backtesting-trading-strategies

Q: How do I install backtesting-trading-strategies?

Run npx skills add https://github.com/jeremylongshore/tons-of-skills-marketplace --skill backtesting-trading-strategies in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/backtesting-trading-strategies/ (personal) or .claude/skills/backtesting-trading-strategies/ (project).

by jeremylongshoreBusiness & Finance

Backtest crypto and traditional trading strategies against historical data. Calculates performance metrics (Sharpe, Sortino, max drawdown), generates equity curves, and optimizes strategy parameters. Use when user wants to test a trading strategy, validate signals, or compare approaches. Trigger with phrases like "backtest strategy", "test trading strategy", "historical performance", "simulate trades", "optimize parameters", or "validate signals".

Installs: 4.7K
Last 24h: —
Trending rank: —
Security: Pass

Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/jeremylongshore/tons-of-skills-marketplace --skill backtesting-trading-strategies

01
Install
Run the command in your project. The CLI asks which agents to install for.
02
Describe the task
Ask your agent for something this skill covers. It loads backtesting-trading-strategies automatically when relevant.
03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.

Security audits

Independent scans of the skill's instructions and bundled files.

Gen Agent Trust Hub PassSAFE ·The skill is a legitimate trading strategy backtesting framework. It performs historical data analysis and parameter optimization without any malicious behavior. It interacts with well-known financial data providers and standard Python libraries.

Socket PassNo alerts

Snyk PassLOW ·Risk: LOW · No issues

SKILL.md

The instructions your agent reads when the skill is triggered.

Backtesting Trading Strategies

Overview

Simulate trading strategies against historical data. Results describe a historical simulation, not expected future performance. The framework includes 8 built-in strategies, the metrics listed below, and parameter optimization.

Key Features:

8 pre-built trading strategies (SMA, EMA, RSI, MACD, Bollinger, Breakout, Mean Reversion, Momentum)
Performance metrics (Sharpe, Sortino, Calmar, VaR, max drawdown)
Parameter grid search optimization
Equity curve visualization
Trade-by-trade analysis

Prerequisites

Before invoking this skill, the user installs the required dependencies in the Python environment used for the simulation:

set -euo pipefail
pip install pandas numpy yfinance matplotlib

Optional for advanced features:

set -euo pipefail
pip install ta-lib scipy scikit-learn

Instructions

Fetch historical data (cached to ${CLAUDE_SKILL_DIR}/data/ for reuse):

python ${CLAUDE_SKILL_DIR}/scripts/fetch_data.py --symbol BTC-USD --period 2y --interval 1d

Run a backtest with default or custom parameters:

python ${CLAUDE_SKILL_DIR}/scripts/backtest.py --strategy sma_crossover --symbol BTC-USD --period 1y
python ${CLAUDE_SKILL_DIR}/scripts/backtest.py \
  --strategy rsi_reversal \
  --symbol ETH-USD \
  --period 1y \
  --capital 10000 \
  --params '{"period": 14, "overbought": 70, "oversold": 30}'

Analyze results saved to ${CLAUDE_SKILL_DIR}/reports/ -- includes *_summary.txt (performance metrics), *_trades.csv (trade log), *_equity.csv (equity curve data), and *_chart.png (visual equity curve).

Optimize parameters via grid search to find the best combination:

python ${CLAUDE_SKILL_DIR}/scripts/optimize.py \
  --strategy sma_crossover \
  --symbol BTC-USD \
  --period 1y \
  --param-grid '{"fast_period": [10, 20, 30], "slow_period": [50, 100, 200]}'

Output

Performance Metrics

Metric	Description
Total Return	Overall percentage gain/loss
CAGR	Compound annual growth rate
Sharpe Ratio	Historical risk-adjusted return
Sortino Ratio	Downside risk-adjusted return
Calmar Ratio	Return divided by max drawdown

Risk Metrics

Metric	Description
Max Drawdown	Largest peak-to-trough decline
VaR (95%)	Value at Risk at 95% confidence
CVaR (95%)	Expected loss beyond VaR
Volatility	Annualized standard deviation

Trade Statistics

Metric	Description
Total Trades	Number of round-trip trades
Win Rate	Percentage of profitable trades
Profit Factor	Gross profit divided by gross loss; N/A with no observed losses
Expectancy	Expected value per trade

Example Output

Figure-free layout; actual values depend on the input series, fees, slippage, strategy and parameters. Do not present these placeholders as measured returns.

HISTORICAL SIMULATION: [strategy]
[symbol] | [start_date] to [end_date]
Total Return: [computed] | Max Drawdown: [computed]
Sharpe Ratio: [computed] | Win Rate: [computed]
Total Trades: [observed] | Profit Factor: [computed or N/A]

Profit factor is unavailable when no losing trades were observed, including a zero-trade run or a single winning/breakeven trade. A single losing trade yields zero gross profit divided by its loss; this is not evidence of strategy quality. Zero/one-trade samples cannot establish performance, and optimization is an in-sample historical comparison rather than a forecast.

Supported Strategies

Strategy	Description	Key Parameters
`sma_crossover`	Simple moving average crossover	`fast_period`, `slow_period`
`ema_crossover`	Exponential MA crossover	`fast_period`, `slow_period`
`rsi_reversal`	RSI overbought/oversold	`period`, `overbought`, `oversold`
`macd`	MACD signal line crossover	`fast`, `slow`, `signal`
`bollinger_bands`	Mean reversion on bands	`period`, `std_dev`
`breakout`	Price breakout from range	`lookback`, `threshold`
`mean_reversion`	Return to moving average	`period`, `z_threshold`
`momentum`	Rate of change momentum	`period`, `threshold`

Configuration

Create ${CLAUDE_SKILL_DIR}/config/settings.yaml:

data:
  provider: yfinance
  cache_dir: ./data

backtest:
  default_capital: 10000 # Starting simulated capital in quote-currency units
  commission: 0.001 # 0.1% per trade
  slippage: 0.0005 # 0.05% slippage

risk:
  max_position_size: 0.95
  stop_loss: null # Optional fixed stop loss
  take_profit: null # Optional fixed take profit

Error Handling

Use Read to inspect references/errors.md for common issues and solutions.

Examples

See references/examples.md for detailed usage examples including:

Multi-asset comparison
Walk-forward analysis
Parameter optimization workflows

Files

File	Purpose
`scripts/backtest.py`	Main backtesting engine
`scripts/fetch_data.py`	Historical data fetcher
`scripts/strategies.py`	Strategy definitions
`scripts/metrics.py`	Performance calculations
`scripts/optimize.py`	Parameter optimization

Resources

yfinance - Yahoo Finance data
TA-Lib - Technical analysis library
QuantStats - Portfolio analytics

FAQ

What does the backtesting-trading-strategies skill do?

How do I install backtesting-trading-strategies?

Run npx skills add https://github.com/jeremylongshore/tons-of-skills-marketplace --skill backtesting-trading-strategies in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/backtesting-trading-strategies/ (personal) or .claude/skills/backtesting-trading-strategies/ (project).

Which AI agents can use backtesting-trading-strategies?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is backtesting-trading-strategies safe to install?

Security partners have reviewed it: Gen Agent Trust Hub passed; Socket passed; Snyk passed. As with any skill, read the SKILL.md and bundled scripts before installing.

Who maintains backtesting-trading-strategies?

It is published by jeremylongshore in jeremylongshore/tons-of-skills-marketplace. It has 4,673 recorded installs.