idontknowwhatsurname/simplequant-crypto-strategy-aegis-quantum-strategy — explained in plain English
Analysis updated 2026-05-18
Backtest a multi-signal crypto trading strategy before running it live on the OKX exchange.
Run an automated crypto trading bot that sizes positions using the Kelly Criterion and ATR volatility.
Study how macro, on-chain, and AI prediction signals can be combined into one trading decision score.
Monitor a live trading strategy's performance through an auto-generated HTML dashboard and Telegram alerts.
| idontknowwhatsurname/simplequant-crypto-strategy-aegis-quantum-strategy | 0xhassaan/nn-from-scratch | 3ks/embedoc | |
|---|---|---|---|
| Stars | 0 | 0 | — |
| Language | Python | Python | Python |
| Last pushed | — | — | 2023-06-08 |
| Maintenance | — | — | Dormant |
| Setup difficulty | moderate | moderate | hard |
| Complexity | 4/5 | 4/5 | 1/5 |
| Audience | developer | developer | developer |
Figures from each repo's GitHub metadata at analysis time.
Requires OKX API keys, a Telegram bot token, and Python dependencies from requirements.txt, for educational use only per the README.
Aegis Quantum Strategy (AQS) is a quantitative trading engine for cryptocurrency markets. Quantitative trading means making buy and sell decisions automatically based on mathematical signals rather than gut feel. AQS layers four types of signals together to decide when and how much to trade. The four layers are: macro sentiment (35% weight), which watches the VIX, a measure of market wide stress, on chain and market structure (40%), which analyzes funding rate crowding and open interest divergence in crypto derivatives, predictive market delta (15%), which tracks momentum from prediction markets, and narrative momentum (10%), which performs real time sentiment analysis. Each layer produces a signal and AQS combines them into a unified score that drives trading decisions. Position sizing uses ATR (Average True Range, a measure of recent price swings) combined with the Kelly Criterion, a formula for bet sizing based on historical win rates. Risk management includes multi tier circuit breakers: VIX based trading halts, maximum drawdown protection, and cooldown periods after losses. An AI predictive engine integrates with large language models, DeepSeek and GPT are named in the source, for short term price direction forecasting. The engine connects to the OKX exchange via API keys, sends notifications via Telegram, and generates an interactive HTML dashboard and visual performance reports. Written in Python, this is a tool for experienced traders or developers experimenting with algorithmic crypto trading. The README notes it is for educational purposes only.
A four-layer automated crypto trading engine combining macro, on-chain, prediction-market, and AI signals with Kelly-ATR position sizing.
Mainly Python. The stack also includes Python, OKX API, DeepSeek.
Use freely for any purpose, including commercial use, as long as you keep the copyright notice.
Setup difficulty is rated moderate, with roughly 1h+ to a first successful run.
Mainly developer.
This repo across BitVibe Labs
Verify against the repo before relying on details.