backtesting-trading-strategies

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Tool: SKILL.md Description: --- name: backtesting-trading-strategies description: | Backtest crypto and traditional trading strategies against historical data.

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Tool: config/settings.yaml Description: # Backtesting Configuration # Copy to settings.yaml and customize # Data source settings data: provider: yfinance # Options: yfinance, coingecko cache_dir: ./data # Where to cache downloaded data default_interval: 1d # Default bar interval # Backtest execution settings backtest: default_capital: 10000 # Starting capital in USD commission: 0.001 # Commission per trade (0.1%) slippage: 0.0005 # Slippage per trade (0.05%) # Risk manag

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Tool: references/errors.md Description: # Error Handling Reference ## Data Fetching Errors ### No Data Returned ``` Error: No data returned for {symbol} ``` **Causes:** - Invalid symbol format (use `BTC-USD` not `BTC/USD`) - Symbol not available on data provider - Date range has no trading data **Solutions:** ```bash # Check valid symbol format for Yahoo Finance python -c "import yfinance as yf; print(yf.Ticker('BTC-USD').info.get('symbol'))" # Try CoinGecko for crypto python scripts/fetch_data.

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Tool: references/examples.md Description: # Backtesting Examples ## Example 1: Basic SMA Crossover Backtest Test a simple moving average crossover strategy on Bitcoin: ```bash python scripts/backtest.py \ --strategy sma_crossover \ --symbol BTC-USD \ --period 1y \ --capital 10000 \ --params '{"fast_period": 20, "slow_period": 50}' ``` **Expected Output:** ``` ╔══════════════════════════════════════════════════════════════════════╗ ║ BACKTEST RESULTS: SMA_CROSSOVER

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Tool: references/implementation.md Description: # Implementation Guide ## Overview This guide covers implementing and extending the backtesting system.

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Tool: scripts/backtest.py Description: #!/usr/bin/env python3 """ Main Backtesting Engine Run trading strategy backtests with performance analysis.

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Tool: scripts/fetch_data.py Description: #!/usr/bin/env python3 """ Historical Data Fetcher Fetch and cache price data from various sources.

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Tool: scripts/metrics.py [1/2] Description: #!/usr/bin/env python3 """ Performance and Risk Metrics for Backtesting """ import numpy as np import pandas as pd from typing import List, Dict, Any from dataclasses import dataclass, field @dataclass class Trade: """Represents a completed trade.""" entry_time: pd.Timestamp exit_time: pd.Timestamp entry_price: float exit_price: float direction: str # "long" or "short" size: float pnl: float = 0.0 pnl_pct: float = 0.0 duration: pd.Timedelta = None def

Tool: scripts/metrics.py [2/2]

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Tool: scripts/optimize.py Description: #!/usr/bin/env python3 """ Strategy Parameter Optimizer Grid search and optimization for trading strategy parameters.

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Tool: scripts/strategies.py [1/2] Description: #!/usr/bin/env python3 """ Trading Strategy Definitions Each strategy implements generate_signals() returning entry/exit signals.

Tool: scripts/strategies.py [2/2] Description: Strategy registry STRATEGIES = { "sma_crossover": SMAcrossover(), "ema_crossover": EMAcrossover(), "rsi_reversal": RSIreversal(), "macd": MACD(), "bollinger_bands": BollingerBands(), "breakout": Breakout(), "mean_reversion": MeanReversion(), "momentum": Momentum(), } def get_strategy(name: str) -> Strategy: """Get strategy by name.""" if name not in STRATEGIES: raise ValueError(f"Unknown strategy: {name}.