flexible momentum analyzers explained + min_volume for each coin

Author: scripting-drafts

Research/README.md

@@ -0,0 +1,173 @@
+# Momentum Flexibility Research Scripts
+
+This directory contains research scripts that reproduce and visualize the intelligent momentum detection system implemented in the trading automation bot.
+
+## 📊 Scripts Overview
+
+### 1. `momentum_visualizer.py` (Graphical Version)
+
+- **Requirements:** matplotlib, seaborn, numpy, pandas
+- **Features:** Interactive charts and graphs
+- **Output:** Visual plots showing momentum analysis
+- **Install dependencies:** `pip install -r requirements_viz.txt`
+
+### 2. `momentum_analyzer.py` (Console Version) ✅
+
+- **Requirements:** Built-in Python libraries only
+- **Features:** Comprehensive console-based analysis
+- **Output:** Detailed text reports and statistics
+- **Ready to run:** No additional dependencies needed
+
+## 🚀 Quick Start
+
+Run the console analyzer (recommended):
+
+```bash
+python momentum_analyzer.py
+```
+
+Or install dependencies and run the graphical version:
+
+```bash
+pip install -r requirements_viz.txt
+python momentum_visualizer.py
+```
+
+## 🎯 What These Scripts Demonstrate
+
+### Dynamic Momentum System Features
+
+1. **ATR-Based Threshold Adaptation**
+   - Automatically adjusts based on market volatility
+   - Higher ATR → Higher thresholds (fewer false signals)
+   - Lower ATR → Lower thresholds (catches small moves)
+
+2. **Multi-Timeframe Confirmation**
+   - Analyzes 1m, 5m, and 15m timeframes
+   - Different sensitivity for each timeframe
+   - Prevents single-timeframe noise
+
+3. **Multi-Indicator Fusion**
+   - Price momentum (primary signal)
+   - RSI momentum (overbought detection)
+   - Volume spikes (unusual activity)
+   - Moving average crossovers (trend changes)
+
+4. **Safety Bounds**
+   - Floor values prevent overly low thresholds
+   - Cap values prevent overly high thresholds
+   - Ensures functionality across all market conditions
+
+5. **Symbol-Specific Calibration**
+   - Different volume thresholds for BTC, ETH, altcoins
+   - Adapts to natural volatility patterns
+   - Recognizes trading characteristics of different pairs
+
+## 📈 Sample Output Analysis
+
+The console analyzer shows:
+
+```text
+🎯 HAS MOMENTUM: 🟢 YES/🔴 NO
+💹 Price Momentum: ✅/❌
+📈 RSI Momentum: ✅/❌  
+📊 Volume Spike: ✅/❌
+🔄 MA Cross: ✅/❌
+```
+
+### Threshold Adaptation Example
+
+```text
+ATR Scenario         1m Threshold    5m Threshold    15m Threshold
+Low Volatility       0.110%          0.140%          0.170%
+Normal Volatility    0.440%          0.560%          0.680%
+High Volatility      0.825%          1.050%          1.275%
+Extreme Volatility   1.500%          1.800%          2.000%
+```
+
+## 🔧 Configuration Parameters
+
+The system uses these key parameters:
+
+```python
+# ATR multipliers (sensitivity)
+k_1m: 0.55   # More sensitive for 1m
+k_5m: 0.70   # Medium sensitivity for 5m  
+k_15m: 0.85  # Less sensitive for 15m
+
+# Safety bounds
+floor_1m: 0.07%   # Minimum threshold
+cap_1m: 1.5%      # Maximum threshold
+
+# Indicator thresholds
+rsi_overbought: 70
+volume_spike_multiplier: 2.0
+```
+
+## 💡 Key Insights
+
+1. **Adaptive Intelligence**: The system learns from recent market behavior and automatically adjusts sensitivity.
+
+2. **Noise Filtering**: Multi-timeframe and multi-indicator confirmation significantly reduces false signals.
+
+3. **Market Condition Awareness**: Different volatility environments get appropriate threshold calibration.
+
+4. **No Manual Tuning**: Parameters self-adjust based on real-time market data.
+
+5. **Cross-Asset Compatibility**: Works effectively across different cryptocurrency pairs.
+
+## 🏗️ Technical Implementation
+
+The scripts reproduce the exact logic from `../Trading-Automation/trading_automation.py`:
+
+- `calculate_atr_percent()` - ATR calculation as percentage of price
+- `dynamic_momentum_threshold()` - ATR-based threshold calculation  
+- `check_advanced_momentum()` - Multi-indicator momentum detection
+- `has_recent_momentum()` - Multi-timeframe confirmation logic
+
+## 📋 Use Cases
+
+1. **System Understanding**: Visualize how the momentum detection works
+2. **Parameter Tuning**: Test different configuration values
+3. **Market Analysis**: Understand momentum patterns across timeframes
+4. **Strategy Validation**: Verify momentum logic before live trading
+5. **Educational**: Learn about adaptive trading algorithms
+
+## 🎨 Customization
+
+Both scripts can be easily modified to:
+
+- Test different symbols (BTC, ETH, altcoins)
+- Adjust timeframe parameters
+- Modify indicator thresholds
+- Add new momentum indicators
+- Change analysis periods
+
+## 📁 File Structure
+
+```text
+Research/
+├── momentum_analyzer.py      # Console-based analyzer (no dependencies)
+├── momentum_visualizer.py    # Graphical analyzer (requires matplotlib)
+├── requirements_viz.txt      # Dependencies for graphical version
+└── README.md                # This file
+```
+
+## 🔗 Related Files
+
+- **Main Trading Bot**: `../Trading-Automation/trading_automation.py`
+- **Symbol Data**: `../Trading-Automation/symbols.yaml`
+- **Bot Configuration**: `../Trading-Automation/secrets.py`
+
+Run the scripts to see the momentum flexibility in action! 🚀
+
+## 🧪 Research Notes
+
+This research demonstrates how the trading bot's momentum detection system:
+
+- **Adapts dynamically** to market conditions without manual intervention
+- **Balances sensitivity vs noise** through multi-indicator confirmation
+- **Scales effectively** across different asset classes and volatility regimes
+- **Maintains robustness** through safety bounds and validation logic
+
+The implementation showcases advanced algorithmic trading concepts including adaptive thresholds, multi-timeframe analysis, and intelligent signal filtering.