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Oct 7, 2025 11 tweets 4 min read Read on X
Build an End-to-End Python Algorithmic Trading System (complete roadmap + skills + tools)

Bookmark this. Image
1) Foundations (1–2 weeks)

Learn these:

1. Python basics → data/ML
2. Pandas (data wrangling)
3. Scikit-learn (ML)
4. SQLAlchemy (DB access) Image
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. Image
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: Image
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 Image
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) Image
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 Image
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: 👇
🚨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…Image
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
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…

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More from @quantscience_

Feb 10
A strategy with a Sharpe of 0.94 (and 513.8% cumulative return): Image
A strategy only Goldman Sachs can trade?

Nope.

It's a VIX-based futures strategy that demolishes the benchmark just like the pros.

Here's how:
SigTech.

Here's a primer in case you're unfamiliar with SigTech:

• Quant tech for institutional investors
• Provides data and tools to hedge funds
• Customers with $5 trillion in assets under management

SigTech was built for institutions. Now you can have it too:
Read 14 tweets
Feb 6
How to make your own algorithmic trading system in Python (a complete roadmap): Image
Step 1: Learn Python

- Pandas: Data Analysis
- Scikit Learn: Machine Learning
- SQLAlchemy: Databases
Step 2: Set up your financial database

Financial Data:
- Price: Yahoo Finance (free)
- Fundamentals: Financial Modeling Prep (paid)

Database:
- DuckDB (free) Image
Read 10 tweets
Feb 2
Automate your trading strategies in Python

How to build your first trading bot:

(a thread) Image
1. What is a trading bot?

A trading bot is a software program that automates buying and selling financial assets like stocks and cryptocurrencies based on pre-defined strategies and rules.

These automated systems can manage portfolios without human intervention, operating 24/7.
2. Let's make a Bitcoin Trading bot

We'll use investing-algorithm-framework in Python Image
Read 12 tweets
Feb 1
How to make a simple algorithmic trading strategy with a 472% return using Python.

A thread. 🧵 Image
This strategy takes advantage of "flow effects", which is how certain points in time influence the value of an asset.

This strategy uses a simple temporal shift to determine when trades should exit relative to their entry for monthly boundary conditions. Image
The signals for when to go short, when to cover shorts, when to go long, and when to close longs are all linked to these recurring monthly cycles.

This periodic "flow" of signals—month-in, month-out—creates a systematic pattern. Image
Read 11 tweets
Feb 1
12 Python libraries for free market data everyone should know: Image
yfinance

Data for stocks (historic, intraday, fundamental), FX, crypto, and options. Uses Yahoo Finance so any data available through Yahoo is available through yfinance.

github.com/ranaroussi/yfi…
pandas-datareader

pandas-datareader used to be part of the pandas project. Now an independent project. Includes data for stocks, FX, economic indicators, Fama-French factors, and many others.

pandas-datareader.readthedocs.io/en/latest/
Read 17 tweets
Jan 30
How to bootstrap your own mini hedge fund in 2026 (Learn these skills): Image
Skills to begin with (ranked in order of importance):

1. Python
2. Pandas
3. Numpy
4. Plotly
5. Scikit Learn

This is your Python foundation.

Then learn these: 👇 Image
Next, add these skills for financial data analysis in Python:

6. yfinance
7. zipline
8. vectorbt
9. pytimetk
10. IBKR

Want to learn how?

I have a free training: 👇 Image
Read 6 tweets

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