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Hybrid ​

Hybrid indicators combine multiple analytical approaches to provide comprehensive market analysis. These indicators often merge trend, momentum, volatility, and volume components for enhanced signal quality.

Import Statement ​

python
from tradeboard import api, ta

# Get data using Tradeboard API
client = api(api_key='your_api_key_here', host='http://127.0.0.1:5000')
df = client.history(symbol="SBIN", exchange="NSE", interval="5m", 
                   start_date="2025-04-01", end_date="2025-04-08")

Available Hybrid Indicators ​

Average Directional Index (ADX) ​

ADX measures the strength of a trend regardless of direction, providing both directional indicators (+DI, -DI) and trend strength (ADX).

Usage ​

python
di_plus, di_minus, adx = ta.adx(high, low, close, period=14)

Parameters ​

  • high (array-like): High prices
  • low (array-like): Low prices
  • close (array-like): Closing prices
  • period (int, default=14): Period for ADX calculation

Returns ​

  • tuple: (+DI, -DI, ADX) arrays in the same format as input

Example ​

python
# Calculate ADX system
di_plus, di_minus, adx = ta.adx(df['high'], df['low'], df['close'], period=14)

df['DI_Plus'] = di_plus
df['DI_Minus'] = di_minus  
df['ADX'] = adx

# Trend analysis
df['Trend_Strength'] = df['ADX'].apply(lambda x: 'Strong' if x > 25 else 'Weak' if x > 20 else 'No Trend')
df['Trend_Direction'] = df.apply(lambda row: 'Bullish' if row['DI_Plus'] > row['DI_Minus'] 
                                 else 'Bearish' if row['DI_Minus'] > row['DI_Plus'] else 'Neutral', axis=1)

print(df[['close', 'DI_Plus', 'DI_Minus', 'ADX', 'Trend_Strength', 'Trend_Direction']].tail())

Aroon Indicator ​

Aroon indicators measure the time since the highest high and lowest low, indicating trend strength and potential reversals.

Usage ​

python
aroon_up, aroon_down = ta.aroon(high, low, period=25)

Parameters ​

  • high (array-like): High prices
  • low (array-like): Low prices
  • period (int, default=25): Period for Aroon calculation

Returns ​

  • tuple: (aroon_up, aroon_down) arrays in the same format as input

Example ​

python
# Calculate Aroon indicators
aroon_up, aroon_down = ta.aroon(df['high'], df['low'], period=25)

df['Aroon_Up'] = aroon_up
df['Aroon_Down'] = aroon_down
df['Aroon_Oscillator'] = df['Aroon_Up'] - df['Aroon_Down']

# Signal interpretation
df['Aroon_Signal'] = df.apply(lambda row: 
    'Strong Uptrend' if row['Aroon_Up'] > 70 and row['Aroon_Down'] < 30
    else 'Strong Downtrend' if row['Aroon_Down'] > 70 and row['Aroon_Up'] < 30
    else 'Sideways' if abs(row['Aroon_Up'] - row['Aroon_Down']) < 20
    else 'Trending', axis=1)

print(df[['close', 'Aroon_Up', 'Aroon_Down', 'Aroon_Oscillator', 'Aroon_Signal']].tail())

Pivot Points ​

Traditional pivot points calculate support and resistance levels based on previous period's high, low, and close.

Usage ​

python
pivot, r1, s1, r2, s2, r3, s3 = ta.pivot_points(high, low, close)

Parameters ​

  • high (array-like): High prices
  • low (array-like): Low prices
  • close (array-like): Closing prices

Returns ​

  • tuple: (pivot, r1, s1, r2, s2, r3, s3) arrays

Example ​

python
# Calculate Pivot Points
pivot, r1, s1, r2, s2, r3, s3 = ta.pivot_points(df['high'], df['low'], df['close'])

df['Pivot'] = pivot
df['Resistance_1'] = r1
df['Support_1'] = s1
df['Resistance_2'] = r2
df['Support_2'] = s2
df['Resistance_3'] = r3
df['Support_3'] = s3

# Identify price position relative to pivot
df['Price_Position'] = df.apply(lambda row:
    'Above R2' if row['close'] > row['Resistance_2']
    else 'Above R1' if row['close'] > row['Resistance_1']
    else 'Above Pivot' if row['close'] > row['Pivot']
    else 'Below Pivot' if row['close'] < row['Support_1']
    else 'Below S1' if row['close'] < row['Support_2']
    else 'Below S2' if row['close'] < row['Support_2']
    else 'Near Pivot', axis=1)

print(df[['close', 'Pivot', 'Resistance_1', 'Support_1', 'Price_Position']].tail())

Parabolic SAR ​

Parabolic SAR provides trailing stop levels. ta.psar returns the stop levels only; the trend direction is read off the position of the SAR relative to price.

Usage ​

python
sar_values = ta.psar(high, low, acceleration=0.02, maximum=0.2)

Parameters ​

  • high (array-like): High prices
  • low (array-like): Low prices
  • acceleration (float, default=0.02): Acceleration factor
  • maximum (float, default=0.2): Maximum acceleration factor

Returns ​

  • array: SAR stop levels in the same format as input. ta.psar returns the SAR values only, not a trend array. Derive the direction by comparing price with the SAR: price above SAR is an uptrend, price below SAR is a downtrend.

Example ​

python
# Calculate Parabolic SAR
sar_values = ta.psar(df['high'], df['low'])

df['SAR'] = sar_values

# Derive trend direction from the SAR position: -1 uptrend, 1 downtrend
df['SAR_Trend'] = (df['close'] > df['SAR']).map({True: -1, False: 1})

# Generate trading signals
df['SAR_Signal'] = df.apply(lambda row:
    'Buy' if row['close'] > row['SAR']    # Uptrend
    else 'Sell' if row['close'] < row['SAR']  # Downtrend
    else 'Hold', axis=1)

# Calculate distance from SAR (risk management)
df['SAR_Distance'] = abs(df['close'] - df['SAR'])
df['SAR_Distance_Pct'] = (df['SAR_Distance'] / df['close']) * 100

print(df[['close', 'SAR', 'SAR_Signal', 'SAR_Distance_Pct']].tail())

Directional Movement Index (DMI) ​

DMI focuses on the directional indicators (+DI and -DI) without the ADX component.

Usage ​

python
di_plus, di_minus = ta.dmi(high, low, close, period=14)

Parameters ​

  • high (array-like): High prices
  • low (array-like): Low prices
  • close (array-like): Closing prices
  • period (int, default=14): Period for DMI calculation

Returns ​

  • tuple: (+DI, -DI) arrays in the same format as input

Example ​

python
# Calculate DMI
di_plus, di_minus = ta.dmi(df['high'], df['low'], df['close'])

df['DI_Plus'] = di_plus
df['DI_Minus'] = di_minus
df['DI_Spread'] = df['DI_Plus'] - df['DI_Minus']

# Generate directional signals
df['DMI_Signal'] = df.apply(lambda row:
    'Strong Buy' if row['DI_Plus'] > row['DI_Minus'] and row['DI_Spread'] > 10
    else 'Buy' if row['DI_Plus'] > row['DI_Minus']
    else 'Strong Sell' if row['DI_Minus'] > row['DI_Plus'] and row['DI_Spread'] < -10
    else 'Sell' if row['DI_Minus'] > row['DI_Plus']
    else 'Neutral', axis=1)

print(df[['close', 'DI_Plus', 'DI_Minus', 'DI_Spread', 'DMI_Signal']].tail())

Williams Fractals ​

Williams Fractals identify turning points (fractals) in price action using local highs and lows.

Usage ​

python
fractal_up, fractal_down = ta.fractals(high, low, periods=2)

Parameters ​

  • high (array-like): High prices
  • low (array-like): Low prices
  • periods (int, default=2): Number of periods to check (minimum 2)

Returns ​

  • tuple: (fractal_up, fractal_down) boolean arrays indicating fractal points

Example ​

python
# Calculate Williams Fractals
fractal_up, fractal_down = ta.fractals(df['high'], df['low'], periods=2)

df['Fractal_Up'] = fractal_up
df['Fractal_Down'] = fractal_down

# Mark fractal levels
df['Fractal_High'] = df['high'].where(df['Fractal_Up'])
df['Fractal_Low'] = df['low'].where(df['Fractal_Down'])

# Count recent fractals for market structure analysis
window = 20
df['Recent_Fractal_Highs'] = df['Fractal_Up'].rolling(window).sum()
df['Recent_Fractal_Lows'] = df['Fractal_Down'].rolling(window).sum()

df['Market_Structure'] = df.apply(lambda row:
    'Bullish Structure' if row['Recent_Fractal_Lows'] > row['Recent_Fractal_Highs']
    else 'Bearish Structure' if row['Recent_Fractal_Highs'] > row['Recent_Fractal_Lows']
    else 'Balanced', axis=1)

print(df[['close', 'Fractal_High', 'Fractal_Low', 'Market_Structure']].dropna().tail())

Random Walk Index (RWI) ​

RWI measures how much a security's price movement differs from a random walk, helping identify trending vs. random price movements.

Usage ​

python
rwi_high, rwi_low = ta.rwi(high, low, close, period=14)

Parameters ​

  • high (array-like): High prices
  • low (array-like): Low prices
  • close (array-like): Closing prices
  • period (int, default=14): Period for RWI calculation

Returns ​

  • tuple: (rwi_high, rwi_low) arrays in the same format as input

Example ​

python
# Calculate Random Walk Index
rwi_high, rwi_low = ta.rwi(df['high'], df['low'], df['close'], period=14)

df['RWI_High'] = rwi_high
df['RWI_Low'] = rwi_low
df['RWI_Max'] = df[['RWI_High', 'RWI_Low']].max(axis=1)

# Interpret RWI signals
df['RWI_Signal'] = df.apply(lambda row:
    'Strong Uptrend' if row['RWI_High'] > 1.0 and row['RWI_High'] > row['RWI_Low']
    else 'Strong Downtrend' if row['RWI_Low'] > 1.0 and row['RWI_Low'] > row['RWI_High']
    else 'Weak Uptrend' if row['RWI_High'] > row['RWI_Low'] and row['RWI_High'] > 0.6
    else 'Weak Downtrend' if row['RWI_Low'] > row['RWI_High'] and row['RWI_Low'] > 0.6
    else 'Random Walk', axis=1)

# Calculate trend strength
df['Trend_Strength_RWI'] = df['RWI_Max'].apply(lambda x:
    'Very Strong' if x > 1.5
    else 'Strong' if x > 1.0
    else 'Moderate' if x > 0.6
    else 'Weak')

print(df[['close', 'RWI_High', 'RWI_Low', 'RWI_Signal', 'Trend_Strength_RWI']].tail())

Complete Example: Comprehensive Trend Analysis ​

python
import pandas as pd
from tradeboard import api, ta

# Get market data
client = api(api_key='your_api_key_here', host='http://127.0.0.1:5000')
df = client.history(symbol="SBIN", exchange="NSE", interval="5m", 
                   start_date="2025-04-01", end_date="2025-04-08")

# Calculate multiple hybrid indicators
print("Calculating hybrid indicators...")

# ADX System
di_plus, di_minus, adx = ta.adx(df['high'], df['low'], df['close'])
df['DI_Plus'] = di_plus
df['DI_Minus'] = di_minus
df['ADX'] = adx

# Aroon System
aroon_up, aroon_down = ta.aroon(df['high'], df['low'])
df['Aroon_Up'] = aroon_up
df['Aroon_Down'] = aroon_down
df['Aroon_Osc'] = df['Aroon_Up'] - df['Aroon_Down']

# Parabolic SAR (values only; direction is derived from price vs SAR)
sar_values = ta.psar(df['high'], df['low'])
df['SAR'] = sar_values
df['SAR_Trend'] = (df['close'] > df['SAR']).map({True: -1, False: 1})

# Random Walk Index
rwi_high, rwi_low = ta.rwi(df['high'], df['low'], df['close'])
df['RWI_High'] = rwi_high
df['RWI_Low'] = rwi_low

# Williams Fractals
fractal_up, fractal_down = ta.fractals(df['high'], df['low'])
df['Fractal_Up'] = fractal_up
df['Fractal_Down'] = fractal_down

# Create comprehensive trend signal
def comprehensive_trend_signal(row):
    signals = []
    
    # ADX Signal
    if row['ADX'] > 25:
        if row['DI_Plus'] > row['DI_Minus']:
            signals.append('ADX_Bull')
        else:
            signals.append('ADX_Bear')
    
    # Aroon Signal
    if row['Aroon_Up'] > 70:
        signals.append('Aroon_Bull')
    elif row['Aroon_Down'] > 70:
        signals.append('Aroon_Bear')
    
    # SAR Signal
    if row['close'] > row['SAR']:
        signals.append('SAR_Bull')
    else:
        signals.append('SAR_Bear')
    
    # RWI Signal
    if row['RWI_High'] > 1.0 and row['RWI_High'] > row['RWI_Low']:
        signals.append('RWI_Bull')
    elif row['RWI_Low'] > 1.0 and row['RWI_Low'] > row['RWI_High']:
        signals.append('RWI_Bear')
    
    # Count bullish vs bearish signals
    bull_count = len([s for s in signals if 'Bull' in s])
    bear_count = len([s for s in signals if 'Bear' in s])
    
    if bull_count > bear_count and bull_count >= 2:
        return f'Bullish ({bull_count}/{len(signals)})'
    elif bear_count > bull_count and bear_count >= 2:
        return f'Bearish ({bear_count}/{len(signals)})'
    else:
        return f'Neutral ({bull_count}B/{bear_count}B)'

df['Comprehensive_Signal'] = df.apply(comprehensive_trend_signal, axis=1)

# Calculate signal strength
df['Signal_Strength'] = df.apply(lambda row:
    row['ADX'] * 0.3 + abs(row['Aroon_Osc']) * 0.3 + 
    max(row['RWI_High'], row['RWI_Low']) * 40, axis=1)

# Display results
result_columns = ['close', 'ADX', 'Aroon_Osc', 'SAR', 'RWI_High', 'RWI_Low', 
                 'Comprehensive_Signal', 'Signal_Strength']

print("\nComprehensive Trend Analysis:")
print(df[result_columns].tail(10))

# Summary statistics
print(f"\nSignal Distribution:")
print(df['Comprehensive_Signal'].value_counts())

print(f"\nAverage Signal Strength: {df['Signal_Strength'].mean():.2f}")
print(f"Current Signal Strength: {df['Signal_Strength'].iloc[-1]:.2f}")

Advanced Usage: Multi-Timeframe Analysis ​

python
# Function to get multiple timeframe data
def get_multi_timeframe_data(symbol, exchange, start_date, end_date):
    timeframes = ['1m', '5m', '15m', '1h']
    data = {}
    
    for tf in timeframes:
        try:
            df = client.history(symbol=symbol, exchange=exchange, interval=tf,
                              start_date=start_date, end_date=end_date)
            data[tf] = df
        except Exception as e:
            print(f"Error fetching {tf} data: {e}")
    
    return data

# Multi-timeframe trend analysis
def analyze_multi_timeframe_trend(data_dict):
    results = {}
    
    for timeframe, df in data_dict.items():
        # Calculate key hybrid indicators
        di_plus, di_minus, adx = ta.adx(df['high'], df['low'], df['close'])
        aroon_up, aroon_down = ta.aroon(df['high'], df['low'])
        
        latest_adx = adx.iloc[-1] if not pd.isna(adx.iloc[-1]) else 0
        latest_di_plus = di_plus.iloc[-1] if not pd.isna(di_plus.iloc[-1]) else 0
        latest_di_minus = di_minus.iloc[-1] if not pd.isna(di_minus.iloc[-1]) else 0
        latest_aroon_up = aroon_up.iloc[-1] if not pd.isna(aroon_up.iloc[-1]) else 0
        latest_aroon_down = aroon_down.iloc[-1] if not pd.isna(aroon_down.iloc[-1]) else 0
        
        # Determine trend
        if latest_adx > 25:
            if latest_di_plus > latest_di_minus:
                trend = 'Bullish'
            else:
                trend = 'Bearish'
        else:
            trend = 'Sideways'
        
        results[timeframe] = {
            'Trend': trend,
            'ADX': latest_adx,
            'Aroon_Strength': abs(latest_aroon_up - latest_aroon_down)
        }
    
    return results

# Example usage
# mtf_data = get_multi_timeframe_data("SBIN", "NSE", "2025-04-01", "2025-04-08")
# mtf_analysis = analyze_multi_timeframe_trend(mtf_data)
# print("Multi-Timeframe Analysis:", mtf_analysis)

Performance Tips ​

  1. Vectorized Operations: Use pandas operations for better performance with large datasets
  2. Memory Optimization: Calculate only needed indicators to reduce memory usage
  3. Caching: Store intermediate calculations for reuse across multiple indicators
  4. Batch Processing: Process multiple symbols together when possible

Common Use Cases ​

  1. Trend Confirmation: Use ADX with Aroon for trend strength validation
  2. Entry Timing: Combine SAR with DMI for precise entry points
  3. Support/Resistance: Use Pivot Points with Fractals for key levels
  4. Risk Management: Use RWI to distinguish trending from random movements
  5. Multi-Timeframe: Align signals across different timeframes for higher probability trades