some of Arbitrage Strategy you can learn and make a robot out of it

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Statistical Arbitrage Strategy:
Concept: Statistical arbitrage, also known as stat arb, involves exploiting short-term price discrepancies between two or more correlated assets. This strategy assumes that the historical relationship between the assets will reassert itself, allowing traders to profit from the convergence of prices.
Strategy Steps:

1. Pair Selection: Choose a pair of assets that are highly correlated, meaning their prices tend to move together over time.
2. Calculation of Deviation: Calculate the historical relationship between the two assets, typically using statistical measures such as cointegration or correlation. Determine the historical spread or deviation from the established relationship.
3. Signal Generation:
   • Entry Signal: When the spread between the two assets diverges from the historical relationship by a significant amount, generate an entry signal.
   • Exit Signal: When the spread approaches or converges back to its historical average, generate an exit signal.
4. Risk Management:
   • Implement stop-loss orders to limit potential losses in case the spread continues to widen beyond expectations.
   • Consider position sizing techniques to allocate capital based on the spread's historical volatility.
5. Execution:
   • Open a long position in the underperforming asset of the pair and a short position in the outperforming asset when the entry signal is generated.
   • Close the positions when the exit signal is generated.
6. Monitoring and Adjustment:
   • Continuously monitor the spread between the assets and the performance of the positions.
   • Adjust the strategy's parameters if necessary based on changes in correlation or market conditions.

Advantages:

• Statistical arbitrage strategies aim to capture short-term price discrepancies, potentially generating profits regardless of market direction.
• They can be automated and systematic, reducing emotional biases in decision-making.

Challenges:

• Successful execution requires accurate modeling of the relationship between the assets, which might not always hold true in changing market conditions.
• Quick and unexpected market events can lead to prolonged deviations from historical relationships, resulting in losses if the assets don't converge as expected.

As with any quantitative trading strategy, extensive back testing on historical data is crucial to evaluate the strategy's performance and robustness. Adequate risk management techniques are essential to mitigate potential losses if the spread continues to diverge instead of converging.