Backtest Strategy
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.
Install
$ agentstack add skill-qunyou-agent-finance-skills-backtest-strategy ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo 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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Links to your public security report.[](https://agentstack.voostack.com/security/report/skill-qunyou-agent-finance-skills-backtest-strategy) Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
Backtest Strategy
Runs backtests on trading strategies and provides performance analysis.
Real Code Reference
tradinglearn/backtest/backtester.py—Backtesterclass:run_backtest(data, strategy_class, strategy_params),get_performance(),plot_results()tradinglearn/strategies/macd_strategy.py—MACDStrategywithgenerate_signals(data)→ positionstradinglearn/pytdx2/backtest.py—BacktestEngine+BacktestConfig+BaseStrategytradinglearn/utils/parameter_optimizer.py—ParameterOptimizer.optimize_macd_parameters()
Architecture
DataLoader → Strategy signals → Portfolio tracking → Metrics calculation → Report- DataLoader — fetch historical K-line via
data_fetcher.fetch_stock_data(ticker, start, end) - Strategy — generate buy/sell signals per bar (
MACDStrategy(fast, slow, signal)) - Backtester —
run_backtest(data, MACDStrategy, params)iterates bars, tracks positions - Metrics —
get_performance()returns Sharpe, max drawdown, win rate, total return, CAGR - 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
tuses only data up to bart - 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.
- Author: qunyou-agent
- Source: qunyou-agent/finance-skills
- License: MIT
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.