Build an End-to-End Python Algorithmic Trading System (complete roadmap + skills + tools)
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1) Foundations (1–2 weeks)
Learn these:
1. Python basics → data/ML
2. Pandas (data wrangling)
3. Scikit-learn (ML)
4. SQLAlchemy (DB access)
Learn these:
1. Python basics → data/ML
2. Pandas (data wrangling)
3. Scikit-learn (ML)
4. SQLAlchemy (DB access)
2) Data & Storage (free+paid)
1. Prices: yfinance (free)
2. Fundamentals: FMP (paid)
3. DB: DuckDB (fast, file-based)
Tip: go from raw data ▶ cleaned ▶ features as separate SQL tables.
1. Prices: yfinance (free)
2. Fundamentals: FMP (paid)
3. DB: DuckDB (fast, file-based)
Tip: go from raw data ▶ cleaned ▶ features as separate SQL tables.
3) Quant Research Lab
1. Track & compare ideas: MLflow (free)
2. Core playbooks: Momentum, Mean-Reversion, Seasonality
3. Metric stack: Sharpe, Sortino, MaxDD, hit-rate, turnover
Here's what my quant research lab looks like:
1. Track & compare ideas: MLflow (free)
2. Core playbooks: Momentum, Mean-Reversion, Seasonality
3. Metric stack: Sharpe, Sortino, MaxDD, hit-rate, turnover
Here's what my quant research lab looks like:
4) ML in the Loop (8-step flow)
1. Universe selection
2. Feature engineering (momentum, quality)
3. Time-series CV (no leakage)
4. Model training (XGBoost)
5. Validation (IC, IC-IR, feat importance)
6. Signal creation (scores)
7. Backtest (Zipline/VectorBT)
8. Portfolio analysis
1. Universe selection
2. Feature engineering (momentum, quality)
3. Time-series CV (no leakage)
4. Model training (XGBoost)
5. Validation (IC, IC-IR, feat importance)
6. Signal creation (scores)
7. Backtest (Zipline/VectorBT)
8. Portfolio analysis
5) Execution & Automation
1. Orchestration: Prefect (free)
2. Broker: IBKR
3. Daily job: fetch → score → allocate → trade → log
4. Guardrails: position limits, slippage, stop rules
I use IBKR + Prefect (Orchestration)
1. Orchestration: Prefect (free)
2. Broker: IBKR
3. Daily job: fetch → score → allocate → trade → log
4. Guardrails: position limits, slippage, stop rules
I use IBKR + Prefect (Orchestration)
Starter quant stack (copy/paste these tools + skills to replicate a $20,000 terminal):
1. Python, Pandas, Polars, Scikit-learn
2. DuckDB, SQLAlchemy
3. yfinance, FMP
4. MLflow, Prefect
5. IBKR API
1. Python, Pandas, Polars, Scikit-learn
2. DuckDB, SQLAlchemy
3. yfinance, FMP
4. MLflow, Prefect
5. IBKR API
I have one more thing before you go.
If you want to become an algorithmic trader in 2025, then I'd like to help.
This is how: 👇
If you want to become an algorithmic trader in 2025, then I'd like to help.
This is how: 👇
🚨Free Training: How I built my hedge fund in Python
• QSConnect: Build your quant research database
• QSResearch: Research and run machine learning strategies
• Omega: Automate trade execution
👉 Join Our Free Algorithmic Trading Workshop: learn.quantscience.io/become-a-pro-q…
• QSConnect: Build your quant research database
• QSResearch: Research and run machine learning strategies
• Omega: Automate trade execution
👉 Join Our Free Algorithmic Trading Workshop: learn.quantscience.io/become-a-pro-q…
That's a wrap! Over the next 24 days, I'm sharing my top 24 algorithmic trading concepts to help you get started.
If you enjoyed this thread:
1. Follow me @quantscience_ for more of these
2. RT the tweet below to share this thread with your audience
If you enjoyed this thread:
1. Follow me @quantscience_ for more of these
2. RT the tweet below to share this thread with your audience
https://twitter.com/1683526993059430411/status/1975537703690121551
P.S. - It took me 3 years to become confident in algorithmic trading.
So I spent 100 hours and made a free course to help others.
Join my free Algo Trading with Python Course + Roadmap here: startalgorithmictrading.com/beginners-algo…
So I spent 100 hours and made a free course to help others.
Join my free Algo Trading with Python Course + Roadmap here: startalgorithmictrading.com/beginners-algo…
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