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Let me analyze the potential biases we've introduced into the system. # 2SD VWAP Trading System Checklist
## Setup Process
### 1. Initial Setup
- [ ] Open TradingView
- [ ] Create two charts side by side
- [ ] Left: ES1! 15-minute
- [ ] Right: BTC/USDT 15-minute
- [ ] Clear all existing indicators
```markdown ### 2. Add Indicators
# VWAP Trading System Bias Analysis - [ ] Add VWAP (standard settings)
- [ ] Add 2SD bands (using code provided)
- [ ] Add ATR (14 period)
- [ ] Make VWAP blue, upper band red, lower band green
- [ ] Verify indicators are updating correctly
## Statistical Biases ### 3. Alert Setup
- [ ] Create band touch alert (upper)
- [ ] Create band touch alert (lower)
- [ ] Set alerts to 'Once Per Bar'
- [ ] Include ATR value in alert message
- [ ] Test alerts are working
1. Normal Distribution Assumption ## Trade Process
### Pre-Trade Checklist
- [ ] Alert received
- [ ] Check which band was touched
- [ ] Note current ATR value
- [ ] Calculate position size (Risk/ATR)
- [ ] Verify pattern exists:
- [ ] Price touched band
- [ ] Clear rejection visible
- [ ] Room to VWAP exists
### Trade Execution
- [ ] Enter after rejection confirms
- [ ] Place stop beyond rejection point
- [ ] Set target at VWAP
- [ ] Record entry details:
- [ ] Entry price
- [ ] Position size
- [ ] Stop level
- [ ] Target level
- [ ] Monitor for pattern break
## Quick Reference
### Long Setup 🟢
* → Touch lower band
* → Rejection upward
* → Target: VWAP
* → Stop: Below rejection
### Short Setup 🔴
* → Touch upper band
* → Rejection downward
* → Target: VWAP
* → Stop: Above rejection
## Position Sizing Formula
``` ```
Current Assumption: Position Size = Account Risk / Current ATR
- 2SD captures 95% of price movement Example:
- Price returns to VWAP reliably - Risk: $100
- ATR: $2.50
Reality Check: - Position = $100/$2.50
- Markets aren't normally distributed
- Fat tails exist
- Black swan events occur
- VWAP reversion isn't guaranteed
``` ```
2. Sample Size Bias ## Pattern Requirements
``` 1. Clean move to band
Current Assumption: 2. Clear rejection
- 15-minute timeframe is optimal 3. Room to VWAP
- 15-period standard deviation is meaningful 4. No pattern = No trade
Reality Check: ## Remember
- Timeframe choice is arbitrary * Pattern exists or doesn't - no maybes
- Period selection lacks statistical validation * Use ATR for position sizing only
- Different assets may require different parameters * Exit at VWAP or pattern break
``` * Keep it simple
3. Look-Ahead Bias ---
``` Note: Check all boxes ([ ]) as you complete each step. Reset checklist for each new trading session.
Current Assumption:
- Rejections are clearly identifiable
- Pattern completion is obvious
Reality Check:
- Real-time pattern recognition is harder
- Clean rejections only visible after the fact
- Entry timing may vary
```
## Behavioral Biases
1. Confirmation Bias
```
Current Implementation:
- Looking for "clean" moves to bands
- Seeking "clear" rejections
- Wanting "obvious" patterns
Risk:
- Overlooking valid setups that look messy
- Overoptimizing for perfect patterns
- Missing profitable but imperfect trades
```
2. Recency Bias
```
Current Implementation:
- Assuming recent pattern success continues
- Expecting consistent behavior
Risk:
- Market regimes change
- Pattern effectiveness varies
- Success rate fluctuates
```
3. Overconfidence Bias
```
Current Implementation:
- "Pattern exists or doesn't"
- Fixed position sizing
- Predetermined targets
Risk:
- Markets are probabilistic
- No pattern is 100% reliable
- Risk doesn't remain constant
```
## Suggested Bias Mitigations
1. Statistical Adjustments:
```python
def calculate_position_size(self, atr, account_risk):
# Add market regime consideration
volatility_factor = self.get_volatility_regime()
adjusted_risk = account_risk * volatility_factor
# Non-normal distribution adjustment
tail_risk_factor = self.calculate_tail_risk()
return adjusted_risk / (atr * tail_risk_factor)
```
2. Pattern Recognition Improvements:
```python
def validate_pattern(self, df):
# Add probabilistic scoring
pattern_score = 0
# Price action validation (70%)
if self.validate_price_action(df):
pattern_score += 70
# Volume confirmation (30%)
if self.validate_volume(df):
pattern_score += 30
# Return confidence level instead of binary
return pattern_score / 100
```
3. Risk Management Enhancements:
```python
def calculate_stop_distance(self, atr, pattern_quality):
# Dynamic stop based on pattern quality
base_stop = atr * 1.5
# Adjust for pattern clarity
if pattern_quality < 0.8: # Less clear pattern
return base_stop * 1.2 # Wider stop
return base_stop
```
## Revised Core Principles
1. Replace Absolute Statements:
- Old: "Pattern exists or doesn't"
- New: "Pattern shows varying degrees of quality"
2. Acknowledge Uncertainty:
- Old: "Exit at VWAP"
- New: "VWAP is probable, not certain, target"
3. Dynamic Position Sizing:
- Old: "Fixed risk/ATR"
- New: "Adjust size based on pattern quality and market conditions"
4. Pattern Recognition:
- Old: "Clear rejection signals reversal"
- New: "Rejection suggests probable reversal with varying strength"
## Implementation Changes Needed
1. Add Pattern Quality Scoring:
```
- Price action clarity (40%)
- Volume confirmation (30%)
- Market context (30%)
```
2. Dynamic Position Sizing:
```
Position = (Account Risk * Pattern Quality) / (ATR * Market Regime Factor)
```
3. Flexible Targets:
```
- Primary: VWAP (60% probability)
- Secondary: 50% retracement (30% probability)
- Extended: Full band return (10% probability)
```
4. Progressive Stops:
```
- Initial: Based on pattern structure
- Trail: Adjusted for volatility
- Time: Maximum hold period
```
## Action Items
1. Add Pattern Quality Metrics:
- Score each setup objectively
- Track success by score
- Adjust criteria based on results
2. Implement Regime Detection:
- Monitor volatility changes
- Track pattern effectiveness
- Adjust parameters dynamically
3. Improve Position Sizing:
- Consider pattern quality
- Account for market regime
- Include correlation factors
4. Enhance Risk Management:
- Dynamic stop placement
- Multiple exit strategies
- Time-based risk reduction
The goal is not to eliminate bias (impossible) but to acknowledge and account for it in system design and execution.
```
Key Recommendations:
1. Statistical Improvements:
- Add regime detection
- Implement dynamic sizing
- Consider non-normal distributions
2. Pattern Recognition:
- Use quality scoring
- Remove absolute statements
- Add probability framework
3. Risk Management:
- Dynamic stop placement
- Multiple exit strategies
- Pattern quality adjustments
4. System Adjustments:
- Replace absolute rules with probabilities
- Add quality metrics
- Implement flexible sizing
- Consider market regimes
Would you like me to:
1. Detail specific implementation changes?
2. Create a bias-adjusted strategy framework?
3. Develop quality scoring metrics?
4. Design regime detection logic?
The goal is to maintain system simplicity while acknowledging and accounting for inherent biases.