Summary of Trading Bots Activity Overnight
Overview
The overnight monitoring period from the attached log file shows a fleet of three trading bots (bot_cl_crack_spread_arbitrage, bot_mbt_halving_call_ladder, and bot_mnq_ai_semiconductor) that experienced significant operational issues preventing any trade execution. The AI watchdog system (Minimax-M2.7-FW) continuously monitored the fleet and provided diagnostic recommendations throughout the period.
Key Events Timeline
Initial Status (Beginning of Log)
All three bots were connected and running
No entry/exit orders placed in 374-389 minutes initially
All three bots flagged for AI diagnosis due to inactivity
Bot-Specific Issues
1. bot_cl_crack_spread_arbitrage (CLV6 - Crude Oil)
Problem Identified:
No spread data published (spread field = null)
Execution timeframe (5 minutes) vs signal timeframe (60 minutes) created a 1:12 ratio instead of the recommended 1:6
No trades executed in 620+ minutes of operation
Root Cause:
The bot is designed to exploit geopolitical crack spreads between crude oil and refined products, but it’s only receiving single-leg CLV6 price data without the required spread information. Without spread data, the arbitrage logic cannot evaluate whether entry conditions are met.
Recommended Fix:
Implement an internal spread calculator using best bid/ask of the two legs
Lower the entry threshold to react to price-level changes
Align execution timeframe to approximately 10 minutes (1/6 of 60-minute signal)
Status: Regeneration recommended - YES
2. bot_mbt_halving_call_ladder (MBTU6 - Bitcoin Options)
Problem Identified:
Data feed stale at 132 seconds (significantly exceeding the 5-second threshold)
No options market data available (option_quote_count = 0)
IV at 100th percentile (extremely high) but realized volatility at only 0.25th percentile
Zero bid-ask spread (bid = ask = price)
No trades executed in 635+ minutes of operation
Root Cause:
The bot is designed to execute a call ladder strategy around Bitcoin halving events, relying on live options market data to calculate Greeks and trigger entries. However, the options market for MBTU6 appears to be either non-existent or extremely illiquid, preventing the bot from constructing the ladder or generating any signals.
Recommended Fix:
Add a data-freshness monitor that forces a gateway reconnect if tick age exceeds 5 seconds
Switch to a more actively quoted options series or replace with futures-based halving trigger
Increase lookback bars to capture enough history for the ladder calculation
Status: Regeneration recommended - YES
3. bot_mnq_ai_semiconductor (MNQQ6 - Nasdaq Futures)
Problem Identified:
Execution timeframe (5 minutes) too long relative to signal timeframe (15 minutes) - ratio 1:3 instead of recommended 1:6
Insufficient lookback period (20 bars) for reliable signal detection
Extremely low proxy_vix readings (0.72-2.06) indicating ultra-low volatility environment
No signals generated (signal_reason = NONE)
No trades executed in 619+ minutes of operation
Root Cause:
The bot is designed to capture AI-driven semiconductor disruptions on the Micro Nasdaq-100 futures using momentum-based signals. However, the current low-volatility environment and insufficient historical data (20 bars) prevent the strategy from generating actionable signals. The lookback period may be too short for the AI semiconductor theme to develop meaningful trends.
Recommended Fix:
Reduce execution timeframe to approximately 2.5 minutes (1/6 of 15-minute signal)
Increase lookback bars to at least 40-50 bars for more reliable indicator calculations
Add a volatility filter requiring proxy_vix ≥ 2.0 before allowing entries
Status: Regeneration recommended - YES
Fleet-Wide Analysis
Risk Assessment
No 2:1 win/loss rule breaches - All bots had zero trades, so no losses or wins recorded
No circuit breakers tripped - No automatic stoppages triggered
Primary risk: Opportunity cost from non-productive capital deployment and potential for unexpected clustering of orders if conditions suddenly align
Kelly Sizing
All bots had unknown win rates, making it impossible to compute meaningful Kelly fractions. The AI recommended:
Sizing down to minimum contract counts (1 contract per bot)
Pausing any size increases until at least 30 trades are collected to establish reliable win-rate estimates
Data Quality Issues Identified
Spread data missing for CLV6 - preventing crack spread arbitrage logic
Stale data feed for MBTU6 - 132 seconds vs 5-second threshold
No options market data for MBTU6 - option_quote_count = 0
Ultra-low volatility across all bots - proxy_vix consistently below 2.0
Timeframe Misalignments
CLV6: 1:12 ratio (should be 1:6)
MNQQ6: 1:3 ratio (should be 1:6)
MBTU6: Execution timeframe not specified, requiring verification
Diagnostic Recommendations Provided
Regeneration Actions Required
bot_cl_crack_spread_arbitrage: Implement spread calculation logic, widen entry thresholds, align execution to 10 minutes
bot_mbt_halving_call_ladder: Add data-freshness monitors, ensure live options feed or switch to futures
bot_mnq_ai_semiconductor: Increase lookback to 40+ bars, reduce execution to 2.5 minutes, add volatility filter
Parameter Adjustments Needed
All bots should have explicit daily and weekly loss limits defined
Data age thresholds should be enforced (max 5 seconds)
Volatility filters should be implemented to prevent trading in unfavorable conditions
Position sizing should remain at minimum until win rate data is collected
Final Outcome
By the end of the monitoring period (04:51 UTC), all three bots remained idle with no trades executed. The AI watchdog system continued to monitor and recommend parameter adjustments. The fleet required:
Immediate reconnection of market data feeds
Parameter updates to align timeframes and entry thresholds
Instrument changes where market conditions are unsuitable
Implementation of proper risk controls before resuming operations
The primary conclusion was that the bots were not failing due to technical issues but rather due to market conditions and configuration mismatches that prevented signal generation. Resolving data quality issues and adjusting strategy parameters should enable the fleet to become productive once market conditions improve.