins with an observation. Maybe you notice breakouts after volume spikes, or reversals after RSI divergence. Whatever the logic, write it down. A system has to be specific, if you can’t define it clearly, you can’t test it. Set Your Entry and Exit Rules Your system should answer three things precisely: When to enter a trade When to exit a trade How much to risk Ambiguity is the enemy. Rules make your strategy repeatable, testable, and objective. Backtest the Logic Before going live, test your rules on past data. You’re not looking for perfection, you’re looking for consistency. If your logic survives bull, bear, and sideways markets, it’s valid. Track win rate, drawdown, and profit factor — they’ll tell you what’s working. Execute With Discipline A system only works if you do. Follow the rules exactly as tested, even when it feels uncomfortable. Consistency turns probability into profit — emotions destroy both. Application Here we have a very good example from our trading signals where we executed one of our strategies for 10 days. The strategy was designed with detailed inputs, logic and executed with a precise setup in a trading bot. Refine and Evolve Once live, keep notes. Track how the system performs under real conditions. Make small, measured improvements based on data, not emotion. A system should evolve, not change its identity. Redefining and tuning is a part of the process, there is no strategy that lasts forever, everything needs to evolve and adjust!
COINBASE:BTCUSDEducation
by Green_Squad
3
Ancient DWEB Node Trends on BTC *No audio new microphone in the mail *Use the video speed controls to speed/slow this up if needed. *Use the full screen option to see better The objective here is to show you just how dialed in these node trends are to price and time. The example I am showing is back-testing nodes from July 2011. Casting just these 2 Gann fans aligned with the nodes, would have given you years of valid data. This is just scratching the surface in terms of what the system is capable of. Even though the fans/trends in this video are from one of the first pumps BTC had, you will probably notice that they held up to many cycles along the way. Almost as if to capture the end of a era when the price left them for good. Things to look for when using node trends: Precision matters: Alignment is everything! Proper placement is going to give you more consistent results. A few pixels off at the start of a trend can alter the trend by 25% much later on. This is also even more important as you progress. Especially when you start to analyze the intersections. Take the time to do it right and trust the nodes without bias. It should be a built-in habit. Back-testing is your friend so you can practice your trades and journal what you saw and the actions you would have taken. Beginners: Always look for the longest trends with anchor nodes at a peak or bottom. The trajectory, shape, and length of the nodes and node trends are telling so always look for patterns! Are they running flat, downward, upward, or is there a long period with no nodes? Are the nodes making a cradle or curve? Think about what that means each time you see these events. Are nodes in bunches (over or under the price?) Intersections in node trends themselves (as seen in vid): This is usually lagging data because the node trends with have already printed, BUT these make meaningful levels horizontally in the future. They also create many great intersections ahead for pivot and often price data when done correctly . Intersections in the trends you have drawn (fans or rays): This takes practice and can be levels or pivot data Again, this is just the beginning and to show you these trends are legit and how you can learn to trust them for trading with practice. A "step one" if you will to get you started. The more trends you add the more data you get. Unfortunately, when doing this, the charts get very busy and it does not make for a nice looking presentation. Trust me, people hate noisy charts and they assume you are a random line artist. Therefore, I will do my best to dissect the chart into different parts along the way to keep it simple. If there is enough interest in the this system, to make it worthwhile , I can envision dozens of videos and streams will be needed to understand it's use cases.
INDEX:BTCUSDLong
by CaptBlackBeard
Understanding Psychological LevelsDefinition: In Trading, Psychological levels are often called round numbers or psy levels. This is because the price ends in zeros and fives naturally attracting a trader’s attention. Examples: • Forex: 1.0000, 1.0500, 1.1000 • Stocks: $50, $100, $150, $200, $250 • Cryptocurrency: $10,000, $15,000, $20,000, $25,000 These levels are crucial as traders instinctively see targets in round numbers. (Or Incremental levels such as 5, 10, 15, 20, 25, 30 and so on... This causes many buy, sell, and stop orders to cluster around the same price zones, creating self-reinforcing areas of interest in the market. Again, price sits at 113.2k – Psychological level is 115k. ___________________________________________________________________________________ Why Psychological Levels Matter in Trading 1) Human Bias: Traders and investors often place orders at simple, rounded numbers. This makes their charts and order list “Clean.” 2) Institutional Targeting: Large groups, whales or organizations use these levels to find liquidity or trigger stops. (Eg, BTC swept 125k before dumping) 3) Market Memory: When a Psychological level reacts, traders remember it, and it often becomes relevant again in the future. (Turns into a prev liquidity sweep.) 5) Order Clustering: Stop losses, take profits, and pending orders frequently build up around these areas. (As above, it builds liquidity.) __________________________________________________________________________________ How to Identify Psychological Levels Begin with marking clean, round (or quarterly) numbers on your chart. These are often major levels such as 4.0000, 5.0000, or 6.0000. See the example below: Then identify the midpoints/quarter points between them, like 4.5, 5.5, 6.5, 7.5, 8.5 See the example below: For stronger assessments, look for psychological levels that align with other forms & tools of technical confluence—such as previous S & R, Supply/Demand, Highs & Lows, Fibonacci retracements, trendlines, or volume clusters. See the example below: When multiple forms of technical evidence converge near a round number, the level tends to have greater impact. __________________________________________________________________________________ Trading Around Psychological Levels When price approaches a psychological level, three common behaviors can occur: 1) Rejection: Price touches the level and reverses quickly, suggesting strong defense by buyers or sellers. (Liquidity Sweep) 2) Break and Retest: Price breaks through the level, then revisits it to confirm it as new support or resistance. 3) Compression or Grind: Price consolidates near the level before a breakout as liquidity builds up. Practical Application: Enable alerts slightly before major psychological levels to observe reactions in real time (for example, 4.45 instead of 4.5 ). Wait for confirmation using price action such as a clear rejection wick, an engulfing candle, or a BOS (Break of Structure). Combine this analysis with liquidity or other forms of technical tools for a stronger assessment. __________________________________________________________________________________ Trader Behavior at These Levels Market reactions at psychological levels are largely directed by emotion and herd (Group) behavior. Fear of missing out can push price through a round number with momentum & speed while profit-taking can trigger short-term reversals & rejections. Stop hunts are also common, where smart money briefly pushes prices beyond a round level to collect liquidity before reversing. (From 4.0 up to 4.25 then down again) Because many traders watch these same levels, reactions often repeat, reinforcing their significance. __________________________________________________________________________________ Example: BTC/USD for $125k When Bitcoin approaches $125k, many retail traders view it as a significant threshold. They might place short orders just below it or stop just above. Institutions recognize this and may intentionally push prices above $125k (sweeping $126k) to trigger those stops and fill large positions. Once that liquidity is collected, price can reverse, and the $125k area may later serve as a new resistance zone. This type of liquidity hunt and reversal pattern occurs frequently across all markets. __________________________________________________________________________________ Practical Tips 1) Never trade purely based on a round number. Always wait for confirmation through structure or price action. (Retests, MSS, BOS, candle patterns etc) 2) Use alerts & alarms rather than fixed lines; prices often wick slightly above or below the exact level. 3) On higher timeframes, psychological levels often act as major turning zones. On lower timeframes, they tend to attract short-term reactions. (Lower the time frame, the more reactions = constant noise) 4) Combine psychological levels with liquidity, order flow, or volume analysis for a more complete view. __________________________________________________________________________________ Summary Psychological levels are where human reactions and liquidity meet. They represent areas of emotional and institutional/organizational interest rather than fixed points of reversal. By understanding how traders behave around these zones and observing how price reacts to them, you can determine key movements with greater confidence.
Education
by King_BennyBag
51
Market Seasonality: Finding Statistical Edges in Price Patterns🟢 Overview Market seasonality refers to recurring, quantifiable patterns in asset price movements that appear consistently across different time periods. Rather than mystical predictions, these patterns reflect systematic behavioral trends, institutional flows, and market structures that have persisted across years, and in some cases, centuries, of trading history. 🟢 How Seasonality Works Seasonality analysis examines historical price data to identify months or periods when specific assets have historically shown strength or weakness. The approach replaces emotion-driven decision-making with probabilistic insights based on historical performance across complete market cycles, including bull markets, bear markets, and periods of consolidation. By quantifying these patterns, traders and investors can identify potential statistical edges in their execution timing. 🟢 Evidence Across Asset Classes 1. Bitcoin INDEX:BTCUSD Since the development of futures markets and institutional participation, Bitcoin has demonstrated notable seasonal patterns with measurable statistical significance. September has averaged -1.92% returns, establishing it as the weakest month. In contrast, October has emerged as the strongest performer with average returns of +21.59% and a 90% positive occurrence. This level of consistency suggests a robust statistical edge rather than random variation. Day-of-week patterns in modern Bitcoin are relatively tight, with differences ranging from 0.07% to 0.50%. Monday edges out as the optimal day for selling positions. However, these daily patterns offer considerably less statistical significance than the monthly seasonality effects, as the weekly variations have smoothed out compared to Bitcoin's earlier history. 2. Ethereum INDEX:ETHUSD Ethereum displays even more pronounced seasonal variations with stronger directional bias. September has been particularly challenging, averaging -10.04% returns and showing negative performance in eight out of ten years, representing an 80% probability of decline. June also demonstrates weakness at -7.20% average returns. Conversely, May stands out as the strongest month with average returns of +34.97%, positive 70% of the time across the dataset. May has delivered positive returns in seven out of ten years, providing a statistically meaningful edge. Day-of-week analysis reveals differences of 0.2% to 0.6%, with Wednesday edging out slightly for selling and Tuesday showing marginally better performance for buying. However, these daily variations lack statistical significance when compared to the dramatic monthly patterns, representing more noise than actionable alpha for systematic strategies. 3. S&P 500 SP:SPX With over 50 years of data dating back to 1971, the S&P 500 demonstrates the famous "September Effect." September averages -0.90% returns and has been negative with notable consistency, establishing statistical significance through sheer sample size. November, capturing typical year-end institutional positioning, averages +1.73% with positive performance 70% of the time. April comes in second at +1.44% average returns. The persistence of these patterns across five decades provides robust evidence of systematic seasonal effects even in highly efficient markets. Day-of-week effects in the S&P 500 are minimal, ranging from just 0.01% to 0.07%. Monday shows a slight negative drift at -0.01%, while Wednesday edges up 0.07%. These intraday variations fall well within normal variance and lack statistical significance for execution timing. For this index, monthly patterns provide the primary source of seasonal alpha. 4. Gold OANDA:XAUUSD Perhaps most compelling is gold's seasonal data spanning nearly 200 years since 1832, offering an extraordinarily large sample size for statistical validation. January shows the strongest average returns at +0.99% and has been positive 80% of the time, representing a highly reliable statistical edge. June represents the weakest period at -0.18% average returns, with October also serving as a potential entry point at just 0.05% average returns. July comes in as the second-best month at +0.79%. The consistency of these patterns across multiple centuries, world events, and monetary system changes indicates deeply embedded structural inefficiencies in market dynamics. Day-of-week patterns in gold are similarly minimal. Thursday edges out at 0.09% for optimal selling, while Sunday shows 0.01% for buying opportunities. Like the S&P 500, gold trades predominantly on monthly patterns rather than daily variations, with intraweek effects lacking statistical significance. 🟢 TL;DR 1. Bitcoin INDEX:BTCUSD : Accumulate during September weakness (-1.92%), sell into October strength (+21.59%). October has been positive 9 out of 10 years since 2015, representing a 90% positive occurrence. Day of week: Sunday dips for buying, Monday for selling. 2. Ethereum INDEX:ETHUSD : Summer pain is real. September (-10.04%) and June (-7.20%) are buying opportunities. May (+34.97%) is the monster month historically, positive 7 out of 10 years (70% positive frequency). Day of week: Tuesday buying, Wednesday selling, but minimal statistical significance. 3. S&P 500 SP:SPX : The September Effect demonstrates statistical significance (-0.90% average over 50+ years). November (+1.73%) captures the year-end rally with 70% positive occurrence. Day of week effects are negligible (0.01-0.07%) and lack statistical significance. 4. Gold OANDA:XAUUSD : January strength (+0.99%, 80% positive frequency) after June weakness (-0.18%). Nearly 200 years of data backing these patterns provides exceptional statistical validation. Day of week: Sunday buying, Thursday selling, but minimal differences. 🟢 Final thoughts Ultimately, seasonality analysis does not guarantee future results, but it provides a framework for probabilistic decision-making with quantifiable statistical edges. Rather than attempting to time markets based on sentiment or short-term price movements, systematic traders and investors can align decisions with periods that have historically shown consistent strength or weakness with statistical significance. This approach is particularly valuable for planning entry and exit points, portfolio rebalancing, and managing position sizing within a rules-based framework. Notably, while day-of-week patterns exist in some assets, monthly seasonality tends to provide more significant and statistically reliable edges across most markets. The data suggests that seasonal patterns persist even in highly efficient markets, driven by recurring institutional behaviors, tax considerations, and structural market dynamics that create exploitable inefficiencies. Market seasonality should be viewed as one analytical tool within a comprehensive quantitative framework, not a guarantee of performance, but a method to incorporate historical probabilities and statistical edges into systematic investment decisions. This isn't about perfect timing either. It's about leveraging statistical edges based on historical probabilities instead of emotion. You'll still be wrong sometimes, but less often when operating with decades of data and quantifiable patterns rather than sentiment alone. 👉 Try the Seasonality Heatmap indicator yourself on TradingView to explore these patterns across different assets and timeframes. *This analysis is for educational purposes only and is not financial advice. Past performance does not guarantee future results. Always do your own research and consult with a qualified financial advisor before making investment decisions.
INDEX:BTCUSDEducation
by QuantAlgo
BTC Bitcoin & Crypto Selloff Alert: Is a Second Wave Imminent?If you haven`t bought BTC before the rally: The cryptocurrency market is still reeling from the brutal sell-off on Friday, October 10, 2025, triggered by U.S. President Donald Trump's announcement of 100% tariffs on Chinese imports. Bitcoin plummeted below $105,000, Ethereum dropped over 16% to under $3,700, and the broader market saw liquidations exceeding $19 billion—the largest single-day wipeout in crypto history, nine times bigger than February 2025's crash. While Monday and Tuesday brought some recovery, with Bitcoin climbing back above $115,000 and the total market cap topping $4 trillion, underlying issues like inflated trading volumes, massive altcoin losses, and a prominent whale re-entering a short position suggest this might just be a dead-cat bounce. This article explores why a second leg down could be imminent, incorporating technical analysis for TradingView users eyeing volatility plays. The Friday Flash Crash: A Perfect Storm Amplified by Fake Volumes The October 10 crash unfolded rapidly, with Bitcoin shedding nearly 10% in hours and over $200 billion erased from the market cap. But what made the drop so severe, especially for altcoins? A key culprit: fake trading volumes driven by wash trading. Wash trading, where traders buy and sell the same asset to themselves to inflate volumes, creates the illusion of liquidity and interest without real economic activity. Estimates suggest that up to 87-88% of crypto volumes are fake, often used in pump-and-dump schemes or to lure retail investors. In thin markets like crypto, these artificial volumes mask true liquidity. When real selling pressure hit—amplified by leveraged positions and the tariff news—exchanges like Binance saw cascading liquidations due to flawed margin systems. This "flash crash" wiped out $600–900 million in longs alone, but the fake volumes meant the market couldn't absorb the shock, leading to exaggerated drops. Analysts note that such illusions persist in 2025, with AI tools now exposing them, but regulators lag behind. If volumes remain overstated, any renewed catalyst could trigger another liquidity vacuum, setting up a second leg down. Altcoin Bloodbath: 50–90% Drops Expose Vulnerabilities While Bitcoin and Ethereum recovered somewhat, altcoins bore the brunt of the carnage. An index tracking altcoins (excluding BTC, ETH, and stablecoins) cratered about 33% in just 25 minutes, with some tokens plunging 50–90%. Tokens like Solana, XRP, and Dogecoin lost 20–60%, but lesser-known altcoins saw even steeper declines, highlighting the sector's over-leveraged and illiquid nature. This disproportionate pain stems from altcoins' reliance on hype and speculative inflows, often propped up by those same fake volumes. In a risk-off environment, capital flees to safer assets like Bitcoin, whose dominance spiked during the crash. Altseason indicators are now cooling (76–78 previously). Bitcoin dominance dropped below 59% post-crash. If earnings season in traditional markets reveals economic weakness, spilling over to crypto, altcoins could lead the next sell-off wave, potentially dropping another 20–50% if support levels break. The Whale Factor: $192M Profit, Then Re-Entry Signals Bearish Conviction Adding fuel to the fire is a mysterious crypto whale who timed the crash impeccably. Just 30 minutes before Trump's tariff announcement, this entity opened a massive short position on Bitcoin, pocketing $192 million in profits as the market tanked. Speculation abounds: Was it insider knowledge, or just savvy trading? Either way, the whale didn't stop there—they've re-entered with another short, increasing their position to $340 million, and even opened a fresh $163 million bearish bet. Other whales have followed suit, including: A Satoshi-era entity shorting $1.1 billion before the news An OG whale opening a $392 million short These moves suggest high conviction in further declines, possibly tied to ongoing trade tensions or macroeconomic risks. In a market where whales can move prices, this re-entry could catalyze the second leg, especially if it triggers more liquidations. My price target for Bitcoin is $95K
Short
by TopgOptions
Smart Money Long SetupKey Concepts: CHoCH (Change of Character) and BOS (Break of Structure) confirm shifts in market direction and continuation. Liquidity Grab: Price swept a strong low and tapped into a major support zone, inducing sellers before reversing. Entry Point: After the liquidity sweep, bullish reaction and structure formed a favorable entry (marked on chart). Target: The target is set at a prior weak high, aligning with imbalance and liquidity resting above. 🔍 Confluences: Price broke multiple minor structures (BOS) after the CHoCH. Dynamic support from the Keltner/Bollinger-like bands shows mean reversion potential. Strong rejection wick and consolidation at support confirm buyer interest. Trade Idea: Entry: ~0.80118 Target: ~0.80810 Stop Loss: Just below the strong low ~0.80010 or adjusted as per risk appetite. Risk-Reward: Favorable R:R setup, especially if price reclaims mid-range levels above 0.80443.
Long
by Hamza_Gold
2
ETH : analysis of the main timeframes!Hi😊 Today we'll be analyzing the ETH USDT trading pair. We'll start with the higher timeframe and move on to the lower one. Let's start with the daily chart: after breaking the high, the price continued to trade sideways. The IMB was tested from below, which responded and sent the price into a correction toward the downward wave. After the local liquidity drain, the price showed bearish aggression. Nearest support zone $4 332 - $4 245 1h timeframe: the price is showing a corrective movement, and has identified zones above from which a reaction to a further decline may occur 15m timeframe: there is a buildup of local liquidity for asset growth and price delivery to nearby problem areas Leave your reactions and comments if you like these short reviews
by mari_trade
NGAS 1D - bulls waiting for the green lightOn the daily chart, Natural Gas has broken out of a falling wedge, but price remains below the MA200, while EMA still hovers above it - a mixed signal showing short-term hesitation within a longer-term downtrend. The 3.10–3.20 buy zone remains key - that’s where the retest area aligns with short-term support. If buyers can reclaim the EMA and push above the MA200, the next upside targets are 4.14 and then 4.92. Volume on the breakout supports growing bullish interest, while fundamentals - like rising seasonal demand - may soon add more fuel to the move. Tactically , watch how price behaves near MA200. Once EMA flips back on top, momentum could accelerate fast. Until then, the market’s like a gas burner waiting for that click - ignition pending
FX:NGASLong
by TotoshkaTrades
1
Bullish Thesis: Why AMD Stock Could Soar by Year-End 2025If you haven`t bought AMD before the previous rally: What to consider now: 1. AI Tailwinds Are Accelerating AMD is finally gaining serious traction in the AI GPU race. Its MI300X accelerator chips are being adopted by big names like Microsoft, Meta, and Oracle for data center AI workloads. While NVIDIA is still dominant, AMD is expected to grab 10–20% of the AI GPU market share by 2025, according to industry estimates. That’s a multibillion-dollar opportunity. The MI300X already passed $1B in revenue within its first quarters. AI server TAM (total addressable market) is expected to grow to $400B by 2027 — AMD is positioning itself aggressively to carve out its slice. 2. Valuation Looks Reasonable vs Peers AMD trades at a forward P/E around 40, significantly below Nvidia (which trades over 60x) despite similar growth projections for the next 2 years. Revenue expected to grow over 15–20% YoY in 2025. Gross margins expanding as high-performance chips dominate the mix. 3. Diversified Growth: Beyond AI Gaming segment (PlayStation 5 and Xbox Series X chips) remains strong. Embedded segment from Xilinx acquisition continues to generate solid cash flow. Client CPU business is rebounding as the PC market stabilizes. 4. Strong Management and Execution CEO Lisa Su is widely respected for turning AMD around and guiding the company through major innovations and acquisitions (Xilinx, Pensando). Execution has remained consistent, especially in delivering cutting-edge performance-per-watt chips. Disclosure: I am part of Trade Nation's Influencer program and receive a monthly fee for using their TradingView charts in my analysis.
TRADENATION:AMDLong
by TopgOptions