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gateway/knowledge/indicators/indicator-development.md

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+# Indicator Development Guide
+
+Custom indicators in Dexorder are Python functions that process OHLCV data and return signals or values.
+
+## Indicator Structure
+
+```python
+def my_indicator(df, **params):
+    """
+    Calculate custom indicator
+
+    Args:
+        df: DataFrame with columns [open, high, low, close, volume]
+        **params: Indicator parameters
+
+    Returns:
+        Series or DataFrame with indicator values
+    """
+    # Implementation
+    return result
+```
+
+## Common Patterns
+
+### Simple Moving Average
+```python
+def sma(df, period=20):
+    return df['close'].rolling(window=period).mean()
+```
+
+### Exponential Moving Average
+```python
+def ema(df, period=20):
+    return df['close'].ewm(span=period, adjust=False).mean()
+```
+
+### RSI (Relative Strength Index)
+```python
+def rsi(df, period=14):
+    delta = df['close'].diff()
+    gain = delta.where(delta > 0, 0).rolling(window=period).mean()
+    loss = -delta.where(delta < 0, 0).rolling(window=period).mean()
+    rs = gain / loss
+    return 100 - (100 / (1 + rs))
+```
+
+### MACD
+```python
+def macd(df, fast=12, slow=26, signal=9):
+    ema_fast = df['close'].ewm(span=fast).mean()
+    ema_slow = df['close'].ewm(span=slow).mean()
+    macd_line = ema_fast - ema_slow
+    signal_line = macd_line.ewm(span=signal).mean()
+    histogram = macd_line - signal_line
+
+    return pd.DataFrame({
+        'macd': macd_line,
+        'signal': signal_line,
+        'histogram': histogram
+    })
+```
+
+## Best Practices
+
+### Data Handling
+- Always validate input DataFrame has required columns
+- Handle NaN values appropriately
+- Use `.copy()` to avoid modifying original data
+- Consider edge cases (not enough data, etc.)
+
+### Performance
+- Vectorize operations when possible (avoid loops)
+- Use pandas/numpy built-in functions
+- Cache expensive calculations
+- Test on large datasets
+
+### Parameters
+- Provide sensible defaults
+- Document parameter ranges
+- Validate parameter values
+- Consider optimization bounds
+
+### Testing
+```python
+def test_indicator():
+    # Create sample data
+    df = pd.DataFrame({
+        'close': [100, 102, 101, 103, 105]
+    })
+
+    # Test calculation
+    result = my_indicator(df, param=10)
+
+    # Validate output
+    assert not result.isna().all()
+    assert len(result) == len(df)
+```
+
+## Common Pitfalls
+
+### Look-Ahead Bias
+Never use future data:
+```python
+# WRONG - uses future data
+df['signal'] = df['close'].shift(-1) > df['close']
+
+# CORRECT - only past data
+df['signal'] = df['close'] > df['close'].shift(1)
+```
+
+### Repainting
+Indicator values should not change for closed bars:
+```python
+# Ensure calculations are based on closed candles
+# Avoid using unstable data sources
+```
+
+### Overfitting
+- Don't optimize on same data you test on
+- Use separate train/validation/test sets
+- Walk-forward analysis for robustness
+- Simple is often better than complex
+
+## Integration with Strategies
+
+Indicators are used in strategy signals:
+```python
+def my_strategy(df):
+    # Calculate indicators
+    df['rsi'] = rsi(df, period=14)
+    df['sma_fast'] = sma(df, period=20)
+    df['sma_slow'] = sma(df, period=50)
+
+    # Generate signals
+    df['signal'] = 0
+    df.loc[(df['rsi'] < 30) & (df['sma_fast'] > df['sma_slow']), 'signal'] = 1
+    df.loc[(df['rsi'] > 70) & (df['sma_fast'] < df['sma_slow']), 'signal'] = -1
+
+    return df
+```
+
+Store indicators in your git repository under `indicators/` directory.