Here is the link → developers.google.com/edu/python

(Link in next 🧵🪡)
🔥 Want to learn building Algo Trading in Python FREE?

👉 Grab HERE:www.quanthustle.com/python-opt?...

The full paper is here → arxiv.org/pdf/2304.07619

📈 Strategy performance from 2022-01 to 2024-01. Short strategy with bad news outperforms the long strategy with good news.

📝 Prompt ChatGPT to classify news as 'YES/NO/UNKNOWN' for stock impact. Scores translated to buy/sell signals.

How should ChatGPT be used for trading? This paper can be the answer

The competition feed in market to GPT-5 for trading, it may be wrong.
Market data is past information that has no predicting power.

Just like whether you will predict tomorrow temperature using SMA, MACD, EMA, etc indicator on past temperature to predict it?

Find good instruments with least correlation

Calculate the returns and identify 10-15 uncorrelated streams

(Python code in next 🧵🪡)
🔥 Want to learn building Algo Trading in Python FREE?

👉 Grab HERE:www.quanthustle.com/python-opt?...

You’ll learn
● Portfolio construction
● Portfolio Theory
☆ Risk Parity Portfolio (Ray Dalio All Weather portfolio) 👍👍👍

🔗 www.youtube.com/watch?v=8TJ...

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7️⃣ Refinement
• Analyze the reasons of underperformance strategy
• Try to go through the whole process again for enhancement
• Document lessons learned for future strategy development

6️⃣ Live Trading
• Start with a small capital allocation to minimize risk during initial deployment.
• Monitor execution including fill rates, slippage, and transaction costs.
• Monitor the live trading performance and compare with backtesting metrics

5️⃣ Paper Trading
• Deploy the strategy in paper trading environment
• Monitor performance and compare results with backtesting to identify discrepancies
• Conduct real world trading for a certain of period to ensure strategy is working well

4️⃣ Backtesting
• Simulate the strategy over the data prepared in step above
• Measure the key performance metrics like Sharpe ratio, MDD, annualized return, profit ratio
• Conduct out-sample testing, walk-forward analysis, cross-validation to avoid overfitting