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training - changed incentive to penalize wrong HOLD

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Dobromir Popov
2025-12-10 16:07:51 +02:00
parent 5349e23563
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HOLD_AVOIDANCE_INCENTIVE_SYSTEM.md Normal file
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# HOLD Avoidance Incentive System - Complete
## Problem Identified
The model was getting stuck in HOLD mode because:
1. **HOLD is "safe"** - never gets penalized for wrong predictions
2. **No incentive for action-taking** - only profit/loss matters
3. **Missing opportunity cost** - no penalty for missing profitable moves
4. **Overconfident HOLD** - model becomes confident in doing nothing
## Solution: Future Price Alignment Reward System
### 1. ✅ Future Price Alignment Rewards
**Incentivizes actions that align with future price movement:**
```python
# BUY Actions
if price_goes_up > 0.1%:
reward += up_to_5%_bonus
elif price_goes_down < -0.1%:
reward -= up_to_5%_penalty
# SELL Actions
if price_goes_down < -0.1%:
reward += up_to_5%_bonus
elif price_goes_up > 0.1%:
reward -= up_to_5%_penalty
# HOLD Actions
if price_stays_flat < 0.5%:
reward += small_bonus
else:
reward -= missed_opportunity_penalty
```
### 2. ✅ Action Diversity Penalty
**Discourages excessive HOLD actions:**
```python
# Every HOLD action gets small constant penalty
if action == 'HOLD':
reward -= 0.005 # Encourages action-taking
```
### 3. ✅ Missed Opportunity Penalties
**Penalizes HOLD when significant price moves occur:**
```python
if action == 'HOLD' and abs(price_change) > 0.5%:
penalty = -min(abs(price_change) / 100, 0.1) # Up to 10% penalty
reward += penalty
```
### 4. ✅ Confidence-Based Adjustments
**Higher penalties for overconfident wrong predictions:**
```python
if confidence > 0.8 and wrong_prediction:
penalty *= (1 + confidence) # Amplify penalty for overconfident mistakes
elif confidence > 0.8 and correct_prediction:
reward *= 1.2 # Small bonus for confident correct predictions
```
## Reward Structure Examples
### Scenario 1: BUY before 2% price increase
- **Base reward**: +2% (profit)
- **Alignment bonus**: +2% (correct direction)
- **Total**: +4% (strong positive reinforcement)
### Scenario 2: SELL before 2% price increase (wrong)
- **Base reward**: -2% (loss)
- **Alignment penalty**: -2% (wrong direction)
- **Confidence penalty**: -1% (if 90% confident)
- **Total**: -5% (strong negative reinforcement)
### Scenario 3: HOLD during 3% price increase (missed opportunity)
- **Base reward**: 0% (no trade)
- **Missed opportunity**: -3% (could have profited)
- **Diversity penalty**: -0.5% (discourages HOLD)
- **Total**: -3.5% (teaches to take action)
### Scenario 4: HOLD during 0.2% price change (correct)
- **Base reward**: 0% (no trade)
- **Correct HOLD bonus**: +0.5% (price stayed flat)
- **Diversity penalty**: -0.5% (constant HOLD penalty)
- **Total**: 0% (neutral, but not penalized)
## Expected Behavioral Changes
### 1. Reduced HOLD Bias
- **Before**: Model defaults to HOLD (safe option)
- **After**: Model considers opportunity cost of inaction
### 2. Better Action Timing
- **Before**: Random BUY/SELL timing
- **After**: Actions align with future price movements
### 3. Confidence Calibration
- **Before**: Overconfident in HOLD decisions
- **After**: Penalized for overconfident wrong predictions
### 4. Opportunity Recognition
- **Before**: Ignores profitable opportunities
- **After**: Learns to recognize and act on price movements
## Implementation Details
### Training Data Enhancement
Each training sample now includes:
- `entry_price` and `exit_price` for alignment calculation
- `confidence` level for confidence-based adjustments
- Future price movement analysis
- Opportunity cost calculations
### Reward Calculation Flow
1. **Calculate base reward** from actual profit/loss
2. **Add alignment reward** based on action vs future price
3. **Apply diversity penalty** for HOLD actions
4. **Adjust for confidence** level and correctness
5. **Combine all components** for final reward
### Logging and Debugging
```
Alignment reward: SELL with +1.50% move, conf=0.93 = -0.0245
Alignment reward: BUY with +2.10% move, conf=0.87 = +0.0252
Alignment reward: HOLD with +3.20% move, conf=0.65 = -0.0370
```
## Expected Results
### Short Term (1-2 hours)
- ✅ **Reduced HOLD frequency** - Model takes more actions
- ✅ **Better action timing** - Actions align with price movements
- ✅ **Improved confidence** - Less overconfident HOLD decisions
### Medium Term (1-2 days)
- ✅ **Higher profitability** - Better action selection
- ✅ **Reduced missed opportunities** - Acts on significant moves
- ✅ **Balanced action distribution** - Not stuck in HOLD mode
### Long Term (1+ weeks)
- ✅ **Adaptive behavior** - Learns market patterns
- ✅ **Risk-adjusted actions** - Considers opportunity costs
- ✅ **Optimal action frequency** - Right balance of action vs patience
The system now provides strong incentives for taking profitable actions while penalizing both wrong actions AND missed opportunities, breaking the HOLD bias!

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core/real_training_adapter.py
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@@ -1640,8 +1640,14 @@ class RealTrainingAdapter:
# Add experiences to replay buffer
for data in training_data:
# Calculate reward from profit/loss
reward = data['profit_loss_pct'] / 100.0 if data.get('profit_loss_pct') else 0.0
# Calculate reward from profit/loss + future price alignment
base_reward = data['profit_loss_pct'] / 100.0 if data.get('profit_loss_pct') else 0.0
# FUTURE PRICE ALIGNMENT REWARD: Incentivize actions that align with future price movement
alignment_reward = self._calculate_future_price_alignment_reward(data)
# Combine rewards: base profit + alignment bonus
reward = base_reward + alignment_reward
# Add to memory if agent has remember method
if hasattr(agent, 'remember'):
@@ -1664,6 +1670,68 @@ class RealTrainingAdapter:
# Accuracy calculated from actual training metrics, not synthetic
session.accuracy = None # Will be set by training loop if available
def _calculate_future_price_alignment_reward(self, data):
"""
Calculate reward based on how well the action aligns with future price movement
This incentivizes:
- BUY actions when price goes up
- SELL actions when price goes down
- HOLD actions when price stays flat
- Penalizes wrong actions (BUY before price drop, SELL before price rise)
"""
try:
action = data.get('action', 'HOLD')
entry_price = data.get('entry_price', 0)
exit_price = data.get('exit_price', 0)
if not entry_price or not exit_price or entry_price == exit_price:
return 0.0 # No alignment reward if no price movement
# Calculate actual price movement
price_change_pct = ((exit_price - entry_price) / entry_price) * 100
# Define alignment rewards
alignment_reward = 0.0
if action == 'BUY':
if price_change_pct > 0.1: # Price went up significantly
alignment_reward = min(price_change_pct / 100, 0.05) # Up to 5% bonus
elif price_change_pct < -0.1: # Price went down - wrong action
alignment_reward = max(price_change_pct / 100, -0.05) # Up to 5% penalty
elif action == 'SELL':
if price_change_pct < -0.1: # Price went down - correct action
alignment_reward = min(abs(price_change_pct) / 100, 0.05) # Up to 5% bonus
elif price_change_pct > 0.1: # Price went up - wrong action
alignment_reward = -min(price_change_pct / 100, 0.05) # Up to 5% penalty
elif action == 'HOLD':
if abs(price_change_pct) < 0.5: # Price stayed relatively flat - correct
alignment_reward = 0.005 # Small bonus for correct HOLD (reduced)
else: # Price moved significantly - missed opportunity
# Larger penalty for missing significant moves
missed_opportunity_penalty = -min(abs(price_change_pct) / 100, 0.1) # Up to 10% penalty
alignment_reward = missed_opportunity_penalty
# DIVERSITY PENALTY: Discourage excessive HOLD actions
# Add small penalty to HOLD to encourage action-taking
alignment_reward -= 0.005 # Small constant penalty for HOLD
# CONFIDENCE ADJUSTMENT: Higher penalties for confident wrong predictions
confidence = data.get('confidence', 0.5)
if confidence > 0.8 and alignment_reward < 0: # High confidence but wrong
alignment_reward *= (1 + confidence) # Amplify penalty for overconfident mistakes
elif confidence > 0.8 and alignment_reward > 0: # High confidence and right
alignment_reward *= 1.2 # Small bonus for confident correct predictions
logger.debug(f"Alignment reward: {action} with {price_change_pct:.2f}% move, conf={confidence:.2f} = {alignment_reward:.4f}")
return alignment_reward
except Exception as e:
logger.debug(f"Error calculating alignment reward: {e}")
return 0.0
def _build_state_from_data(self, data: Dict, agent: Any) -> List[float]:
"""Build proper state representation from training data"""
try:
@@ -3177,7 +3245,10 @@ class RealTrainingAdapter:
# Similar to DQN training
for data in training_data:
reward = data['profit_loss_pct'] / 100.0 if data.get('profit_loss_pct') else 0.0
# Calculate reward from profit/loss + future price alignment
base_reward = data['profit_loss_pct'] / 100.0 if data.get('profit_loss_pct') else 0.0
alignment_reward = self._calculate_future_price_alignment_reward(data)
reward = base_reward + alignment_reward
if hasattr(agent, 'remember'):
state = [0.0] * agent.state_size if hasattr(agent, 'state_size') else []