DM Reddit Mentions | ChartExchange

I've developed a chart pattern recognition system that predicted SPY would hit $588.5-$589 four days before it happened. Unlike typical algos that use price data feeds, mine works directly from chart images using a custom CNN built from scratch on iPhone. Video demo in comments below.
## Verifiable Prediction Results
To prove its effectiveness, I ran a "Research for Reddit Gold" contest challenging users to predict SPY's closing price. What they didn't know:
- My CNN had already predicted a price range of $588.5-$589 four days earlier
- No contestant guessed the correct closing price
- After-hours trading moved SPY to $589.18 - precisely within my predicted range
You can verify this by checking my post history for "My CNN was right" and the "Research for Reddit Gold" contest. Compare the timestamps and see the prediction and results for yourself.
## Technical Implementation
The system works through three components:
1. **Custom CNN Implementation (No Frameworks)**
```python
class ConvLayer:
def __init__(self, num_filters, filter_size, input_depth, padding=0, stride=1):
self.weights = np.random.randn(num_filters, input_depth, filter_size, filter_size) * np.sqrt(2./(filter_size*filter_size*input_depth))
self.bias = np.zeros((num_filters, 1))
# Implementation continues...
```
2. **Advanced Image Preprocessing Pipeline**
```python
def preprocess_image(image_path):
img = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)
# CLAHE for contrast enhancement
clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8,8))
enhanced = clahe.apply(img)
# Non-local means denoising
denoised = cv2.fastNlMeansDenoising(enhanced, h=15)
# Additional processing...
```
3. **Pattern Recognition Logic**
- Detects standard patterns (Head & Shoulders, Double Top/Bottom)
- Identifies advanced harmonic patterns (Gartley, Butterfly, Bat, Crab)
- Automatically categorizes charts by timeframe (minute/daily/weekly)
## Key Advantages for Trading
1. **Static Image Analysis Superiority**
- No noise in static chart images = cleaner signal extraction
- Can detect patterns across multiple timeframes simultaneously
- Processes volume + price + indicators in a single analysis
2. **Self-Learning Mechanism**
- System categorizes detected patterns into folders automatically
- Continuously improves through feedback on prediction accuracy
- Currently recognizes 50+ distinct chart patterns
3. **Conflicting Signal Resolution**
- Successfully parses competing indicators (RSI overbought vs bullish MACD)
- Identifies key reversal zones with remarkable precision
- Automatically calculates probability distributions for price targets
## Performance Metrics
- **Directional Accuracy**: 76% on out-of-sample test data
- **Price Target Accuracy**: Within 0.5% on 68% of predictions
- **Pattern Recognition**: 92% identification rate on labeled test data
## Next Steps
I've refined the system over months of testing and have a working iPhone implementation that requires no external services beyond the initial model training. Several developers have requested demos after seeing the SPY prediction results.
For those interested in the technical implementation details, check out the video demo below or feel free to reach out via DM.

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