Skill: rmbell09-lang/trading-strategies (version 1.0.0)

--- name: trading description: Comprehensive trading knowledge base covering fundamentals, technicals, strategies, backtesting, and risk management. Use when building trading apps or evaluating strategies. --- # Trading Skill — Complete Reference ## Purpose Comprehensive trading knowledge base covering fundamentals, technicals, strategies, backtesting, and risk management. Use when building trading apps, evaluating strategies, or making trading decisions. --- ## 1. TRADING STYLES ### Scalping - Hold: seconds to minutes - Goal: profit from tiny price movements - Pros: many opportunities, reduced exposure to big moves - Cons: high transaction costs, stressful, tiny profit per trade - Best for: highly liquid markets with tight spreads ### Day Trading - Hold: minutes to hours (close all by market close) - Goal: profit from intraday price movements - Pros: no overnight risk, high profit potential per trade - Cons: high risk, emotional pressure, costs add up - Best for: volatile stocks with clear intraday patterns ### Swing Trading - Hold: days to months - Goal: catch short-to-intermediate moves - Pros: lower costs, more analysis time, less stressful - Cons: overnight risk, may miss long-term moves - Best for: trending markets with pullbacks ### Position Trading - Hold: months to years - Goal: profit from major long-term trends - Pros: lowest costs, highest profit potential, most flexibility - Cons: capital tied up, exposed to macro events - Best for: fundamentally strong companies in sector uptrends --- ## 2. FUNDAMENTAL ANALYSIS ### Key Metrics **P/E Ratio (Price-to-Earnings)** - Formula: `Share Price / Earnings Per Share` - S&P 500 average: ~30 (as of late 2025) - Low (<15): potentially undervalued or troubled - High (>30): potentially overvalued or high growth expected - Compare WITHIN same industry only - Forward P/E uses projected earnings; Trailing P/E uses last 12 months **P/B Ratio (Price-to-Book)** - Formula: `Share Price / Book Value Per Share` - Book Value = total assets - intangible assets - liabilities - P/B < 1 = trading below asset value (potential bargain) - Most useful for capital-heavy industries (banks, manufacturing) **Debt-to-Equity Ratio** - Formula: `Total Liabilities / Shareholders' Equity` - High = heavy debt reliance (risky in downturns) - Compare within industry — some sectors carry more debt naturally **Revenue Growth Rate** - Year-over-year revenue increase - Consistent > spiky - Accelerating growth rate = strongest signal **Free Cash Flow (FCF)** - Cash generated after capital expenditures - Positive FCF = real cash generation, not accounting tricks - FCF Yield = FCF / Market Cap (higher = better value) **EPS Growth (Earnings Per Share)** - Consistent EPS growth over 3-5 years = strong signal - Check quality: operations vs one-time events **Return on Equity (ROE)** - Formula: `Net Income / Shareholders' Equity` - ROE > 15% = generally good management efficiency **Dividend Yield & Payout Ratio** - Yield = Annual Dividend / Share Price - Payout = Dividends / Net Income - Payout > 80% may be unsustainable --- ## 3. TECHNICAL INDICATORS ### RSI (Relative Strength Index) - **Formula:** `RSI = 100 - (100 / (1 + (Avg Gain / Avg Loss)))` over 14 periods - Scale: 0-100 - **>70 = Overbought** (potential sell) - **<30 = Oversold** (potential buy) - **Strengths:** Simple, effective in ranging markets - **Weaknesses:** Stays >70 for extended periods in strong uptrends — don't auto-sell - **RSI Divergence:** Price makes new high but RSI makes lower high = bearish (momentum weakening) - **Swing Rejection:** RSI crosses 30 upward → dips but stays above 30 → breaks prior high = bullish entry - **Best in:** Ranging/sideways markets. Combine with ADX to filter. ### MACD (Moving Average Convergence Divergence) - **MACD Line** = 12-period EMA - 26-period EMA - **Signal Line** = 9-period EMA of MACD Line - **Histogram** = MACD Line - Signal Line - **Buy:** MACD crosses ABOVE signal line - **Sell:** MACD crosses BELOW signal line - **Divergence:** MACD rising while price falling = potential reversal - **Strengths:** Good trend-following indicator - **Weaknesses:** Lagging, many false positives in sideways markets - **Best combined with ADX** — only trust MACD signals when ADX > 25 (confirming trend) ### Bollinger Bands - **Middle Band** = 20-day SMA - **Upper Band** = SMA + 2 standard deviations - **Lower Band** = SMA - 2 standard deviations - 95% of price action stays within bands - **Squeeze:** Bands narrow → low volatility → breakout imminent (direction unknown) - **Bollinger Bounce:** Price off lower band toward middle = buy; off upper toward middle = sell - **Breakout:** Price outside bands with volume = trend continuation - **Strengths:** Visual, adapts to volatility - **Weaknesses:** Secondary indicator — always confirm with RSI/MACD - Created by John Bollinger in the 1980s ### Moving Averages - **SMA (Simple):** Equal weight all periods - **EMA (Exponential):** More weight on recent prices (faster reaction) - Key periods: 5, 9, 20, 50, 100, 200 day - **Golden Cross:** 50-day crosses ABOVE 200-day = strong bullish - **Death Cross:** 50-day crosses BELOW 200-day = strong bearish - MAs act as dynamic support/resistance levels - Use longer MAs for volatile stocks to avoid false signals ### Volume - Confirms price movements - Price up + high volume = strong bullish - Price up + low volume = weak rally, likely to reverse - Price down + high volume = strong selling pressure - Price down + low volume = lack of selling conviction - 60-80% of daily volume is algorithmic ### Ichimoku Cloud (Ichimoku Kinko Hyo) - **5 Components:** - Tenkan-sen (Conversion Line): (9-period high + 9-period low) / 2 - Kijun-sen (Base Line): (26-period high + 26-period low) / 2 - Senkou Span A: (Tenkan + Kijun) / 2, plotted 26 periods ahead - Senkou Span B: (52-period high + 52-period low) / 2, plotted 26 periods ahead - Chikou Span: Current close plotted 26 periods back - **The Cloud (Kumo):** Area between Span A and Span B = support/resistance zone - **Buy:** Price above cloud, Tenkan crosses above Kijun - **Sell:** Price below cloud, Tenkan crosses below Kijun - **Strengths:** All-in-one indicator (trend, momentum, support/resistance) - **Weaknesses:** Complex visually, lagging in fast markets - **Best in:** Trending markets. Avoid in ranging/sideways. - Michael Automates created an AI-improved Ichimoku strategy that performs well ### Super Trend - **Formula:** Based on ATR (Average True Range) and a multiplier - Upper Band = (High + Low) / 2 + (Multiplier × ATR) - Lower Band = (High + Low) / 2 - (Multiplier × ATR) - **Buy:** Price crosses above Super Trend line - **Sell:** Price crosses below Super Trend line - **Strengths:** Simple, good trend-following, clear signals - **Weaknesses:** Late entries, bad in ranging markets - AI iteration took Super Trend from 44% to 3,605% P&L in Michael Automates' testing ### VWAP (Volume-Weighted Average Price) - **Formula:** `Sum(Price × Volume) / Total Volume` (intraday only) - Above VWAP = bullish intraday - Below VWAP = bearish intraday - Institutional benchmark — they buy below VWAP, sell above - Resets daily — intraday tool only --- ## 4. SIGNAL COMBINATIONS ### Strong Buy Signal (High Confidence) All of these together: - RSI < 30 (oversold) AND recovering - MACD crossing above signal line - Price bouncing off lower Bollinger Band - Volume increasing on the bounce - Price above 200-day MA (long-term uptrend intact) - Fundamentals solid (P/E reasonable, FCF positive, revenue growing) ### Moderate Buy Signal - RSI between 40-50 in an uptrend (pullback buy) - Price touching 50-day MA support - MACD histogram turning positive - Volume above average ### Trend-Following Entry - Golden Cross (50-day crosses above 200-day) - Price breaks above resistance with high volume - ADX > 25 confirming trend strength - Buy on first pullback after breakout ### Strong Sell Signal - RSI > 70 in a ranging market - Price hitting upper Bollinger Band with declining volume - MACD crossing below signal line - Price reaching prior resistance level **RULE: Never rely on a single indicator. Always combine 2-3 minimum.** --- ## 5. POSITION SIZING ### The 1% Rule Never risk more than 1% of total account on a single trade. - Account: $1,000 → max risk per trade: $10 - If stop-loss is $2 below entry → buy 5 shares max - Keeps you alive through losing streaks ### Kelly Criterion - `f = (bp - q) / b` - b = win/loss ratio, p = win probability, q = loss probability - Gives optimal position size based on historical win rate - **Use half-Kelly** for safety (most professionals do) ### Example - Win rate: 55%, average win: $200, average loss: $100 - Kelly: f = (2 × 0.55 - 0.45) / 2 = 0.325 (32.5% of account) - Half-Kelly: 16.25% — still aggressive, many pros use quarter-Kelly --- ## 6. EXIT STRATEGIES ### Stop-Loss Types - **Fixed percentage:** Sell if price drops X% (typically 5-8%) - **ATR-based:** Stop at entry minus 2× Average True Range (adjusts for volatility) - **Support-based:** Stop below key support level or moving average - **Trailing stop:** Moves up with price, locks in profits (e.g., trail by 5%) ### Take-Profit Methods - **Fixed target:** Pre-set price target (e.g., 2:1 or 3:1 risk/reward) - **Technical target:** Previous resistance level, Fibonacci extension - **Trailing:** Let winners run with trailing stop ### The 3 Hardest Sells 1. **Cutting losses** — "it'll come back" kills accounts. Honor your stops. 2. **Taking profits too early** — use trailing stops to let winners run 3. **Holding through earnings** — volatility spikes. Reduce position or hedge. --- ## 7. STRATEGY ARCHETYPES ### Mean Reversion - **Theory:** Prices revert to their historical average over time - **Entry:** Buy when RSI < 30 or price 2+ standard deviations below mean - **Exit:** Sell when RSI > 70 or price returns to mean - **Tools:** Z-scores, Bollinger Bands, RSI - **Best in:** Range-bound/sideways markets - **Weakness:** Fails badly in strong trending markets (price keeps going) - **Key stat:** Z-score above 1.5 or below -1.5 signals opportunity ### Momentum - **Theory:** Stocks that are going up tend to keep going up (and vice versa) - **Entry:** Buy stocks with strongest 3-6 month performance - **Exit:** Sell when momentum weakens (MACD crossover, RSI divergence) - **Philosophy:** "Buy high, sell higher" — opposite of value investing - **Best in:** Trending markets with clear direction - **Weakness:** Sudden reversals can be devastating - **Key tools:** Trend lines, MACD, RSI, relative strength vs index ### Moving Average Crossover - **Entry:** Buy on Golden Cross (50-day crosses above 200-day) - **Exit:** Sell on Death Cross (50-day crosses below 200-day) - **Pros:** Simple, catches major trends - **Cons:** Very lagging — you'll miss the first 10-20% of a move and give back 10-20% at the end - **Variant:** Use shorter MAs (9/21) for faster signals but more noise ### Bollinger Band Squeeze - **Entry:** When bands contract to minimum width, enter on the breakout direction with volume confirmation - **Exit:** When price reaches opposite band or bands start contracting again - **Key:** The squeeze only tells you a big move is coming — NOT the direction - **Must combine with:** Volume, MACD, or other directional indicator ### Pairs Trading - **Origin:** Morgan Stanley, mid-1980s - **How:** Find two highly correlated stocks (0.80+ correlation). When they diverge, go long the underperformer, short the outperformer - **Profit:** When they converge back to historical correlation - **Market-neutral:** Hedged against broad market moves - **Risk:** Correlation can break permanently (one company's fundamentals change) - **Example:** Coca-Cola vs Pepsi, Visa vs Mastercard ### VWAP Reversion (Intraday) - **Entry:** Buy below VWAP when overall trend is bullish - **Exit:** Sell above VWAP or at end of day - **Best for:** Day trading liquid stocks - **Why it works:** Institutional traders use VWAP as a benchmark --- ## 8. BACKTESTING ### The 4 Deadly Biases 1. **Optimization Bias (Curve Fitting):** Over-tuning to historical data. Fix: minimal parameters, out-of-sample testing 2. **Look-Ahead Bias:** Using future data accidentally. Fix: strict chronological processing 3. **Survivorship Bias:** Only testing stocks that exist today. Fix: survivorship-free datasets or recent data 4. **Psychological Tolerance Bias:** A 25% drawdown looks fine on a chart but feels devastating in real-time ### Key Metrics | Metric | Formula | Good | Great | |--------|---------|------|-------| | Sharpe Ratio | (Return - Risk-Free) / Std Dev | >1 | >2 | | Max Drawdown | Worst peak-to-trough | <20% | <10% | | Win Rate | Wins / Total Trades | >40% | >55% | | Profit Factor | Gross Profits / Gross Losses | >1.5 | >2 | | Risk/Reward | Avg Win / Avg Loss | >2:1 | >3:1 | **Expected Return per Trade:** `(Win% × Avg Win) + (Loss% × Avg Loss)` — must be positive ### Drawdown Math (Critical) - 10% loss → need 11% gain to recover - 20% loss → need 25% gain to recover - 30% loss → need 43% gain to recover - 50% loss → need 100% gain to recover - **The math gets exponentially worse.** Avoiding big losses > chasing big wins. ### Process 1. Define strategy rules with zero ambiguity 2. Get clean OHLCV data (Open, High, Low, Close, Volume) 3. Build engine (Python: backtrader, vectorbt, quantstats) 4. Split: 70% training / 30% out-of-sample validation 5. Run Monte Carlo simulation (randomize trade order) for robustness 6. Paper trade winners for 3+ months 7. Go live small — start with minimum position sizes ### Michael Automates Backtesting Workflow (Proven) His backtesting engine ($99) does this — we can replicate for free: 1. **Get historical OHLCV data** (from CCXT/Binance/Alpaca — free) 2. **AI creates Python version** of your trading strategy 3. **Run backtest locally** → get metrics 4. **Compare with TradingView numbers** to verify accuracy 5. **If numbers don't match:** Export TradingView Excel report, do trade-by-trade comparison 6. **AI can auto-fix discrepancies** by modifying the backtest engine code 7. **Critical settings:** Commission = 0.1%, timezone = UTC, date range = not full history **Auto-download data:** - CCXT pulls from Binance, Coinbase, Kraken automatically - Store in `/data/cache/` folder by asset and timeframe - Format: `BTC_USDT_1d.csv`, `ETH_USDT_4h.csv`, etc. ### Data Sources - **Free:** Yahoo Finance (yfinance), Alpha Vantage, Alpaca, Polygon.io free tier, CCXT (100+ crypto exchanges) - **Paid:** Norgate, Nasdaq Data Link (Quandl) - **Warning:** Yahoo Finance has survivorship bias - **Best for crypto:** CCXT + Binance (most history, most pairs) - **Best for stocks:** Alpaca (free, clean data, built-in paper trading) --- ## 9. RISK MANAGEMENT ### Core Rules - **1% Rule:** Never risk more than 1% of account per trade - **Daily Loss Limit:** Stop trading if down 3% in a day - **Correlation Risk:** Don't hold 5 tech stocks — one sector crash kills you - **Position Limits:** Max 5-10 open positions at once - **Cash Reserve:** Always keep 20-30% cash for opportunities ### Circuit Breakers - Hit daily loss limit → done for the day - Hit weekly loss limit (5%) → reduce position sizes 50% - Hit monthly loss limit (10%) → pause, review all strategies - 3 consecutive losses → take a break, re-evaluate ### Fees & Slippage (Often Ignored) - Commission-free doesn't mean cost-free — there's still the spread - Slippage: the difference between expected and actual execution price - High-frequency strategies amplify these costs - **Always include fees + slippage in backtests** or results are meaningless --- ## 10. COMMON MISTAKES 1. **Trading without a plan** → Write rules BEFORE trading 2. **Ignoring position sizing** → The 1% rule exists for a reason 3. **Moving stop-losses** → Set them and honor them 4. **Averaging down** → Adding to losers hoping they'll recover 5. **Overtrading** → More trades ≠ more profit (costs eat you alive) 6. **Single indicator reliance** → Always combine 2-3 indicators 7. **Not accounting for fees** → Especially in backtests 8. **Skipping paper trading** → Going live without 3+ months of validation 9. **Revenge trading** → Trying to win back losses with bigger bets 10. **Ignoring the macro** → Individual stocks don't exist in a vacuum --- ## 11. TOOLS & APIS ### Broker APIs - **Alpaca** — Commission-free, great API, paper trading built in (RECOMMENDED for us) - **Interactive Brokers** — Most comprehensive, supports everything - **TD Ameritrade/Schwab** — thinkorswim API ### Data APIs - **Alpha Vantage** — Free tier (5 calls/min), stocks + crypto + forex - **Polygon.io** — Real-time and historical, free tier - **Yahoo Finance** — Free via yfinance (unreliable, survivorship bias) - **Alpaca Market Data** — Included with account ### Python Stack - **pandas** — Data manipulation - **numpy** — Numerical computing - **ta-lib / pandas-ta** — Technical indicators - **backtrader** — Full backtesting framework - **vectorbt** — Fast vectorized backtesting - **quantstats** — Portfolio analytics - **scikit-learn** — ML pattern recognition - **alpaca-trade-api** — Broker integration ### Architecture ``` [Data Feed] → [Indicator Engine] → [Signal Generator] → [Risk Manager] → [Order Executor] ↑ | └──────────── [Backtesting Engine] ←────────────────────────┘ ↓ [Performance Analytics] ``` --- ## 12. CRYPTO-SPECIFIC STRATEGIES (From Video Research) ### Super Trend Strategy - Built-in TradingView indicator - AI can iterate: base version (44% P&L) → optimized V4 (3,605% P&L) - Process: give AI base → "improve it" → backtest → repeat overnight - Test on multiple assets to avoid overfitting ### Strategy Tournament (Evolution Method) - Create 10 different strategies with different parameters/logic - Run all 10 in parallel with small positions ($100 each) - After 1-2 weeks: cut losers, keep winners - Create variations of winners, repeat process - Natural selection for trading strategies ### Market Regime Detection - Run separate strategies for trending vs ranging markets - AI detects current regime and switches strategies automatically - Trend-following for trending markets, mean reversion for ranging ### Prediction Market Strategies (Polymarket) - **15-Minute Windows:** BTC up/down resolution every 15 minutes - **Late Entry:** Look at trend in last 3-4 minutes, enter in that direction - **Arbitrage:** When both sides cost <$1 total = guaranteed profit - **Mention Markets:** AI studies speech patterns to predict word usage - **Sports Scanner:** AI researches obscure low-volume markets for edges - **Counter-Trend AI:** Bet against AI consensus (contrarian) - **Wallet Analysis:** Copy successful wallets → AI reverse-engineers their strategy - KEY: Not dependent on bull/bear markets — works always ### Multi-Agent Trading Architecture - **Coordinator:** Delegates tasks, manages priorities, daily briefing - **Quant Scanner:** Scans 30+ coins every 15 min with CCXT, confluence scoring - **Researcher:** Daily news/intel scanning, deep dives - **Alert Agent:** Formats and delivers signals - **Security Agent:** 24hr audit for malicious code, prompt injections, bugs - Each agent needs a detailed "soul" (personality + instructions) - Agents work in parallel, not sequentially ### Crypto Exchange APIs - **Hyperliquid** — Decentralized perps, no KYC, good API - **Blofin** — Centralized, API with trading perms (disable withdrawals!) - **CCXT Library** — Open-source, supports 100+ exchanges, start read-only - **Alpaca** — Stocks (commission-free, paper trading built in) ### AI Strategy Iteration Workflow (KEY — From Michael Automates) This is the single most powerful workflow from all our research: 1. **Pick a base strategy** (e.g., Super Trend, RSI+MACD, Bollinger Squeeze) 2. **AI backtests it** → gets baseline metrics (P&L, drawdown, win rate, Sharpe) 3. **Tell AI "improve this"** → it modifies parameters, adds filters, changes logic 4. **AI creates V2, V3, V4...** each iteration potentially better 5. **Compare AI numbers with TradingView** to verify accuracy 6. **LET IT RUN OVERNIGHT** — wake up to results 7. **Score across multiple assets** (BTC, ETH, SOL, SPY, AAPL) to avoid overfitting 8. **Only keep strategies that work on 3+ different assets** 9. Repeat until you have top 3 strategies → paper trade those **Michael's results:** Super Trend went from 44% P&L → 3,605% P&L through this process. **Critical anti-overfitting rules:** - Test on multiple assets, not just one - Use walk-forward validation (train on years 1-3, test on years 4-5) - If a strategy only works on one asset, it's overfit — discard it - Commission must be set to 0.1% (not 0%) - TradingView timezone must be UTC for number matching ### Strategy Tournament (From Alex Carter) 1. Create 10 different strategies (vary parameters, timeframes, indicators) 2. Run ALL 10 in parallel with small positions ($100 each) 3. Track for 1-2 weeks 4. Kill bottom 5 performers 5. Create mutations/variations of top 5 6. Run new tournament 7. Repeat — "evolution for trading strategies" 8. After 3-4 rounds, surviving strategies are battle-tested ### Counter-Trend Against AI Crowd (From Coin Bureau) - Most AI trading bots use similar data and strategies - When AI crowd consensus is heavy one direction, bet the opposite - Works because: AI herding creates overcrowded trades that reverse - Requires: monitoring what popular AI strategies are doing - Best for: short-term contrarian plays in crypto and prediction markets ### Divergence Trading (From Coin Bureau) - Use TBT Divergence indicator on 15-minute timeframe - Contrarian entry when price makes new high/low but indicator doesn't confirm - Specifically: price up but RSI/MACD diverging = weakening momentum - Enter against the trend on confirmed divergence - Best for: short-term crypto and Polymarket 15-min windows ### Critical Crypto Trading Rules - NEVER enable withdrawal permissions on API keys - Whitelist your IP on centralized exchanges - Store keys in .env files, never hardcoded - Use testnet/paper trading first - Rotate API keys every few months - Run on dedicated machine (not personal computer) - Daily security audit via cron job --- ## 13. POLYMARKET (PREDICTION MARKETS) ### How It Works - Binary markets: buy Yes/No shares in real-world outcomes - Correct = $1.00 payout, wrong = $0.00 - Price = probability (Yes at $0.70 = market thinks 70% likely) - Runs on Polygon (Ethereum L2), uses USDC.e - Python SDK: `py-clob-client` - Public data API (no auth): `gamma-api.polymarket.com` ### Strategy Types 1. **Arbitrage** — Yes + No < $1.00 = free money (rare, need speed) 2. **Late Entry** — 15-min BTC windows, enter last 3-4 min when trend is clear 3. **Mention Markets** — AI analyzes speech patterns to predict word usage 4. **Obscure Sports** — Low-volume markets with inefficient pricing 5. **Wallet Analysis** — Reverse-engineer top performers' strategies 6. **Market Making** — Provide liquidity, profit from spread (advanced) ### Why AI Has an Edge - Can process more data than any human (speeches, news, stats) - 24/7 monitoring of all markets simultaneously - Niche markets have little competition = more mispricing - Uncorrelated to stock/crypto markets — works in any cycle ### Market Making (Advanced) - Place both buy and sell orders, profit from the spread - Requires: understanding inventory risk, dynamic spread adjustment - Need algorithm to adjust quotes based on position, volatility, flow - Complex math but AI handles research and implementation - Start with wide spreads in low-volume markets, tighten as you learn - Risk: getting stuck holding losing positions if market moves fast ### TBO Cloud Strategy (Coin Bureau — Proprietary) - Trending Breakout indicator on 4-hour timeframe - Advanced moving average strategy — details not public - Combined with TBT Divergence indicator for entry/exit - We can build similar: EMA cloud (9/21/55 EMAs) with breakout confirmation - Key concept: cloud acts as dynamic support/resistance, breakout above = long ### Key Insight Build scanner first (read-only, free). Paper trade. Then go live small. --- ## 14. CRYPTO vs STOCK STRATEGIES — WHAT TRANSFERS ### Strategies That Work in BOTH Markets | Strategy | Stocks | Crypto | Notes | |----------|--------|--------|-------| | Trend Following | ✅ | ✅ | Works in any market with trends | | Mean Reversion | ✅ | ✅ | Better in stocks (more mean-reverting) | | RSI/MACD Signals | ✅ | ✅ | Same indicators, different parameters | | Bollinger Bands | ✅ | ✅ | Adjust for volatility differences | | Pairs Trading | ✅ | ✅ | Stocks: KO/PEP. Crypto: BTC/ETH | | Moving Average Crossover | ✅ | ✅ | Universal trend indicator | | Ichimoku Cloud | ✅ | ✅ | Works anywhere with enough volume | | Strategy Tournament | ✅ | ✅ | Run 10 strategies, evolve winners | | AI Iteration | ✅ | ✅ | Give AI base strategy, let it improve | ### Key Differences | Factor | Stocks | Crypto | |--------|--------|--------| | Hours | 9:30 AM - 4 PM ET | 24/7/365 | | Volatility | Lower (1-3% daily avg) | Higher (5-10%+ daily avg) | | Leverage | 2x (margin), options for more | 10-100x available | | Regulation | Heavy (SEC, FINRA) | Light (evolving) | | Data Quality | Excellent (decades) | Good (5-10 years) | | Fees | Commission-free (Alpaca) | 0.1% typical | | Short Selling | Restricted (uptick rule) | Easy (perps) | | Pattern Day Trading | $25K minimum (PDT rule) | No restriction | | Market Manipulation | Illegal, enforced | Common, less enforcement | ### What Needs Adjusting for Stocks - **Wider MA periods** — crypto is faster, use longer MAs for stocks to filter noise - **Lower leverage** — 2x max for stocks vs 5-10x crypto - **Market hours** — need pre/after-hours data handling, overnight gaps - **PDT Rule** — if under $25K, limited to 3 day trades per 5 business days - **Earnings events** — stocks have scheduled volatility events quarterly - **Sector correlation** — stocks in same sector move together more than crypto - **Volume patterns** — stocks have predictable volume curves (open, close spikes) ### Bottom Line **Yes, the core strategies transfer to stocks.** The math is the same. What changes: 1. Parameters (slower timeframes for stocks) 2. Risk settings (lower leverage, wider stops) 3. Schedule (market hours vs 24/7) 4. Starting capital ($25K for day trading stocks due to PDT rule, or use swing trading to avoid it) **Best starting point for stocks:** Alpaca (free, commission-free, paper trading built in, great API). Same strategies, just tune the parameters. --- ## 15. MULTI-AGENT TRADING DESK (From Coin Bureau) ### Architecture Each agent gets a detailed "soul" (personality + priorities + instructions). Without a soul, agents produce generic output. ### Agent Roles 1. **Coordinator (Betty)** — Manager agent. Delegates tasks, manages priorities, monitors agent health, delivers daily briefing. Never does the work itself. 2. **Quant Scanner** — Scans 30+ coins every 15 minutes via cron job. Uses CCXT for OHLCV data. Runs indicators (RSI, MACD, Bollinger, etc). Produces confluence score for each signal. Rules: never places trades without operator approval. 3. **Researcher** — Runs daily. Scans news, X/Twitter, market updates. Produces morning research brief. Focused on narratives, sectors, new listings. 4. **Alert Agent** — Formats signals from quant scanner into actionable alerts. Pushes to Discord channels. Includes: asset, timeframe, direction, confidence score, entry/exit suggestions. 5. **Security Agent (Radar)** — 24hr security audit. Checks for malicious code, prompt injections, bugs. Quality assurance on all strategy code before deployment. Non-negotiable. 6. **Backtesting Agent** — Validates strategies against historical data before deployment. 7. **Trading Agent** — Executes trades on exchange APIs. Only activated after paper trading period. ### Notification Pipeline (Discord Recommended) - Private Discord server with bot admin privileges - Bot creates channels per category (signals, research, alerts, P&L) - Curated alerts — only coins/markets you care about - Much better than Telegram (categories, threads, searchable) ### Key Insight From Coin Bureau "Your output is only as good as your input." Generic prompts = generic agents. Spend time writing detailed soul descriptions for each agent. Include: - Exact responsibilities - What data to use - What format to output - What rules to follow - What to never do ### Cost Reality - Claude Max ($200/mo) — unlimited for heavy agent usage - API credits alternative: $1,000-10,000/mo depending on volume - Ollama (free) — can run specific tasks locally but less smart - Strategy: use Claude for complex work, Ollama for routine scans --- ## 16. OUR APPROACH ### Phase 1: Foundation - Set up Alpaca paper trading (free) - Build data pipeline for historical OHLCV data - Implement core indicators: RSI, MACD, Bollinger, MAs, Volume - Build backtesting engine with walk-forward validation ### Phase 2: Strategy Development - Backtest all 6 strategy archetypes against 5-10 years of data - Find highest Sharpe ratio + lowest max drawdown combo - Monte Carlo simulations for robustness - Paper trade top 2-3 strategies simultaneously ### Phase 3: Live (After 3+ Months Paper) - Minimum position sizes - AI-driven condition analysis (use Claude to interpret market context) - Full risk management: 1% rule, daily limits, circuit breakers - Dashboard monitoring all positions, signals, performance ### Key Principles 1. Paper trade until proven over 3+ months 2. Position sizing > entry signals 3. Risk management is the ONLY thing that keeps you in the game 4. No single indicator is reliable alone 5. Past performance ≠ future results 6. Start with stocks, not crypto (more data, less manipulation) 7. Account for fees, slippage, and taxes ALWAYS


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)