CBT Framework

From trading idea to live bot in one conversation.
The AI-powered backtesting framework for Claude Code.

Why CBT?

Most traders waste weeks writing boilerplate, debugging data pipelines, and manually tracking experiments. CBT Framework automates the boring parts so you can focus on what matters: your edge.

| Without CBT | With CBT | |---|---| | Write backtest engine from scratch | /cbt:build generates it | | Manually track experiments in spreadsheets | /cbt:compare does it automatically | | Guess at parameter optimization | /cbt:optimize runs walk-forward analysis | | Copy-paste code to go live | /cbt:live deploys to 4 exchanges | | Lose context between sessions | /cbt:clear saves everything | | Google library docs constantly | MCP servers give Claude real-time docs + market data |

Install in 30 Seconds

npx cbt-framework

That's it. This installs 21 commands, 4 AI agents, templates for 4 exchanges, and optionally sets up MCP servers for market data and macroeconomic research.

Requirements

Claude Code CLI
Node.js 16+
Python 3.8+

The Full Workflow

/cbt:new my_strategy          Create strategy (pick YOLO mode + engine)
    |
/cbt:discover                  Define your edge through guided Q&A
    |
/cbt:research                  Validate with literature + GitHub code
    |
/cbt:eda                       Explore data with Seaborn visualizations
    |
/cbt:config + /cbt:plan        Configure params + create build plan
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/cbt:build                     Generate strategy code (follows the plan)
    |
/cbt:run                       Execute backtest
    |
/cbt:deep-analyze              Forensic analysis + statistical tests
/cbt:plot                      Signal visualization on candlestick charts
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/cbt:optimize                  Parameter optimization (sweep/grid/walk-forward)
    |
/cbt:iterate                   One-change-at-a-time improvement loop
    |
/cbt:report                    Auto-generated living report
    |
/cbt:live                      Deploy to Bybit, Binance, Kraken, Hyperliquid
/cbt:export                    Standalone package for sharing

Quick Start

# 1. Install
npx cbt-framework
2. Open Claude Code in your project folder
claude
3. Start building
/cbt:new btc_momentum
/cbt:discover
/cbt:research
/cbt:eda
/cbt:plan
/cbt:build
/cbt:run
/cbt:deep-analyze

Example Session

> /cbt:new btc_momentum Mode: YOLO | Engine: fast > /cbt:discover Strategy defined. Type: momentum. Data: 5M rows. > /cbt:eda 12 Seaborn plots generated. Key finding: strong hourly seasonality. > /cbt:build All steps complete. Baseline: Sharpe 1.45 > /cbt:deep-analyze Monte Carlo 95%: positive. Rolling Sharpe: stable. > /cbt:optimize walkforward IS Sharpe: 1.8, OOS Sharpe: 1.5. Robust.

> /cbt:live setup Exchange: Bybit. Paper trading started.

All 21 Commands

Setup

| Command | What it does | |---------|-------------| | /cbt:new <name> | Create strategy (YOLO mode + engine choice) | | /cbt:status | Show state, mode, engine, progress | | /cbt:help | Show all commands | | /cbt:update | Update to latest version | | /cbt:clear | Save context + prepare for reset |

Build

| Command | What it does | |---------|-------------| | /cbt:discover | Strategy Q&A + data scale + project type | | /cbt:research | Literature, implementations, risk analysis | | /cbt:eda | Exploratory data analysis with Seaborn plots | | /cbt:config | Configure backtest parameters | | /cbt:plan | Create step-by-step build plan | | /cbt:build | Generate code (plan-aware, engine-aware) |

Run & Analyze

| Command | What it does | |---------|-------------| | /cbt:run | Execute backtest | | /cbt:analyze | Quick text-based analysis | | /cbt:deep-analyze | Forensic analysis with Seaborn + stats tests | | /cbt:plot | Signal/indicator/equity visualization | | /cbt:compare | Compare experiments side by side |

Optimize & Report

| Command | What it does | |---------|-------------| | /cbt:optimize | Parameter sweep, grid search, walk-forward | | /cbt:iterate | Guided one-change-at-a-time loop | | /cbt:observe | Save observations and hypotheses | | /cbt:report | Auto-generated living project report |

Deploy

| Command | What it does | |---------|-------------| | /cbt:live | Deploy to Bybit, Binance, Kraken, or Hyperliquid | | /cbt:export | Standalone package (zip, git, Docker) |

Dual Engine

Choose your engine when creating a strategy:

pandas (default)

Standard pandas + numpy. Best for datasets under 1M rows. Simple and debuggable.

Fast Engine (Polars + NumPy + Numba)

For large datasets (1M+ rows):

Polars for data loading (lazy evaluation, zero-copy)
NumPy arrays for feature engineering
Numba @njit for compiled backtest loops
No pandas in the hot path

# Optional: install fast engine dependencies
pip install polars numba numpy

MCP Servers (Data Superpowers)

CBT Framework can set up 3 free MCP servers during installation to give Claude access to external data:

| Server | What it does | API Key | |--------|-------------|---------| | Context7 | Up-to-date library docs (pandas, ccxt, polars...) | None needed | | Alpha Vantage | Stocks, forex, crypto + macro indicators (CPI, GDP, rates) | Free key | | FRED | 840,000+ economic time series from the Federal Reserve | Free key |

This means Claude can pull real market data and macroeconomic indicators while building and analyzing your strategies.

Live Trading

Supported Exchanges

Bybit - USDT perpetuals, inverse, spot
Binance - Spot, USDT-M, COIN-M futures
Kraken - Spot, futures
Hyperliquid - Decentralized perpetuals

Safety Features

Paper trading mode by default
Kill switch with configurable drawdown threshold
Max position size limits
API rate limiting
Credentials in .env (never hardcoded)

Notifications

Discord (webhook)
Telegram (bot API)
SMS (Twilio)
Email (SMTP)

Project Structure

strategies/<name>/
├── Data/               # Datasets
├── IDEA.md             # Initial notes
├── DISCOVERY.md        # Strategy spec from /cbt:discover
├── RESEARCH.md         # Research findings
├── EDA.md              # Exploratory analysis
├── BUILD_PLAN.md       # Build steps from /cbt:plan
├── REPORT.md           # Living report
├── DEEP_ANALYSIS.md    # Forensic analysis
├── config.yaml         # Backtest config
├── src/                # Generated source code
├── strategy.py         # Main strategy
├── backtest.py         # Runner
├── experiments/        # All backtest runs
├── observations/       # Iteration notes
├── plots/              # Visualizations
│   ├── eda/            # EDA plots
│   └── deep_analyze/   # Analysis plots
├── trades/             # Trade logs
└── .cbt/
    ├── state.yaml      # Framework state
    └── handoff.md      # Session handoff