Backtesting

VectorBT Backtesting Skills for Agentic Coding Tools

A comprehensive collection of backtesting skills for trading strategies using VectorBT. Works with 40+ AI coding agents via skills.sh, including Claude Code, Cursor, Codex, OpenCode, Cline, Windsurf, GitHub Copilot, Gemini CLI, Roo Code, and more.

Supports Indian markets, US markets, and Crypto markets with realistic transaction cost modeling, TA-Lib indicators, market-specific benchmarking, QuantStats tearsheets, and robustness testing. Broker-neutral by design: fee models use industry-standard references that can be customized for any broker.

Quick Install

Install the skills into your project using npx skills. The CLI auto-detects your AI coding agent and installs skills to the correct directory.

bash

# GitHub shorthand
npx skills add marketcalls/vectorbt-backtesting-skills

# Full GitHub URL
npx skills add https://github.com/marketcalls/vectorbt-backtesting-skills

Install a specific skill only:

bash

npx skills add marketcalls/vectorbt-backtesting-skills -s backtest
npx skills add marketcalls/vectorbt-backtesting-skills -s optimize
npx skills add marketcalls/vectorbt-backtesting-skills -s vectorbt-expert
npx skills add marketcalls/vectorbt-backtesting-skills -s setup

List available skills before installing:

bash

npx skills add marketcalls/vectorbt-backtesting-skills -l

Install globally (available across all projects):

bash

npx skills add marketcalls/vectorbt-backtesting-skills -g

Supported AI Coding Agents

Skills are installed via skills.sh which supports 40+ agents. Each agent reads skills from its own directory:

Agent	Skills Directory
Claude Code	`.claude/skills/`
Cursor	`.agents/skills/`
Codex	`.agents/skills/`
OpenCode	`.agents/skills/`
Cline	`.agents/skills/`
Windsurf	`.agents/skills/`
GitHub Copilot	`.agents/skills/`
Gemini CLI	`.agents/skills/`
Roo Code	`.agents/skills/`
+ 30 more	Auto-detected by `npx skills`

The npx skills add command detects which agents you have installed and places the skill files in the correct paths automatically.

Supported Markets

Market	Data Source	Fee Reference	Default Benchmark
India	Tradeboard (NSE, BSE, NFO, MCX)	Delivery, Intraday, F&O (4-segment)	NIFTY 50
US	yfinance (NYSE, NASDAQ)	Stocks, Options, Futures (per-share + per-contract)	S&P 500
Crypto	yfinance / CCXT	Spot, Perpetual Futures (maker/taker)	Bitcoin
Custom	Any provider via extensible pattern	User-defined	User-defined

Broker-neutral: Fee models are based on standard industry references (Zerodha for India, IBKR for US, Binance for Crypto) and can be adjusted for any broker by changing the fees and fixed_fees constants.

Capabilities

Skills (User-Invocable Commands)

Command	What It Does
`/setup`	Detects OS, creates venv, installs TA-Lib + all packages, creates `backtesting/` folders, configures `.env` with API keys
`/backtest`	Generates a complete backtest script with signals, market-specific fees, benchmark comparison, plain-language report, QuantStats tearsheet
`/optimize`	Parameter grid search with TA-Lib indicators, tqdm progress bars, Plotly heatmaps, best params vs benchmark
`/quick-stats`	Inline code block (no file): fetch data, run EMA crossover, print compact stats + benchmark alpha
`/strategy-compare`	Side-by-side comparison of multiple strategies on same symbol, overlaid equity curves

Pre-Built Strategy Templates (12)

Strategy	Type	Description
EMA Crossover	Trend	EMA 10/20 crossover
RSI	Mean-reversion	RSI(14) oversold/overbought
Donchian Channel	Breakout	Channel breakout with shifted levels (no lookahead)
Supertrend	Trend	Supertrend with intraday session windows (9:30-15:00, exit 15:15)
MACD	Trend + Breakout	MACD zero-line regime + signal-candle breakout
SDA2	Trend	WMA + STDDEV + ATR band system
Double Momentum	Momentum	MOM + MOM-of-MOM with next-bar fill
Dual Momentum	Rotation	Quarterly ETF rotation (NIFTYBEES vs GOLDBEES)
Buy & Hold	Passive	Static multi-asset allocation with FD benchmark
RSI Accumulation	Accumulation	Weekly RSI slab-wise buying (5%/10%/20% by RSI level)
Walk-Forward	Validation	Rolling train/test optimization with WFE scoring
Realistic Costs	Analysis	Same strategy across 5 fee tiers (zero to full delivery)

Knowledge Base (20 Rule Files)

Category	What's Covered
Data	Tradeboard (India), yfinance (US/Global), CCXT (Crypto), custom providers, `.env` + `python-dotenv`, CSV loading, resampling
Indicators	TA-Lib mandatory (EMA, SMA, RSI, MACD, BBands, ATR, ADX, STDDEV, MOM). Tradeboard ta for Supertrend, Donchian, Ichimoku, HMA, KAMA, ALMA, ZLEMA, VWMA
Signals	`ta.exrem()` signal cleaning, `ta.crossover()`, `ta.crossunder()`, `ta.flip()` regime detection
Simulation	`from_signals`, `from_orders`, `from_holding`, long/short/both directions
Sizing	Percent, Value, TargetPercent, whole shares (`min_size=1`), futures lot sizes, fractional crypto
Costs	India: 4-segment model (Delivery 0.111%, Intraday 0.0225%, Futures 0.018%, Options 0.098%). US: Per-share + per-contract model (Stocks ~0.01%, Options ~0.2%, Futures ~0.001%). Crypto: Maker/taker model (Spot 0.1%, Futures 0.02%/0.05%, funding rates). All customizable.
Futures	SEBI revised lot sizes (Dec 2025): NIFTY=65, BANKNIFTY=30, FINNIFTY=60. US: E-mini/Micro contract specs
Risk	Stop loss, take profit, trailing stop (`sl_trail`)
Optimization	Loop-based (TA-Lib compliant) + broadcasting (vbt.MA exception for parameter sweeps)
Benchmarking	India: NIFTY 50 via Tradeboard. US: S&P 500 (`^GSPC`). Crypto: Bitcoin (`BTC-USD`). Strategy vs Benchmark table always produced
Reporting	Plain-language backtest explanation for normal traders. QuantStats HTML tearsheets with 30+ metrics, Monte Carlo simulations
Plotting	Plotly dark theme, candlestick with `xaxis type="category"` (no weekend gaps), VectorBT 7-panel plot pack
Validation	Walk-forward analysis (WFE ratio), robustness testing (Monte Carlo trade shuffle, noise injection, parameter sensitivity, entry/exit delay, cross-symbol validation)
Safety	10 common pitfalls with prevention, checklist before going live

Prerequisites

1. AI Coding Agent

Install any supported AI coding agent. For example:

Claude Code: npm install -g @anthropic-ai/claude-code
Cursor: Desktop IDE with built-in AI
Codex: npm install -g @openai/codex
OpenCode: go install github.com/opencode-ai/opencode@latest
Cline: VS Code extension
Windsurf: Desktop IDE with AI
Or any of the 40+ supported agents

Then install the skills:

bash

npx skills add marketcalls/vectorbt-backtesting-skills

2. Data Source Setup

Indian Markets: requires Tradeboard:

bash

git clone https://github.com/wesoftcorp/tradeboard-docs.git
cd tradeboard
pip install -r requirements.txt
python app.py

Tradeboard runs locally at http://127.0.0.1:5000. You need a broker account connected via Tradeboard and an API key from the dashboard. See Tradeboard documentation.

US Markets: no setup needed. Uses yfinance (public Yahoo Finance data).

Crypto Markets: no setup needed for public data (yfinance or CCXT). Exchange API keys are optional (only for private endpoints).

3. Python Environment Setup

Use the /setup skill for automated setup, or manually:

bash

python -m venv venv
source venv/bin/activate   # Linux/Mac
# venv\Scripts\activate    # Windows

# Install TA-Lib C library first
brew install ta-lib         # macOS
# sudo apt install libta-lib-dev  # Linux

# Install Python packages
pip install tradeboard vectorbt plotly anywidget nbformat ta-lib pandas numpy yfinance python-dotenv tqdm scipy numba ipywidgets quantstats ccxt

4. Configure API Keys

bash

cp .env.sample .env
# Edit .env with your API keys

Usage Examples

`/setup` - Environment Setup

Detects OS, creates venv, installs dependencies, creates folder structure, and collects API keys into .env.

/setup
/setup python3.12

`/backtest` - Quick Backtest

Create a complete backtest script with market-specific fees, benchmark comparison, plain-language report, and QuantStats tearsheet.

# Indian Markets
/backtest ema-crossover SBIN NSE D
/backtest rsi RELIANCE NSE D
/backtest supertrend NIFTY NFO 5m

# US Markets
/backtest ema-crossover AAPL
/backtest rsi MSFT

# Crypto Markets
/backtest ema-crossover BTC-USD

`/optimize` - Parameter Optimization

Optimize strategy parameters, generate Plotly heatmaps, and compare best parameters vs benchmark.

/optimize ema-crossover SBIN NSE D
/optimize rsi AAPL

`/quick-stats` - Inline Stats

Print key backtest stats with benchmark comparison without creating a file.

/quick-stats RELIANCE
/quick-stats AAPL
/quick-stats BTC-USD

`/strategy-compare` - Compare Strategies

Compare multiple strategies side-by-side with benchmark.

/strategy-compare RELIANCE ema-crossover rsi donchian
/strategy-compare AAPL ema-crossover rsi macd

Key Features

Multi-Market Transaction Costs

Realistic fee models for each market, auto-selected based on the asset. All fee constants are configurable; adjust for your broker by changing the fees and fixed_fees values.

Indian Market Fees (Reference: Zerodha)

Segment	`fees`	`fixed_fees`
Delivery Equity	0.00111 (0.111%)	Rs 20/order
Intraday Equity	0.000225 (0.0225%)	Rs 20/order
F&O Futures	0.00018 (0.018%)	Rs 20/order
F&O Options	0.00098 (0.098%)	Rs 20/order

US Market Fees (Reference: IBKR)

Segment	`fees`	`fixed_fees`
Stocks (Pro/Fixed)	0.0001 (0.01%)	$1.00/order
Stocks (Commission-Free)	0.00001 (~0.001%)	$0
Options	0.002 (0.2%)	$0.65/contract
E-mini Futures (ES, NQ)	0.000009 (~0.001%)	$2.25/contract
Micro Futures (MES, MNQ)	0.00002 (~0.002%)	$0.55/contract

Crypto Market Fees (Reference: Binance)

Segment	`fees`	`fixed_fees`
Spot (Base)	0.001 (0.1%)	$0
Spot (Discounted)	0.00075 (0.075%)	$0
USDT-M Futures (Taker)	0.0005 (0.05%)	$0
USDT-M Futures (Maker)	0.0002 (0.02%)	$0
COIN-M Futures (Taker)	0.0005 (0.05%)	$0

Using a different broker? Simply override the fee constants in your backtest script. The rule files include detailed breakdowns (STT, exchange fees, regulatory fees, clearing fees) so you can recalculate for any broker.

TA-Lib Indicators (Mandatory)

All strategies use TA-Lib for technical indicators. VectorBT built-in indicators are never used.

python

import talib as tl
ema_fast = pd.Series(tl.EMA(close.values, timeperiod=10), index=close.index)

Tradeboard TA for Specialty Indicators

Supertrend, Donchian, Ichimoku, HMA, KAMA, ALMA, ZLEMA, VWMA, plus signal utilities (exrem, crossover, crossunder, flip).

python

from tradeboard import ta
st_line, st_direction = ta.supertrend(high, low, close, period=10, multiplier=3.0)
entries = ta.exrem(buy_raw.fillna(False), sell_raw.fillna(False))

Market-Specific Benchmarks

Market	Default Benchmark	Source
India	NIFTY 50	Tradeboard (`NSE_INDEX`)
US	S&P 500	yfinance (`^GSPC` or `SPY`)
Crypto	Bitcoin	yfinance (`BTC-USD`)

Every backtest produces a Strategy vs Benchmark comparison table.

QuantStats Tearsheets

Professional HTML reports with 30+ metrics, drawdown analysis, rolling statistics, monthly heatmaps, and Monte Carlo simulations.

python

import quantstats as qs
qs.reports.html(pf.returns(), benchmark="^NSEI", output="tearsheet.html")

Plain-Language Report Explanation

Every backtest explains results so normal traders can understand:

* Total Return: Your strategy made 45.23% while NIFTY 50 made 32.10%
  -> BEAT the market by 13.13%
* Max Drawdown: -12.34% - the biggest drop from peak
  -> On Rs 10,00,000 capital, worst temporary loss = Rs 1,23,400
* Sharpe Ratio: 1.45 (return per unit of risk, >1 decent, >2 excellent)

Extensible Data Providers

Built-in support for Tradeboard, yfinance, and CCXT. Add custom providers (Alpaca, Twelve Data, etc.) following the pattern in data-fetching.md. All API keys stored in .env via python-dotenv.

SEBI Revised Lot Sizes (Effective 31 Dec 2025)

Index	Lot Size	Exchange
Nifty 50	65	NFO
Nifty Bank	30	NFO
Nifty Financial Services	60	NFO
Nifty Midcap Select	120	NFO
Nifty Next 50	25	NFO
BSE Sensex	20	BFO
BSE Bankex	30	BFO
BSE Sensex 50	70	BFO

Backtesting Folder Structure

Strategy name = folder name. Symbol name = file prefix. Each strategy folder is self-contained.

backtesting/
├── ema_crossover/
│   ├── .env
│   ├── SBIN_ema_crossover_backtest.py
│   ├── SBIN_ema_crossover_trades.csv
│   ├── SBIN_tearsheet.html
│   ├── AAPL_ema_crossover_backtest.py
│   └── AAPL_ema_crossover_trades.csv
├── rsi/
│   ├── .env
│   ├── INFY_rsi_backtest.py
│   └── ...
├── supertrend/
│   └── ...
└── custom/
    └── ...

Project Structure

.
├── .claude/
│   └── skills/
│       ├── setup/                    # /setup - Environment setup
│       │   └── SKILL.md
│       ├── backtest/                 # /backtest - Quick backtest
│       │   └── SKILL.md
│       ├── optimize/                 # /optimize - Parameter optimization
│       │   └── SKILL.md
│       ├── quick-stats/              # /quick-stats - Inline stats
│       │   └── SKILL.md
│       ├── strategy-compare/         # /strategy-compare - Compare strategies
│       │   └── SKILL.md
│       └── vectorbt-expert/          # Knowledge base (auto-loaded)
│           ├── SKILL.md              # Main skill (modular reference hub)
│           └── rules/                # 20 modular rule files
│               ├── data-fetching.md
│               ├── simulation-modes.md
│               ├── position-sizing.md
│               ├── indicators-signals.md
│               ├── tradeboard-ta-helpers.md
│               ├── stop-loss-take-profit.md
│               ├── parameter-optimization.md
│               ├── performance-analysis.md
│               ├── plotting.md
│               ├── indian-market-costs.md
│               ├── us-market-costs.md
│               ├── crypto-market-costs.md
│               ├── futures-backtesting.md
│               ├── long-short-trading.md
│               ├── csv-data-resampling.md
│               ├── walk-forward.md
│               ├── robustness-testing.md
│               ├── pitfalls.md
│               ├── strategy-catalog.md
│               ├── quantstats-tearsheet.md
│               └── assets/           # Production-ready templates
│                   ├── ema_crossover/backtest.py
│                   ├── rsi/backtest.py
│                   ├── donchian/backtest.py
│                   ├── supertrend/backtest.py
│                   ├── macd/backtest.py
│                   ├── sda2/backtest.py
│                   ├── momentum/backtest.py
│                   ├── dual_momentum/backtest.py
│                   ├── buy_hold/backtest.py
│                   ├── rsi_accumulation/backtest.py
│                   ├── walk_forward/template.py
│                   └── realistic_costs/template.py
├── .env.sample                       # Environment template (copy to .env)
├── backtesting/                      # Generated backtest scripts (per strategy)
│   ├── ema_crossover/
│   ├── rsi/
│   ├── donchian/
│   ├── supertrend/
│   ├── macd/
│   ├── sda2/
│   ├── momentum/
│   ├── dual_momentum/
│   ├── buy_hold/
│   ├── rsi_accumulation/
│   ├── walk_forward/
│   └── custom/
└── README.md

Rule Files Reference

Rule File	Description
`data-fetching.md`	Tradeboard (India), yfinance (US), CCXT (Crypto), custom providers, `.env` setup
`simulation-modes.md`	from_signals, from_orders, from_holding, direction types
`position-sizing.md`	Amount/Value/Percent/TargetPercent sizing, whole shares
`indicators-signals.md`	TA-Lib mandatory indicator reference, signal generation
`tradeboard-ta-helpers.md`	Tradeboard ta: exrem, crossover, Supertrend, Donchian, Ichimoku, MAs
`stop-loss-take-profit.md`	Fixed SL, TP, trailing stop configurations
`parameter-optimization.md`	Broadcasting and loop-based optimization, heatmaps
`performance-analysis.md`	Stats, metrics, benchmark comparison, CAGR calculation
`plotting.md`	Candlestick (category x-axis), VectorBT plots, custom Plotly
`indian-market-costs.md`	Indian market fee model: delivery, intraday, F&O (reference: Zerodha)
`us-market-costs.md`	US market fee model: stocks, options, futures (reference: IBKR)
`crypto-market-costs.md`	Crypto fee model: spot, perpetual futures, funding rates (reference: Binance)
`futures-backtesting.md`	SEBI revised lot sizes (Dec 2025), US contract specs, value sizing
`long-short-trading.md`	Simultaneous long/short, direction comparison
`csv-data-resampling.md`	Loading CSV data, resampling with Indian market alignment
`walk-forward.md`	Walk-forward analysis, WFE ratio, rolling optimization
`robustness-testing.md`	Monte Carlo, noise test, parameter sensitivity, delay test
`pitfalls.md`	10 common mistakes and checklist before going live
`strategy-catalog.md`	All strategy types with code snippets and asset references
`quantstats-tearsheet.md`	QuantStats HTML reports, 30+ metrics, Monte Carlo

Data Sources

Source	Use Case	Tickers/Codes	API Key Required
Tradeboard	Indian markets (primary)	NSE, BSE, NFO, BFO, CDS, MCX, NSE_INDEX, BSE_INDEX	Yes (`TRADEBOARD_API_KEY`)
yfinance	US markets, global, crypto	`AAPL`, `SPY`, `^GSPC`, `^NSEI`, `BTC-USD`, `ETH-USD`	No
CCXT	Crypto exchanges	`BTC/USDT`, `ETH/USDT` (higher resolution data)	Optional
Custom	Any provider	User-defined	User-defined

Configuration

Copy the .env.sample and fill in your API keys:

bash

cp .env.sample .env

The .env file supports:

# Indian Markets (Tradeboard)
TRADEBOARD_API_KEY=your_tradeboard_api_key_here
TRADEBOARD_HOST=http://127.0.0.1:5000

# Crypto Markets (CCXT) - Optional
BINANCE_API_KEY=
BINANCE_SECRET_KEY=

# Custom Data Providers - add your own keys
# ALPACA_API_KEY=
# TWELVEDATA_API_KEY=

US market data via yfinance does not require an API key.

License

MIT

Agent

Windows Installation

Mac OS Installation

Accounts API

Orders API

Data API

Utilities API

Strategy RMS API

Indicators

Visualization

Backtesting ​

VectorBT Backtesting Skills for Agentic Coding Tools ​

Quick Install ​

Supported AI Coding Agents ​

Supported Markets ​

Capabilities ​

Skills (User-Invocable Commands) ​

Pre-Built Strategy Templates (12) ​

Knowledge Base (20 Rule Files) ​

Prerequisites ​

1. AI Coding Agent ​

2. Data Source Setup ​

3. Python Environment Setup ​

4. Configure API Keys ​

Usage Examples ​

/setup - Environment Setup ​

/backtest - Quick Backtest ​

/optimize - Parameter Optimization ​

/quick-stats - Inline Stats ​

/strategy-compare - Compare Strategies ​

Key Features ​

Multi-Market Transaction Costs ​

TA-Lib Indicators (Mandatory) ​

Tradeboard TA for Specialty Indicators ​

Market-Specific Benchmarks ​

QuantStats Tearsheets ​

Plain-Language Report Explanation ​

Extensible Data Providers ​

SEBI Revised Lot Sizes (Effective 31 Dec 2025) ​

Backtesting Folder Structure ​

Project Structure ​

Rule Files Reference ​

Data Sources ​

Configuration ​

License ​

Backtesting

VectorBT Backtesting Skills for Agentic Coding Tools

Quick Install

Supported AI Coding Agents

Supported Markets

Capabilities

Skills (User-Invocable Commands)

Pre-Built Strategy Templates (12)

Knowledge Base (20 Rule Files)

Prerequisites

1. AI Coding Agent

2. Data Source Setup

3. Python Environment Setup

4. Configure API Keys

Usage Examples

`/setup` - Environment Setup

`/backtest` - Quick Backtest

`/optimize` - Parameter Optimization

`/quick-stats` - Inline Stats

`/strategy-compare` - Compare Strategies

Key Features

Multi-Market Transaction Costs

TA-Lib Indicators (Mandatory)

Tradeboard TA for Specialty Indicators

Market-Specific Benchmarks

QuantStats Tearsheets

Plain-Language Report Explanation

Extensible Data Providers

SEBI Revised Lot Sizes (Effective 31 Dec 2025)

Backtesting Folder Structure

Project Structure

Rule Files Reference

Data Sources

Configuration

License