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Backtest Strategy

skill-qunyou-agent-finance-skills-backtest-strategy · by qunyou-agent

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.

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Install

$ agentstack add skill-qunyou-agent-finance-skills-backtest-strategy

✓ scanned · ✓ verified — works with Claude Code, Cursor, and more.

Security review

✓ Passed

No issues found. Passed automated security review. · v0.1.0 How review works →

  • ✓ Prompt-injection patterns
  • ✓ Secret / credential exfiltration
  • ✓ Dangerous shell & filesystem operations
  • ✓ Untrusted network calls
  • ✓ Known-malicious package signatures

What it can access

  • ✓ Network access No
  • ✓ Filesystem access No
  • ✓ Shell / process execution No
  • ✓ Environment & secrets No
  • ✓ Dynamic code execution No

From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.

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About

Backtest Strategy

Runs backtests on trading strategies and provides performance analysis.

Real Code Reference

  • 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) → positions
  • tradinglearn/pytdx2/backtest.py — BacktestEngine + BacktestConfig + BaseStrategy
  • tradinglearn/utils/parameter_optimizer.py — ParameterOptimizer.optimize_macd_parameters()

Architecture

DataLoader → Strategy signals → Portfolio tracking → Metrics calculation → Report
  1. DataLoader — fetch historical K-line via data_fetcher.fetch_stock_data(ticker, start, end)
  2. Strategy — generate buy/sell signals per bar (MACDStrategy(fast, slow, signal))
  3. Backtester — run_backtest(data, MACDStrategy, params) iterates bars, tracks positions
  4. Metrics — get_performance() returns Sharpe, max drawdown, win rate, total return, CAGR
  5. Plot — plot_results() shows price vs portfolio value overlay

Usage

from 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()

Key Checks

  • No lookahead bias — signal at bar t uses only data up to bar t
  • Out-of-sample validation separate from parameter optimization
  • Account for transaction costs (commission + slippage)
  • Handle corporate actions (splits, dividends) in price data

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

Install and usage instructions live in the source repository linked above.

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Versions

  • v0.1.0 Imported from the upstream source.

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