MonteWalk

""" Educational resources and cheat sheets for algorithmic trading. """ def get_algo_cheat_sheet() -> str: """ Returns a cheat sheet of common algorithmic trading concepts and formulas. """ return """ # 📈 Algorithmic Trading Cheat Sheet ## Key Metrics - **Sharpe Ratio**: (Rp - Rf) / σp - *Measure of risk-adjusted return. >1 is good, >2 is excellent.* - **Sortino Ratio**: (Rp - Rf) / σd - *Like Sharpe, but only penalizes downside volatility.* - **Maximum Drawdown (MDD)**: (Trough Value - Peak Value) / Peak Value - *Worst possible loss from a peak to a trough.* - **CAGR**: (Ending Value / Beginning Value)^(1/n) - 1 - *Compound Annual Growth Rate.* ## Common Strategies 1. **Mean Reversion**: Betting that prices will revert to the mean (e.g., Bollinger Bands, RSI). 2. **Momentum/Trend Following**: Betting that trends will continue (e.g., Moving Average Crossover, MACD). 3. **Statistical Arbitrage**: Exploiting pricing inefficiencies between correlated assets (e.g., Pairs Trading). 4. **Sentiment Analysis**: Using news/social media sentiment to predict moves (e.g., FinBERT). ## Risk Management Rules - **Position Sizing**: Never risk more than 1-2% of capital on a single trade. - **Stop Loss**: Always have an exit plan for losing trades. - **Diversification**: Don't put all eggs in one basket (check correlations). - **Kelly Criterion**: f* = (bp - q) / b - *Optimal bet size (use fractional Kelly for safety).* """ def get_classic_papers() -> str: """ Returns a list of must-read academic papers in quantitative finance. """ return """ # 📚 Classic Quantitative Finance Papers ## Foundations 1. **"Portfolio Selection"** by Harry Markowitz (1952) - *Introduced Modern Portfolio Theory (MPT) and the efficient frontier.* 2. **"Capital Asset Prices: A Theory of Market Equilibrium"** by William Sharpe (1964) - *Introduced the CAPM model and Beta.* 3. **"The Pricing of Options and Corporate Liabilities"** by Black & Scholes (1973) - *The foundation of options pricing.* ## Statistical Arbitrage & Alpha 4. **"Statistical Arbitrage in the U.S. Equities Market"** by Avellaneda & Lee (2008) - *A rigorous framework for pairs trading and mean reversion.* 5. **"101 Formulaic Alphas"** by Kakushadze (2016) - *A treasure trove of alpha factors for quantitative trading.* ## Machine Learning in Finance 6. **"Deep Learning for Limit Order Books"** by Zhang et al. (2019) - *Applying CNNs/LSTMs to high-frequency data.* 7. **"Financial Sentiment Analysis with Pre-trained Language Models"** (FinBERT) - *Using BERT for financial text classification.* """ def get_risk_checklist() -> str: """ Returns a pre-flight checklist for risk management before deploying strategies. """ return """ # ✅ Pre-Flight Risk Checklist ## 1. Strategy Validation - [ ] **Backtest Period**: Does it cover different market regimes (bull, bear, sideways)? - [ ] **Overfitting**: Did you optimize parameters too much? (Look for parameter stability). - [ ] **Out-of-Sample**: Did it perform well on data it hasn't seen before? - [ ] **Transaction Costs**: Did you include slippage and commissions? ## 2. Portfolio Health - [ ] **Exposure**: Is leverage within safe limits (< 2x)? - [ ] **Concentration**: Is any single position > 10% of equity? - [ ] **Correlation**: Are all assets highly correlated? (e.g., all Tech stocks). - [ ] **Liquidity**: Can you exit positions easily without moving the price? ## 3. System & Infrastructure - [ ] **Data Quality**: Is your data clean and adjusted for splits/dividends? - [ ] **Execution**: Is the order execution logic robust (retries, error handling)? - [ ] **Fail-safes**: Is there a "Kill Switch" to stop trading if losses exceed X%? - [ ] **Logging**: Is every action logged for audit? """