Following the success of our $25,000 AI Trading Arena, we gathered some of the most innovative traders in the space for an in-depth discussion about AI quantitative strategies, indicator optimization, and market adaptation. Here are the critical insights from our second AMA session.
The Core Questions That Shape AI Trading
Our session tackled three fundamental questions that every AI trader faces:
How can AI quantitative strategies combine indicator information to genuinely improve win rates?
How can indicator providers become effective signals for AI models?
What's the next phase of AI quantitative trading?
Strategy Philosophy: The Path of Least Resistance
Benson from CoinKarma: Order Book Depth as Alpha
Benson revealed his core trading philosophy: "Price always moves toward the path of least resistance." His swing/oscillation trading system focuses on capturing turning points using a unique advantage: proprietary technology that maintains a full snapshot of the order book with 30-40% depth visibility.
This depth allows him to identify reversal points where the cost for whales to continue market manipulation exceeds potential profit. His "Outflow Analysis" strategy has achieved remarkable results, outperforming spot BTC by four times with a net value reaching 2.78 starting from 1.
Bruno from CoinSight: The Power of Social Sentiment
Bruno's approach combines crypto sentiment analysis with AI large models, building on social media sentiment indicators. His key innovation? The VOS (Volume of Sentiment) metric that gauges signal strength and timing.
His high-frequency strategy operates with strict risk management, assuming BTC/ETH won't fall below $800. Using leverage and capital to ensure maximum liquidation price stays below this threshold, his system maintained drawdown under 5% during recent market corrections and captured a 15% capital increase during a market surge.
Fan Xiang De Zhong: The DCA Master
This Post-00s trader employs a time-based DCA (Dollar Cost Averaging) strategy, splitting funds into four parts across high-frequency, medium-frequency, low-frequency, and active intervention components. His approach relies on the simple premise that fixed upward trend assets like BTC/ETH are profitable in the long run.
BQ Peter: Following the Trend
BQ Peter's quantitative signals primarily follow the trend, using blue/yellow arrows to indicate direction. His unique insight: a 2:1 ratio for long signals suggests a strong trend opportunity, particularly effective when combined with other indicators like RSI showing oversold conditions.
Handling Market Volatility: When AI Meets Black Swans
The October 11 Liquidation Event
Benson shared a crucial moment of human intervention during the October 11 super liquidation event. When BTC was around $103,000, he manually halved his long exposure from 50% to 25%, demonstrating "reverence for the market." This highlights that even sophisticated AI systems require human oversight during unprecedented events.
Version 4 and Beyond
Benson's strategy is now in Version 4, with each iteration handling risks from past black swan events. The system uses a volatility filter based on Average True Range (ATR). If volatility is too high (like a 15% daily drop), the strategy avoids entry until volatility subsides, preventing liquidation of leveraged positions.
Channel Trading in Volatile Markets
BQ Peter introduced his channel model approach: "Sudden market volatility like 'long-short double kill' is a natural phenomenon." His strategy uses channels/tunnels for stability. If price retracts into a descending channel, execute a short; stop loss if it fully breaks the channel. In an uptrend, if price pulls back into the ascending channel, execute a long; stop loss if it fully breaks the channel.
The key? Confirmation across multiple timeframes (15-minute and 1-hour). If directions differ, don't enter rashly.
The Three-Layer Testing Process for Indicators
Both Benson and Bruno emphasized rigorous indicator validation:
Layer 1: Big Data Modeling Backtest
Signals derived from deep learning must first demonstrate a basic win rate.
Layer 2: AI Logic Live Test
The AI LLM integrates the indicator signal with K-lines, fundamentals, and other metrics to formulate complete strategy and position management logic, validated through live running.
Layer 3: Cross-Model Testing
Compare execution across different AI models to find the optimal model for executing that specific strategy.
Benson stressed the importance of avoiding overfitting by slicing the time axis into distinct market regimes (Bear Market Rebound, Massive Bull Market, Bull Market Consolidation). An indicator must show effectiveness across these different regimes to be considered valid, not dismissed as coincidence.
Practical Indicator Recommendations
For Beginners: Start with RSI
Fan Xiang De Zhong recommends RSI as the most beginner-friendly indicator:
Set parameters to 14
View on 4-hour or daily charts (daily is most accurate)
For BTC/ETH, buying when RSI is below 30 is generally profitable
Understanding Market Sentiment with KDJ
BQ Peter explains KDJ (0-100 scale) reflects current market sentiment:
Above 70: Overbought, potential time to sell
Below 30: Oversold, good opportunity to buy the dip
Volume Analysis for Market Energy
BQ Peter's volume analysis revealed weaker bullish momentum compared to previous days, leading to his recommendation for a "low-long" strategy: waiting for BTC to pull back around $91,500 or ETH around $3,000-$3,100 before entering long positions.
Key Takeaways: The Evolution of AI Trading
Human Intervention Remains Critical: Even the most sophisticated AI systems need human oversight during unprecedented market events.
Composite Strategies Win: Using indicators in combination, comparing accuracy across three different indicators for specific price segments, yields better results than single-indicator approaches.
Risk Management Above All: Whether through position sizing, volatility filters, or predetermined liquidation thresholds, protecting capital remains paramount.
Backtesting Isn't Enough: Real market validation across different regimes and forward testing are essential to avoid overfitting.
Adapt to Market Conditions: Strategies must evolve through versions, incorporating lessons from each black swan event.
Looking Forward: The Next Phase
The future of AI quantitative trading lies not in replacing human judgment but in augmenting it. As our speakers demonstrated, the most successful strategies combine sophisticated data analysis, rigorous testing processes, and thoughtful human intervention when markets behave unexpectedly.
Whether you're building sentiment analysis models, order book depth strategies, or trend-following systems, the key is maintaining flexibility while adhering to strict risk management principles.
Disclaimer
Cryptocurrency investments are subject to high market risk and volatility. Please conduct your own research and invest cautiously.
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