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Momentum ​

Momentum indicators measure the speed and strength of price movements, helping identify overbought/oversold conditions and potential trend reversals.

Import Statement ​

python
from tradeboard import ta

Available Momentum Indicators ​

Relative Strength Index (RSI) ​

RSI is a momentum oscillator that measures the speed and magnitude of price changes, oscillating between 0 and 100.

Usage ​

python
rsi_result = ta.rsi(data, period=14)

Parameters ​

  • data (array-like): Price data (typically closing prices)
  • period (int, default=14): Number of periods for RSI calculation

Returns ​

  • array: RSI values (range: 0 to 100) in the same format as input

Example ​

python
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 RSI
df['RSI_14'] = ta.rsi(df['close'], 14)
df['RSI_21'] = ta.rsi(df['close'], 21)

print(df[['close', 'RSI_14', 'RSI_21']].tail())

Moving Average Convergence Divergence (MACD) ​

MACD is a trend-following momentum indicator showing the relationship between two exponential moving averages.

Usage ​

python
macd_line, signal_line, histogram = ta.macd(data, fast_period=12, slow_period=26, signal_period=9)

Parameters ​

  • data (array-like): Price data (typically closing prices)
  • fast_period (int, default=12): Period for fast EMA
  • slow_period (int, default=26): Period for slow EMA
  • signal_period (int, default=9): Period for signal line EMA

Returns ​

  • tuple: (macd_line, signal_line, histogram) arrays

Example ​

python
# Calculate MACD
macd_line, signal_line, histogram = ta.macd(df['close'])

# Add to DataFrame
df['MACD'] = macd_line
df['MACD_Signal'] = signal_line
df['MACD_Histogram'] = histogram

# Custom parameters
macd_fast, signal_fast, hist_fast = ta.macd(df['close'], fast_period=8, slow_period=21, signal_period=5)

print(df[['close', 'MACD', 'MACD_Signal', 'MACD_Histogram']].tail())

Stochastic Oscillator ​

The Stochastic Oscillator compares a security's closing price to its price range over a given time period.

Usage ​

python
k_percent, d_percent = ta.stochastic(high, low, close, k_period=14, smooth_k=3, d_period=3)

Parameters ​

  • high (array-like): High prices
  • low (array-like): Low prices
  • close (array-like): Closing prices
  • k_period (int, default=14): Lookback for the raw %K stochastic
  • smooth_k (int, default=3): SMA smoothing applied to raw %K to produce the returned %K
  • d_period (int, default=3): Period for %D calculation (SMA of the smoothed %K)

Returns ​

  • tuple: (k_percent, d_percent) arrays

Example ​

python
# Calculate Stochastic Oscillator
stoch_k, stoch_d = ta.stochastic(df['high'], df['low'], df['close'])

# Add to DataFrame
df['Stoch_K'] = stoch_k
df['Stoch_D'] = stoch_d

# Custom parameters
stoch_k_fast, stoch_d_fast = ta.stochastic(df['high'], df['low'], df['close'], 
                                          k_period=5, smooth_k=3, d_period=3)

print(df[['close', 'Stoch_K', 'Stoch_D']].tail())

Commodity Channel Index (CCI) ​

CCI measures the current price level relative to an average price level over a given period.

Usage ​

python
cci_result = ta.cci(high, low, close, period=20)

Parameters ​

  • high (array-like): High prices
  • low (array-like): Low prices
  • close (array-like): Closing prices
  • period (int, default=20): Number of periods for CCI calculation

Returns ​

  • array: CCI values in the same format as input

Example ​

python
# Calculate CCI
df['CCI_20'] = ta.cci(df['high'], df['low'], df['close'], 20)
df['CCI_14'] = ta.cci(df['high'], df['low'], df['close'], 14)

print(df[['close', 'CCI_20', 'CCI_14']].tail())

Williams %R ​

Williams %R is a momentum indicator that measures overbought and oversold levels on a scale from 0 to -100.

Usage ​

python
williams_r = ta.williams_r(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): Number of periods for Williams %R calculation

Returns ​

  • array: Williams %R values (range: 0 to -100) in the same format as input

Example ​

python
# Calculate Williams %R
df['Williams_R'] = ta.williams_r(df['high'], df['low'], df['close'])
df['Williams_R_21'] = ta.williams_r(df['high'], df['low'], df['close'], 21)

print(df[['close', 'Williams_R', 'Williams_R_21']].tail())

Balance of Power (BOP) ​

Balance of Power measures the strength of buyers versus sellers by assessing the ability of each side to drive prices to an extreme level.

Usage ​

python
bop_result = ta.bop(open_prices, high, low, close)

Parameters ​

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

Returns ​

  • array: BOP values in the same format as input

Example ​

python
# Calculate Balance of Power
df['BOP'] = ta.bop(df['open'], df['high'], df['low'], df['close'])

print(df[['close', 'BOP']].tail())

Elder Ray Index ​

Elder Ray Index consists of Bull Power and Bear Power, measuring the ability of bulls and bears to drive prices above or below an EMA.

Usage ​

python
bull_power, bear_power = ta.elderray(high, low, close, period=13)

Parameters ​

  • high (array-like): High prices
  • low (array-like): Low prices
  • close (array-like): Closing prices
  • period (int, default=13): Period for EMA calculation

Returns ​

  • tuple: (bull_power, bear_power) arrays

Example ​

python
# Calculate Elder Ray Index
bull_power, bear_power = ta.elderray(df['high'], df['low'], df['close'])

# Add to DataFrame
df['Bull_Power'] = bull_power
df['Bear_Power'] = bear_power

print(df[['close', 'Bull_Power', 'Bear_Power']].tail())

Fisher Transform ​

The Fisher Transform converts prices into a Gaussian normal distribution, making it easier to identify turning points.

Usage ​

python
fisher, trigger = ta.fisher(high, low, length=9)

Parameters ​

  • high (array-like): High prices
  • low (array-like): Low prices
  • length (int, default=9): Length for highest/lowest calculation

Returns ​

  • tuple: (fisher, trigger) arrays

Example ​

python
# Calculate Fisher Transform
fisher, fisher_trigger = ta.fisher(df['high'], df['low'])

# Add to DataFrame
df['Fisher'] = fisher
df['Fisher_Trigger'] = fisher_trigger

# Custom length
fisher_14, trigger_14 = ta.fisher(df['high'], df['low'], length=14)

print(df[['close', 'Fisher', 'Fisher_Trigger']].tail())

Connors RSI (CRSI) ​

Connors RSI is a composite momentum oscillator consisting of three components: RSI of price, RSI of updown streak, and percent rank of 1-period ROC.

Usage ​

python
crsi_result = ta.crsi(data, lenrsi=3, lenupdown=2, lenroc=100)

Parameters ​

  • data (array-like): Price data (typically closing prices)
  • lenrsi (int, default=3): RSI Length (period for price RSI)
  • lenupdown (int, default=2): UpDown Length (period for streak RSI)
  • lenroc (int, default=100): ROC Length (period for ROC percent rank)

Returns ​

  • array: Connors RSI values in the same format as input

Example ​

python
# Calculate Connors RSI
df['CRSI'] = ta.crsi(df['close'])

# Custom parameters
df['CRSI_Custom'] = ta.crsi(df['close'], lenrsi=5, lenupdown=3, lenroc=50)

print(df[['close', 'CRSI', 'CRSI_Custom']].tail())

Complete Example: Multiple Momentum Indicators ​

python
from tradeboard import api, ta
import pandas as pd

# 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 momentum indicators
df['RSI'] = ta.rsi(df['close'], 14)

# MACD
macd_line, signal_line, histogram = ta.macd(df['close'])
df['MACD'] = macd_line
df['MACD_Signal'] = signal_line
df['MACD_Histogram'] = histogram

# Stochastic
stoch_k, stoch_d = ta.stochastic(df['high'], df['low'], df['close'])
df['Stoch_K'] = stoch_k
df['Stoch_D'] = stoch_d

# CCI
df['CCI'] = ta.cci(df['high'], df['low'], df['close'], 20)

# Williams %R
df['Williams_R'] = ta.williams_r(df['high'], df['low'], df['close'])

# Balance of Power
df['BOP'] = ta.bop(df['open'], df['high'], df['low'], df['close'])

# Elder Ray
bull_power, bear_power = ta.elderray(df['high'], df['low'], df['close'])
df['Bull_Power'] = bull_power
df['Bear_Power'] = bear_power

# Fisher Transform
fisher, fisher_trigger = ta.fisher(df['high'], df['low'])
df['Fisher'] = fisher
df['Fisher_Trigger'] = fisher_trigger

# Connors RSI
df['CRSI'] = ta.crsi(df['close'])

# Display results
momentum_cols = ['close', 'RSI', 'MACD', 'MACD_Signal', 'Stoch_K', 'Stoch_D', 
                'CCI', 'Williams_R', 'BOP', 'Bull_Power', 'Bear_Power', 
                'Fisher', 'CRSI']

print(df[momentum_cols].tail(10))

# Trading signals example
df['RSI_Oversold'] = df['RSI'] < 30
df['RSI_Overbought'] = df['RSI'] > 70
df['MACD_Bullish'] = df['MACD'] > df['MACD_Signal']
df['Stoch_Oversold'] = (df['Stoch_K'] < 20) & (df['Stoch_D'] < 20)

# Combine signals
df['Bullish_Signal'] = (df['RSI_Oversold']) & (df['MACD_Bullish']) & (df['Stoch_Oversold'])

print("\nBullish signals:")
print(df[df['Bullish_Signal']][['close', 'RSI', 'MACD', 'Stoch_K']].head())

Signal Interpretation Guide ​

RSI ​

  • > 70: Overbought (potential sell signal)
  • < 30: Oversold (potential buy signal)
  • 50: Neutral momentum

MACD ​

  • MACD > Signal: Bullish momentum
  • MACD < Signal: Bearish momentum
  • Histogram > 0: Increasing bullish momentum
  • Histogram < 0: Increasing bearish momentum

Stochastic ​

  • %K > 80: Overbought conditions
  • %K < 20: Oversold conditions
  • %K crossing above %D: Bullish signal
  • %K crossing below %D: Bearish signal

CCI ​

  • > +100: Strong uptrend
  • < -100: Strong downtrend
  • -100 to +100: Ranging market

Williams %R ​

  • > -20: Overbought
  • < -80: Oversold
  • Crossing -50: Trend change signal

Performance Tips ​

  1. Use appropriate periods: Shorter periods for more sensitive signals, longer for smoother trends
  2. Combine indicators: Use multiple momentum indicators to confirm signals
  3. Market context: Consider overall market trend when interpreting momentum signals
  4. Divergences: Look for divergences between price and momentum indicators