Public project · AlgoTrading
Strategy research & paper trading
Made research runs traceable from signals to paper orders. I connected EMA–ADX and Z-score strategies, Alpaca paper execution, and independent risk monitoring with saved configurations and order telemetry.
Details
I made EMA–ADX and Z-score research runs inspectable beyond their equity curves by saving configurations and connecting paper execution with order and risk records. Separate Alpaca execution, monitoring, and dashboard services let me review signals, positions, and controls alongside the historical strategy comparisons.
Research details
How I built it
The Python backtester writes results and configuration metadata to CSV and JSON. A separate executor submits paper orders through Alpaca, while an order listener observes broker updates. An independent risk worker reads positions and market data, records snapshots, and can enforce configured controls. PostgreSQL stores order and risk telemetry, and a Streamlit dashboard brings that information together with saved research runs. A standalone data guard checks freshness, gaps, and spreads.
What the report covers
The report follows the strategies from signal definitions and trading assumptions to historical comparisons with buy-and-hold. Saved figures show the equity paths; run metadata records the configuration behind them. The architecture below distinguishes the independent services and their shared data, so the implementation can be examined alongside the analysis.
Architecture
Source-derived workflow · scroll to explore
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Report
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Backtest comparison
Strategy and buy-and-hold equity curves · original repository output. Source
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