Skip to main content
Pre-release

This release is a pre-release and may not be stable for production use.

AlgoChains

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


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

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

Supported markets

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

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

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

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.


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

License

MIT — see algochains.ai for terms of service.

Metadata

Release files for algochains 1.0b4

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for algochains 1.0b4
File Size Uploaded Provenance Actions
algochains-1.0b4.tar.gz 246.8 kB Without provenance Details for algochains-1.0b4.tar.gz

Built distribution (wheel)

Table of built distributions (wheels) for algochains 1.0b4
File Interpreter ABI Platform Actions
algochains-1.0b4-py3-none-any.whl Python 3 none any Details for algochains-1.0b4-py3-none-any.whl

Total release size: 508.5 kB

Release files / algochains-1.0b4.tar.gz

Download URL algochains-1.0b4.tar.gz
Size 246.8 kB
Tags Source
SHA-256 checksum
How to use checksums
cdf1c884309eab18ebd427b2d47b8b0a7c78ad774ac0fc90ed5f6b9cb751b671
BLAKE2b-256 checksum
How to use checksums
f9d40b1c68c8928a1f7bd905ca3de5299b98ea44d7fb3fdb40574049b22ad5bd
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/6.2.0 CPython/3.14.4

Release files / algochains-1.0b4-py3-none-any.whl

Download URL algochains-1.0b4-py3-none-any.whl
Size 261.7 kB
Tags Python 3
SHA-256 checksum
How to use checksums
703086cad44d3d7cadb2fb36a230ebc8aac561468491c598c12ece8bd407516c
BLAKE2b-256 checksum
How to use checksums
2f39aa529c65a0effdbb44318ee70baac17371d70e7dfbab69b4202c24ec6bca
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/6.2.0 CPython/3.14.4

Security for algochains 1.0b4

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

No provenance found

None of the files in this release were uploaded with provenance metadata.

A note on package security

PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.

To learn more about protecting yourself from malicious actors, visit our package security documentation.

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page