A Bloomberg Terminal costs $30,000 a year.

Here's how to build 90% of it for free.

Wall Street doesn't advertise this. But every function that matters has a free alternative in Python.

Here's the DIY version (with Python): ━━━━━━━━━━━━━━━━━
🔹 REAL-TIME PRICES & CHARTS
━━━━━━━━━━━━━━━━━
Bloomberg: GP
Python: yfinance + Plotly

yfinance pulls live and historical price data for any ticker in seconds. Plotly turns it into interactive charts. Add your own indicators, overlays, and alerts. No subscription needed.

The secret of hedge funds is revealed in a 41-page PDF:

This paper analyzed 464 stocks that 10X-ed over a 24-year period.

Here are the best factors that drive outperformance: (number 3 is the best 🧵) 1. Size Effect

"Small-cap stocks outperform medium and large companies in 11 out of 12 cases"

Smaller stocks tend to perform better, but it's not the only contributor.

A 23-page research paper reveals the number 1 method Hedge Funds use to beat the market:

Time Series Momentum

This is how: 🧵 1. What Is Time Series Momentum?

Time Series Momentum (TSMOM) bets on trends continuing. If a stock’s up, buy more; if down, sell. A 2011 study of 58 assets proved it works!

🚨BREAKING: Python's Newest Algorithmic Trading Tool.

Introducing Nautilus Trader. 100% free.

Here's what it does (and how to get started in under 3 minutes): Nautilus Trader is an open-source Python library for production-grade algorithmic trading.

- Fast: made with Rust for blazing speed
- Natively runs with Python
- Risk management
- Backtesting
- Highly extendable

How to make your own algorithmic trading system in Python (a complete roadmap): Step 1: Learn Python

- Pandas: Data Analysis
- Scikit Learn: Machine Learning
- SQLAlchemy: Databases

Algorithmic trading is the domain of secretive Hedge Funds.

Python unlocked these secrets for everyone (even Goldman Sachs has an open-source tool).

Here are 17 Python libraries that open the hedge fund black box (📚bookmark this for later): 1. OpenBB Terminal - Terminal for investment research for everyone. github.com/OpenBB-finance…

2. PyQL - QuantLib's Python port. github.com/enthought/pyql

3. vollib - vollib is a Python library for calculating option prices, implied volatility, and greeks. github.com/vollib/vollib

Top 10 Algorithmic Trading Strategies (and how they work) 🧵 1. Pairs Trading

Trades two correlated instruments simultaneously. It goes long on one asset and short on the other to profit from deviations from their historical relationship, expecting the correlation to eventually resume.

Python is wild for finance.

You can get FinViz in Python for free (this is how):

(a thread) 1. What is finvizfinance?

finvizfinance is a package that collects financial information from FinViz website. It has:

- Stock charts, fundamental & technical information
- Insider information
- Stock news
- Forex charts
- Crypto charts

Here's some examples of what you can do:

🚨BREAKING: Microsoft open-sourced an AI Quant investment platform in Python

This is what you need to know:

(a thread) 1. What is Qlib?

Qlib is an open-source, AI-oriented quantitative investment platform that aims to realize the potential, empower research, and create value using AI technologies in quantitative investment, from exploring ideas to implementing production.

12 Python libraries for free market data everyone should know: 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…

A 23-page research paper reveals the number 1 method Hedge Funds use to beat the market:

Time Series Momentum

This is how: 🧵 1. What Is Time Series Momentum?

Time Series Momentum (TSMOM) bets on trends continuing. If a stock’s up, buy more; if down, sell. A 2011 study of 58 assets proved it works!

A Bloomberg Terminal costs $30,000 a year.

Here's how to build 90% of it for free.

Wall Street doesn't advertise this. But every function that matters has a free alternative in Python.

Here's the DIY version (with Python): ━━━━━━━━━━━━━━━━━
🔹 REAL-TIME PRICES & CHARTS
━━━━━━━━━━━━━━━━━
Bloomberg: GP
Python: yfinance + Plotly

yfinance pulls live and historical price data for any ticker in seconds. Plotly turns it into interactive charts. Add your own indicators, overlays, and alerts. No subscription needed.

This strategy has delivered 18% annual returns since 1926.

No black box. No complex ML model.

Just breakouts, trailing stops, and volatility sizing applied to industry portfolios.

Here's how it works (and link to the 37 page PDF): ─────────────────
🔹 THE ENTRIES
─────────────────
A long position triggers when an industry breaks above either:
• A Donchian Channel (20-day high, 40-day lower band)
• A Keltner Channel (20-day EMA ± 1.4× ATR, 40-day lower band)

The asymmetric lookback keeps you invested during sustained trends.
No short positions. When nothing is trending, capital sits in T-bills.

Most traders skip the math.

Then wonder why their backtests fail in live trading.

You don't need a math degree. But you need the right foundations.

Here's the complete math & stats roadmap for trading: ─────────────────
🔹 STATISTICS & PROBABILITY
─────────────────
• Sample Size & Law of Large Numbers — a 10-trade backtest proves nothing. You need hundreds before trusting your edge.
• Expected Value — the only number that actually matters: (Win% × Avg Win) - (Loss% × Avg Loss)
• Standard Deviation — becomes volatility when applied to returns. Every position sizing formula runs through this.
• Correlation — 3 momentum strategies on correlated assets isn't diversification. It's 3x the risk.
• Conditional Probability — your strategy's win rate changes by market regime. Know when it works.

🚨BREAKING: Microsoft open-sourced an AI Quant investment platform in Python

This is what you need to know:

(a thread) 1. What is Qlib?

Qlib is an open-source, AI-oriented quantitative investment platform that aims to realize the potential, empower research, and create value using AI technologies in quantitative investment, from exploring ideas to implementing production.

Stock Prediction AI: Using Machine Learning and Deep Learning to predict stock price movements in Python.

The Python code is 100% free on GitHub.

Let's dive in (bookmark this): 1. The Python Machine Learning and Deep Learning Libraries:

- mxnet
- gluon
- sklearn
- xgboost

Stop trading in Excel like it’s 2009.

Start using Python + ChatGPT.

Here's 7 small steps: 1. Pick ONE strategy rule (keep it boring)

Start with something simple like moving averages or a 12-1 momentum.

Your goal is execution and learning, not “printing money.”

Build an End-to-End Python Algorithmic Trading System (complete roadmap + skills + tools)

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

Learn these:

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

Top 10 Algorithmic Trading Strategies (and how they work) 🧵 1. Pairs Trading

Trades two correlated instruments simultaneously. It goes long on one asset and short on the other to profit from deviations from their historical relationship, expecting the correlation to eventually resume.

Python is wild for finance.

You can get FinViz in Python for free (this is how):

(a thread) 1. What is finvizfinance?

finvizfinance is a package that collects financial information from FinViz website. It has:

- Stock charts, fundamental & technical information
- Insider information
- Stock news
- Forex charts
- Crypto charts

Here's some examples of what you can do:

How to create your own "mini" hedge fund with algorithmic trading and Python

A thread 🧵 1. What is a Hedge Fund

Hedge funds pool money from wealthy individuals or institutions to seek higher, risk-adjusted returns across multiple markets.