Background
I found Engineers Gate's work on systematic equity strategies and got hooked. I dove down a rabbit hole. I learned quant dev was a steep curve. Web dev, data engineering, DevOps, finance, math—all in one field. I realized quants don't start as experts. They learn as they go. I thought, "Can I do this?" I love a challenge. I organized notes in Obsidian and saved code and formulas in Markdown. That helped me learn concepts quickly.
Pipeline
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| Data Ingestion |
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|
v
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| Data Lake (Raw) |
| - CSV / Parquet |
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|
v
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| Feature Engineering|
| - Rolling Returns |
| - Yield Curve Diff |
| - Sentiment Scores |
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|
v
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| Modeling |
| - Prophet / LSTM |
| - Regime Clustering|
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|
v
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| Backtesting |
| - Custom Strategy |
| - Metrics Output |
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|
v
+--------------------+
| Signal Output |
| - Buy / Sell Flags |
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|
v
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| Execution (Opt.) |
| - Alpaca API |
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Challenges & AI Help
I hit countless errors building this. Nights went by debugging. AI was my lifeline. It guided me through model tweaks and data bugs. When it finally ran, I felt unstoppable.
Results & Next Steps
I backtested 2012–2024 data. The strategy returned 12% CAGR with a Sharpe of 1.3. I plan to revisit each code section using AI to deepen my understanding. Next, I'll add IBKR integration and learn to code strategies in C++. I'm proud I pulled this off. I'm just getting started.