菜单
Runs backtests on trading strategies using historical market data. Calculates performance metrics including Sharpe ratio, maximum drawdown, win rate, total return, and generates equity curves. Trigger when the user requests backtesting, strategy simulation, or performance evaluation.
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
| name | backtest-strategy |
| description | Runs backtests on trading strategies using historical market data. Calculates performance metrics including Sharpe ratio, maximum drawdown, win rate, total return, and generates equity curves. Trigger when the user requests backtesting, strategy simulation, or performance evaluation. |
| metadata | {"hermes":{"tags":["backtest","strategy","performance","trading"],"category":"trading","requires_toolsets":["terminal"]}} |
Runs backtests on trading strategies and provides performance analysis.
tradinglearn/backtest/backtester.py — Backtester class: run_backtest(data, strategy_class, strategy_params), get_performance(), plot_results()tradinglearn/strategies/macd_strategy.py — MACDStrategy with generate_signals(data) → positionstradinglearn/pytdx2/backtest.py — BacktestEngine + BacktestConfig + BaseStrategytradinglearn/utils/parameter_optimizer.py — ParameterOptimizer.optimize_macd_parameters()DataLoader → Strategy signals → Portfolio tracking → Metrics calculation → Report
data_fetcher.fetch_stock_data(ticker, start, end)MACDStrategy(fast, slow, signal))run_backtest(data, MACDStrategy, params) iterates bars, tracks positionsget_performance() returns Sharpe, max drawdown, win rate, total return, CAGRplot_results() shows price vs portfolio value overlayfrom backtest.backtester import Backtester
from strategies.macd_strategy import MACDStrategy
from utils.data_fetcher import fetch_stock_data
data = fetch_stock_data("000001", start_date="2024-01-01", end_date="2025-01-01")
bt = Backtester(initial_capital=100000.0, transaction_cost=0.001)
bt.run_backtest(data, MACDStrategy, {"fast_period": 12, "slow_period": 26, "signal_period": 9})
bt.generate_detailed_report()
bt.plot_results()
t uses only data up to bar t燕赤霞短线打板体系 — 一进二/二进三/反包/弱转强/竞价/分歧转一致/涨停金凤凰。基于抖音口播稿蒸馏，适用于A股短线打板、龙头战法、情绪周期判断。
Generates structured trading performance reports. Includes performance metrics summary, equity curve, trade-by-trade log, monthly returns heatmap, and comparative benchmark analysis. Trigger when the user wants a summary or report of trading results.
Creates financial charts and visualizations. Supports candlestick charts, equity curves, indicator overlays, correlation heatmaps, and distribution plots. Trigger when the user wants to visualize financial data or trading results.
Cleans and preprocesses financial time series data. Handles missing values, outliers, corporate actions (splits, dividends), date alignment, and data quality checks. Trigger when the user has messy financial data or needs to prepare data for analysis.
Reviews code with awareness of financial domain pitfalls: lookahead bias, data leakage, proper decimal handling, survivorship bias, overfitting, and correct backtesting methodology. Trigger when the user requests code review of trading or financial code.
Generates documentation for trading and financial code: strategy documentation, API references, usage examples, README files, and inline code comments. Trigger when the user wants to document trading code or generate project documentation.