📌 Key Concepts
| Term | Explanation |
|---|---|
| Quantitative Trading | Trading based on mathematical models, statistics, and data. |
| Algorithmic Trading | Automating the execution of trades using computer algorithms. |
| Algorithm | A step-by-step set of instructions that the computer follows. |
🔍 What is Quantitative Trading?
Quantitative Trading (or "Quant trading") uses:
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Historical data (prices, volumes, indicators),
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Mathematics and statistics,
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To create models that suggest when to buy or sell an asset.
Example model:
If the stock price falls below the 50-day moving average and volume increases — buy.
🤖 What is Algorithmic Trading?
Algorithmic Trading is when strategies are programmed, and the computer automatically:
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Monitors the market,
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Executes trades based on pre-defined conditions,
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Does this quickly, precisely, and without emotion.
These systems are used by both large banks and individual traders.
⚙️ How Does It Work?
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Data Collection – Historical prices, volumes, and indicators.
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Strategy Development – Creating a model for price behavior.
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Backtesting – Testing the strategy on past data.
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Algorithm Coding – Written in Python, R, C++, etc.
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Live Trading – Automated trades in real-time.
🔑 Popular Quantitative Strategies
| Strategy Name | Explanation |
|---|---|
| Mean Reversion | The price returns to the average (e.g., after a sharp rise). |
| Momentum | If the price moves in one direction, it is likely to continue (inertia). |
| Statistical Arbitrage | Exploiting price discrepancies between correlated assets. |
| Pair Trading | Trading two similar assets (e.g., Coca-Cola vs Pepsi). |
| High-Frequency Trading | Trading in milliseconds, with thousands of trades per second. |
✅ Advantages
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No emotions, decisions are based strictly on data.
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High-speed execution of trades.
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Ability to analyze large volumes of data.
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Can operate 24/7 automatically.
⚠️ Disadvantages
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Requires knowledge of programming and mathematics.
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Poor models can lead to significant losses.
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Strong competition from large institutions and funds.
💡 What Should Beginners Know?
Programming Languages:
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Python – the best choice for beginners (libraries: Pandas, NumPy, Backtrader).
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R – excellent for statistical analysis.
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Excel + VBA – for basic strategies.
What to Study:
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Statistics: variance, correlation, z-scores.
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Programming basics.
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Financial market mechanics.
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Strategy testing and optimization.
🧠 Practical Example (Python Pseudo-code):
if current_price < moving_average_50 and volume > average_volume:
buy()
📝 Conclusion:
Quantitative and Algorithmic Trading is an intelligent approach to investing. By combining science and technology, traders receive accurate signals, fast execution, and are independent of emotions.