Feasability Query

 19/01/25    7:47 pm

I have downloaded and installed the new EA31337 botand am educating myself in it's use.  It is very comprehensive with multi-timeframes and multi-strategies.  I ran it without any optimisation on the JPYNZD and it did about as well as mine does.  It does of course need optimisation first since by default it is optimised for the EURUSD..

What I have researched also is the feasability of capabilities for these bots just to see generally what we should expect from them and what others are achieving.  The results are very interesting. There is a table summarizing them at the end.  After you've read the first block you can probably get the gist and skip to the table at the end otherwise it's a good read.


Forex Trading:

Here’s a ballpark range of general average performance metrics for algorithmic currency trading bots, including pre-AI and post-AI developments:

Pre-AI Era:

  1. Profit/Loss Ratios (P/L):

    • Typically ranged from 1.2:1 to 1.5:1 for well-tuned strategies.
    • Less sophisticated bots could have struggled to maintain a break-even point due to outdated algorithms or data lag.

  2. Win Rates:

    • Around 40%-55% for trend-following and breakout strategies.
    • Higher-frequency mean-reversion bots might have seen 55%-65% win rates.

  3. Annual Returns:

    • Average returns ranged from 5%-20%, depending on leverage, market conditions, and strategy risk.

  4. Drawdowns:

    • Max drawdowns typically 15%-30%, with less advanced bots struggling in volatile or range-bound markets.

Post-AI Integration:

  1. Profit/Loss Ratios (P/L):

    • Improved to 1.8:1 to 2.5:1 for bots leveraging deep learning and real-time data.
    • AI-enabled systems dynamically adapt to changing market conditions, improving risk/reward metrics.

  2. Win Rates:

    • Increased to 50%-65% for AI-augmented systems.
    • Advanced predictive models and machine learning can achieve up to 70% in highly optimized environments.

  3. Annual Returns:

    • High-performing bots generate 15%-40%+, especially in volatile markets.
    • Conservative AI-driven bots aim for 8%-12% with lower risk.

  4. Drawdowns:

    • Reduced to 10%-20% for optimized AI systems due to better risk management algorithms.

  5. Sharpe Ratios (Risk-Adjusted Returns):

    • Improved from 0.7-1.2 (pre-AI) to 1.5-2.5 in AI-enhanced systems, reflecting more consistent performance.

General Observations:

  • High-Frequency Trading (HFT):
    Algorithms executing multiple trades per second tend to have higher win rates (>90%) but razor-thin profit margins per trade.

  • Retail Market Impact:
    Retail bots often underperform compared to institutional-grade systems. Many fail due to overfitting, lack of robust data, or market slippage.

  • AI Revolution:
    Post-AI systems outperform in chaotic or complex markets but are still subject to unexpected geopolitical or black-swan events.

These figures are broad averages. Individual performance depends on the sophistication of the algorithm, market conditions, and the trader's skill in adapting systems to real-world challenges.


Stock Trading:

Pre-AI Era:

  1. Profit/Loss Ratios (P/L):

    • Ranged from 1.1:1 to 1.4:1 for momentum or trend-following strategies.
    • Market-neutral strategies often targeted 1.2:1 due to hedging.

  2. Win Rates:

    • Generally 45%-55% for swing trading or position-based bots.
    • Day trading algorithms might achieve 55%-65%, depending on volatility.

  3. Annual Returns:

    • Moderate bots achieved 8%-15% annually.
    • High-risk strategies could push 20%-30%, though with increased drawdowns.

  4. Drawdowns:

    • Typically 15%-25%, with long-only bots suffering in bear markets.
    • Hedged bots showed 10%-15% drawdowns but often lagged in bull runs.

  5. Sharpe Ratios:

    • Averaged around 0.6-1.0, reflecting less efficient risk/reward balance compared to modern AI systems.

Post-AI Integration:

  1. Profit/Loss Ratios (P/L):

    • Increased to 1.5:1 to 2.2:1, leveraging real-time sentiment analysis, deep learning, and multi-asset correlation models.

  2. Win Rates:

    • 50%-65% for long/short equity bots with AI.
    • AI-driven scalping bots can achieve 70%-80% win rates, compensating with smaller profits per trade.

  3. Annual Returns:

    • High-performing AI-driven bots achieve 15%-40%+, particularly in volatile or sector-focused trading.
    • Conservative bots aim for 10%-20% with enhanced risk management.

  4. Drawdowns:

    • Reduced to 10%-20% for AI bots, benefiting from advanced stop-loss mechanisms and adaptive learning.
    • AI excels in recognizing regime changes (e.g., bull/bear markets) to limit downside.

  5. Sharpe Ratios:

    • Improved to 1.5-2.5 for AI-enhanced systems.
    • Indicates more consistent risk-adjusted returns across various market cycles.

Stock-Specific Strategies:

  1. Momentum and Trend-Following:

    • Pre-AI win rates were 50%-55%, now improved to 60%-70% with AI that better identifies price patterns.

  2. Mean Reversion:

    • Pre-AI annual returns were 5%-12%, now reaching 12%-20%+ with AI-enabled optimization.
    • AI helps detect oversold/overbought conditions more reliably.

  3. Event-Driven Trading:

    • AI has revolutionized this space, analyzing earnings, news, and corporate actions in real-time.
    • Win rates improved from 45%-50% to 55%-65%.

General Observations:

  • Retail Traders:
    Retail stock trading bots have limited success, with average annual returns ranging from 5%-15% pre-AI and 10%-20% post-AI.

    • Often limited by data access and execution speed.

  • Institutional Traders:
    Institutions using AI systems dominate, with bots contributing to 60%-80% of equity trading volume in U.S. markets post-2020.

  • AI Advancements:
    AI excels in sentiment analysis (e.g., social media/news), sector rotations, and volatility hedging.

  • Risks:
    Despite improvements, stock bots remain vulnerable to black swan events, low liquidity, or market regulation changes.

These figures are indicative of industry averages and can vary based on individual strategies, market conditions, and levels of sophistication.


Crypto Trading:

Given the unique characteristics of cryptocurrency markets (24/7 operation, high volatility, and speculative trading), the performance metrics for trading bots differ significantly from traditional asset classes.

Pre-AI Era:

  1. Profit/Loss Ratios (P/L):

    • Averaged 1.2:1 to 1.5:1 due to high volatility and manual strategy adjustments.
    • Lower for arbitrage bots, typically around 1:1 to 1.2:1.

  2. Win Rates:

    • Swing trading bots: 40%-55%.
    • Arbitrage bots: 70%-90% success rate on opportunities, but profits were slim due to fees and slippage.

  3. Annual Returns:

    • Aggressive strategies: 30%-50%+, driven by volatility.
    • Conservative strategies: 10%-20%, with a focus on stablecoins or arbitrage.

  4. Drawdowns:

    • Typically 25%-40%, reflecting the market's inherent volatility.
    • Hedging was less effective in pre-AI bots due to limited predictive modeling.

  5. Sharpe Ratios:

    • Averaged 0.5-1.0, indicating high risk per unit of return.
    • Market downturns (e.g., 2018 crypto winter) heavily impacted performance.

Post-AI Integration:

  1. Profit/Loss Ratios (P/L):

    • Improved to 1.8:1 to 2.5:1, driven by advanced market sentiment analysis and real-time anomaly detection.

  2. Win Rates:

    • Scalping bots: 75%-85%, leveraging AI for rapid execution.
    • Momentum bots: 55%-65%, enhanced by neural networks identifying breakout patterns.

  3. Annual Returns:

    • High-frequency trading (HFT): 20%-50%+ annually.
    • Long/short strategies with AI: 30%-100%, especially during bull markets.
    • Conservative risk-managed bots: 10%-25%, suitable for prolonged bear markets.

  4. Drawdowns:

    • Reduced to 15%-30%, with AI-driven bots adapting dynamically to volatility.
    • Hedging through derivatives (e.g., perpetual futures) improved stability.

  5. Sharpe Ratios:

    • Improved to 1.2-2.0, reflecting better risk-adjusted returns even in volatile markets.

Crypto-Specific Strategies:

  1. Arbitrage Trading:

    • Pre-AI: Profitable opportunities often required manual adjustments; returns were 5%-15% annually.
    • Post-AI: Returns improved to 15%-30% annually, with bots leveraging machine learning to optimize timing and minimize fees.

  2. Market-Making:

    • Pre-AI: Margins were slim, with returns of 5%-10%.
    • Post-AI: Advanced liquidity models yield 10%-25%, reducing exposure during market crashes.

  3. Momentum and Breakout Trading:

    • Pre-AI: 20%-40% annual returns during bull markets.
    • Post-AI: Returns of 30%-100% as AI improves entry/exit accuracy and manages risk better.

  4. Sentiment-Based Trading:

    • AI bots excel here, analyzing social media, news, and blockchain activity.
    • Typical annual returns are 20%-50% with moderate risk.

General Observations:

  • Retail Traders:

    • Pre-AI: Retail bots often underperformed, with annual returns of 5%-15%, due to slippage, fees, and lack of institutional-grade tools.
    • Post-AI: Retail bot performance improved to 15%-40%, leveraging pre-built AI systems.

  • Institutional Traders:

    • Institutions dominate HFT and market-making in crypto, often achieving annual returns of 20%-50%+, with superior infrastructure.

  • AI Advancements:

    • Real-time anomaly detection and sentiment analysis provide a competitive edge.
    • AI bots excel in managing diverse portfolios, rebalancing dynamically across assets like BTC, ETH, and altcoins.

  • Risks:

    • Crypto's extreme volatility can lead to catastrophic losses without strict risk management.
    • Regulatory changes and exchange hacks remain significant concerns.

Summary:

Crypto bots post-AI generally outperform traditional bots due to advanced analytics, faster execution, and better adaptability. However, the market's inherent risks make robust risk management essential. Average annual returns for AI-driven crypto bots range from 15%-100%, depending on risk tolerance and strategy sophistication.



Detailed Pre AI Statistics of All Trading Types

+----------------------------------+-------------------+-------------------+---------------------+-------------------+

| Type                                  | P/L Ratios       |  Win Rates     | Annual Returns | Drawdowns    |

+----------------------------------+--------------------+------------------+---------------------+-------------------+

| Forex                                  |  1.2:1 to 1.4:1  |  40%-55%          | 5%-15%         | 15%-35%         |

| Stocks                                |  1.2:1 to 1.5:1  |  50%-60%          | 5%-15%         | 10%-20%         |

| Crypto                                |  1.1:1 to 1.3:1  |  30%-50%          | 20%-50%       | 40%-70%         |

| Commodity                         |  1.2:1 to 1.5:1  |  40%-60%          | 10%-20%       | 20%-30%         |

| Options                                |  1:1 to 1.3:1     |  30%-50%          | 5%-15%         | 20%-50%         |

| Fixed Income (Bonds)         |  1.1:1 to 1.3:1  |  60%-80%          | 3%-8%          | 5%-15%           |

| Real Estate (REITs)             |  1.2:1 to 1.4:1  |  50%-60%          | 5%-12%         | 15%-25%         |

| Futures                                 |  1.3:1 to 1.5:1  |  45%-60%          | 15%-30%       | 25%-40%         |

| Emerging Markets                |  1.1:1 to 1.3:1  |  40%-55%          | 8%-20%         | 30%-50%         |

+-------------------------------------+--------------------+----------------------+-------------------+--------------------+


Detailed Post AI Statistics of All Trading Types

+---------------------------------+--------------------------+----------------------+---------------------+--------------------+

| Type                                |  P/L Ratios              |  Win Rates         | Annual Returns | Drawdowns     |

+---------------------------------+--------------------------+----------------------+---------------------+-------------------+

| Forex                                 |  1.5:1 to 2:1         |  55%-70%          | 10%-30%         |  15%-35%         |

| Stocks                               |  1.5:1 to 2.5:1      |  55%-65%          | 10%-25%        |  10%-20%         |

| Crypto                               |  1.5:1 to 2.5:1      |  50%-70%          | 30%-60%        |  40%-70%         |

| Commodity                       |  2:1 or higher      |  55%-70%          | 15%-40%        |  15%-30%        |

| Options                              |  1.5:1 to 2:1          |  50%-70%          | 10%-30%        |  20%-50%         |

| Fixed Income (Bonds)       |  1.5:1 or higher     |  70%-90%          | 5%-15%          |  5%-15%           |

| Real Estate (REITs)           |  1.6:1 or higher     |  60%-75%          | 8%-15%          |  15%-25%         |

| Futures                              |  1.8:1 to 2.2:1        |  55%-70%          | 20%-50%        |  25%-40%         |

| Emerging Markets             |  1.5:1 to 2:1           |  50%-65%          | 15%-30%        |  30%-50%         |

+----------------------------------+-----------------------+----------------------+--------------------+--------------------+





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