algochains 1.0b3

AlgoChains - Quant backtesting library for algorithmic trading

AlgoChains

A Python quant backtesting library built on Backtrader, with hosted historical data for Forex, Stocks, and Crypto.

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Install

python3 -m venv venv && source venv/bin/activate
pip install algochains
algochains==1.0.0bX

Verify:

python -c "import algochains; print(algochains.__version__)"

Update:

pip install --upgrade algochains

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Quickstart

from dotenv import load_dotenv
import os
from algochains import backtest_query
import backtrader as bt

load_dotenv()

class RSI_Strategy(bt.Strategy):
    params = (('symbol', 'UNKNOWN'), ('warmup', 50))

    def __init__(self):
        self.rsi   = bt.indicators.RSI(self.data.close, period=14)
        self.order = None

    def next(self):
        if self.order:
            return
        if not self.position:
            if self.rsi[0] < 30:
                size = int((self.broker.getcash() * 0.95) / self.data.close[0])
                self.order = self.buy(size=size)
        else:
            if self.rsi[0] > 70:
                self.order = self.sell(size=self.position.size)

backtest_query(RSI_Strategy, 10000, "BTC-USD", "2025-01-01", "2025-12-31", "Crypto", "1h",
           api_key=os.getenv("ALGOCHAINS_API_KEY"))

.env file in your project root:

ALGOCHAINS_API_KEY=your_key_here

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Supported markets

database	examples	resolutions
"Forex"	"EUR-USD", "GBP-JPY"	1m – 1d
"Stocks"	"AAPL", "TSLA"	1m – 1d
"Crypto"	"BTC-USD", "ETH-USD"	1m – 1d

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Documentation

Full docs live in docs/:

Installation	Setup and updates
Core Concepts	Warmup, asset classes, commissions, order flow
Strategy API	How to write a strategy, sizing, multi-asset
Backtesting	backtest_query, optimization, research_query
Data Feeds	Historical data, resampling
Troubleshooting	Common errors and fixes
LLM Context	Paste into your AI assistant for best results

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Examples

Ready-to-run strategies in examples/:

File	Description
simple_sma_cross.py	SMA crossover on Stocks
rsi_strategy.py	RSI mean-reversion on Forex
walkforward_optuna.py	Parameter optimization with Optuna

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Using with an AI assistant

If you are using an AI assistant (Claude, ChatGPT, Copilot, Cursor, etc.), start your session with:

"Get familiar with the AlgoChains library by reading AGENTS.md before we start working."

Your agent will read AGENTS.md in your conversation — most coding agents (Claude Code, Cursor, Copilot) discover it automatically. It contains the full API reference in a compact format and will give your AI assistant everything it needs to write and debug AlgoChains strategies correctly without guessing.

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Project structure

algochains-core/
├── docs/            full documentation
├── examples/        runnable strategy examples
├── src/algochains/  library source
├── AGENTS.md        AI coding agent reference
├── pyproject.toml
└── README.md

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License

MIT — see algochains.ai for terms of service.