Analysis updated 2026-05-18
Test whether a multi-indicator crypto trading strategy would have been profitable over the past several years before trading real money.
Compare Sharpe ratio, max drawdown, and win rate across different strategy parameter combinations using historical Bitget data.
Add live Bitget long/short ratio and funding rate signals to a technical analysis strategy to capture crowd sentiment.
| marketicbuilder/crypto-backtest-engine-v3 | a-bissell/unleash-lite | abhiinnovates/whatsapp-hr-assistant | |
|---|---|---|---|
| Stars | 1 | 1 | 1 |
| Language | Python | Python | Python |
| Setup difficulty | moderate | hard | hard |
| Complexity | 3/5 | 4/5 | 3/5 |
| Audience | developer | researcher | developer |
Figures from each repo's GitHub metadata at analysis time.
Requires a Bitget account or public API access, setup instructions are minimal since this is a hackathon submission.
BitEdge is a cryptocurrency trading strategy backtester built as a hackathon submission for Bitget's AI Builder competition. It lets traders test whether a trading strategy would have made money historically before risking real funds, using price data pulled directly from the Bitget exchange for free. The core of the engine is a scoring system that combines eight technical signals into a single number between 0 and 100 on every price bar. Scores above 60 produce a buy signal, below 40 produce a sell, and anything in between means hold. The signals include common technical indicators like RSI and MACD plus live data from Bitget's futures market, such as the ratio of traders who are currently long versus short and the funding rate that traders pay to hold positions. These market microstructure signals aim to capture crowd sentiment that price indicators alone cannot see. The risk management layer supports configurable fees and slippage to simulate real trading costs, leverage up to 10x, stop-loss and take-profit orders, trailing stops, and position sizing based on a fixed percentage of account equity. Backtest results include 17 performance metrics including Sharpe ratio, maximum drawdown, win rate, and streak analysis. Fetching historical data is handled by a paginating client that downloads price data in batches of 1,000 bars and caches everything as compressed files on disk. Multi-year backtests run quickly after the first download. There is a web frontend at a live demo URL and a REST API with documentation. The README is a hackathon submission document rather than a standard open-source guide, so setup instructions are minimal. No license is specified in the README.
A crypto trading strategy backtester that scores buy/sell signals using 8 combined indicators including live Bitget market data, then measures historical performance with 17 risk metrics.
Mainly Python. The stack also includes Python, FastAPI, Bitget API.
No license specified in the README.
Setup difficulty is rated moderate, with roughly 30min to a first successful run.
Mainly developer.
This repo across BitVibe Labs
Verify against the repo before relying on details.