
Quant Science
@quantscience_
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Develop profitable trading strategies, build a systematic trading process, and trade your ideas with Python—even if you’ve never done it before.
Free Python newsletter 👉
Joined July 2023
🚨 LIVE Python Algo Trading Workshop: Learn how we built our hedge fund • QSConnect: Build your quant research database • QSResearch: Research and run machine learning strategies • Omega: Automate trade execution with Python 👉 Get the system: https://t.co/a8hBLutD7I
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How to create your own "mini" hedge fund with algorithmic trading and Python A thread 🧵
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P.S. - Want Algorithmic Trading with Python tutorials every Sunday? Register here to join our Sunday Quant Scientist Newsletter (it's free):
learn.quantscience.io
The smart way to learn algorithmic trading, investing, and stock portfolio analysis.
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Things you DON'T need to start algo trading: • C programming • 100s of strategies • $1,000,000 Things you DO need to start algo trading: • Python • IBKR • A $500 computer Want to learn how? 👉 Join Our Free Algorithmic Trading Workshop: https://t.co/a8hBLutD7I
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FTMO Verified prop traders walk their path. Rise now and start your FTMO Challenge!
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4 lines of Python code is what it takes to have your own financial advisor. Fully open source (this is how):
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P.S. - Want to learn Algorithmic Trading Strategies that actually work? I'm hosting a live workshop. Join here:
us02web.zoom.us
Algorithmic Trading in Python: From Strategy to Trade Execution Learn how to develop trading strategies, backtest them, and execute the strategies all in Python. Inside the LIVE EVENT, we will...
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That's a wrap! 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
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🚨How I built my algorithmic trading system in Python (for Free) • QSConnect: Build your quant research database • QSResearch: Research and run machine learning strategies • Omega: Automate trade execution  👉 Register here (790+ registered): https://t.co/a8hBLutD7I
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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: 👇
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Next: Analyze metrics: Regularly monitoring performance, drawdowns, and market conditions is critical for refining your strategy portfolio over time.
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6. Tracking Performance and Grouping Strategies: Maintain a portfolio of several strategies. Group them by market type (e.g., equities vs. commodities) or by aggressiveness (e.g., “conservative” vs. “aggressive”).
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5. Learn Python Python Quant Stack is 100% free (and covers data, analysis, research, backtesting, and execution): OpenBB $0 Pandas $0 NumPy $0 Zipline $0 AlphaLens $0 VectorBT $0 Riskfolio $0 IBAPI $0
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Long/Short Pairs: Having both bullish and bearish strategies for each market (e.g., long ES, short ES, long CL, short CL) offers additional diversification and helps hedge exposure.
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Mean Reversion Strategies: Identify when prices deviate from an average or band (like Bollinger Bands) and expect prices to revert back.
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4. Algorithmic Strategy Ideas: Momentum Strategies: Buy (go long) when the price is above a long-term moving average (e.g., 200-day SMA) or sell (go short) when below. This aims to catch trends.
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Example: If equity markets are falling (S&P 500 futures, “ES”), another market like oil (“CL”) might be trending up, which could offset losses.
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3. Choose Your Markets: Trading across different asset classes (e.g., equities, commodities, futures) can reduce overall risk through diversification.
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Extremely high returns (e.g., 100% per year) can be possible but come with huge drawdowns (50–70%), which most investors find difficult to handle psychologically.
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2. Define Your Goals Decide on a target annual return and understand the drawdown (potential loss) you can tolerate. For instance, aiming for ~20% annual returns may entail accepting a ~10% drawdown.
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While they often strive to outperform benchmarks like the S&P 500, the focus is usually on lowering risk (drawdowns) rather than purely maximizing returns.
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1. What is a Hedge Fund Hedge funds pool money from wealthy individuals or institutions to seek higher, risk-adjusted returns across multiple markets.
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