Technical Indicators¶
QNT Technical Indicators¶
The qnt.ta module is a collection of technical analysis indicators and functions specially optimized for working with qnt, a platform for quantitative finance research and trading strategies.
Indicator groups:
Moving Averages: These indicators calculate the average price over a specified number of periods to help identify trends in the market.
Oscillators: These indicators measure the momentum and trend of the market by comparing the current price to its historical average.
Volatility Indicators: These indicators help to identify how much the price of an asset is changing over time, which can be useful for managing risk.
Volume Indicators: These indicators measure the strength or weakness of a price trend based on the volume of trades occurring in the market.
Overlap Studies: These indicators are used to identify potential areas of support and resistance by analyzing the relationship between the current price and its historical moving averages.
Momentum Indicators: These indicators measure the rate of change of an asset’s price over time to help identify trend reversals.
Cycle Indicators: These indicators help identify trends in the market by analyzing repeating patterns over a fixed period of time.
import qnt.data as qndata
import qnt.ta as qnta
data = qndata.stocks.load_ndx_data(min_date="2005-06-01")
high = data.sel(field='high')
low = data.sel(field='low')
close = data.sel(field='close')
volume = data.sel(field='vol')
# Moving Averages
sma_20 = qnta.sma(close, 20)
ema_20 = qnta.ema(close, 20)
wilder_ma_20 = qnta.wilder_ma(close, 20)
lwma_20 = qnta.lwma(close, 20)
dema_20 = qnta.dema(close, 20)
tema_20 = qnta.tema(close, 20)
# Oscillators
rsi_14 = qnta.rsi(close, 14)
roc_10 = qnta.roc(close, 10)
sroc_10 = qnta.sroc(close, 10)
macd_line, macd_signal, macd_hist = qnta.macd(close, 12, 26, 9)
trix_15 = qnta.trix(close, 15)
stoch_k = qnta.stochastic_k(high, low, close, 14)
stoch_d = qnta.stochastic(high, low, close, 14)
slow_stoch_d = qnta.slow_stochastic(high, low, close, 14)
# Index Indicators
atr_14 = qnta.atr(high, low, close, 14)
tr_1 = qnta.tr(high, low, close)
dms = qnta.dms(high, low, close, 14, 14, 14)
# Cumulative
obv_line = qnta.obv(close, volume)
chaikin_adl_line = qnta.chaikin_adl(high, low, close, volume)
chaikin_oscillator = qnta.chaikin_osc(chaikin_adl_line, 3, 10)
# Global
ad_line_result = qnta.ad_line(close * data.sel(field="is_liquid"))
ad_ratio_result = qnta.ad_ratio(close * data.sel(field="is_liquid"))
# Pivot Points
pivot_points_result = qnta.pivot_points(data, 2, 3)
top_pivot_points_result = qnta.top_pivot_points(data)
bottom_pivot_points_result = qnta.bottom_pivot_points(data)
# Other functions
price_change = qnta.change(close)
shifted_data = qnta.shift(close, periods=1)
std_dev = qnta.std(close, 20)
variance_value = qnta.variance(close, 20)
covariance_value = qnta.covariance(close, close, 20)
beta_value = qnta.beta(close, close, 20)
correlation_value = qnta.correlation(close, close, 20)
print(sma_20, ema_20, wilder_ma_20,
lwma_20, dema_20, tema_20,
rsi_14, roc_10, sroc_10, macd_line,
macd_signal, macd_hist, trix_15, stoch_k,
stoch_d, slow_stoch_d, atr_14, tr_1,
obv_line, chaikin_adl_line,
chaikin_oscillator, ad_line_result, ad_ratio_result,
pivot_points_result, top_pivot_points_result,
bottom_pivot_points_result, price_change,
shifted_data, std_dev,variance_value,
covariance_value, beta_value, correlation_value)
Moving Averages¶
| Short Name | Full Name and Description | Code Example |
|---|---|---|
| SMA | Simple Moving Average Calculates the average price over a specified number of periods. |
sma = qnt.ta.sma(close, 20) |
| EMA | Exponential Moving Average Assigns more weight to recent prices and less weight to older prices. |
ema = qnt.ta.ema(close, 20) |
| Wilder MA | Wilder Moving Average Assigns more weight to recent prices and less weight to older prices. |
wilder_ma = qnt.ta.wilder_ma(close, 20) |
| DEMA | Double Exponential Moving Average Smooths out price data more effectively than a single EMA. |
dema = qnt.ta.dema(close, 20) |
| TEMA | Triple Exponential Moving Average Smooths out price data more effectively than a single or double EMA. |
tema = qnt.ta.tema(close, 20) |
| WMA | Weighted Moving Average Assigns more weight to recent prices and less weight to older prices based on a specified weighting function. |
wma = qnt.ta.wma(close, 20) |
| LWMA | Linearly Weighted Moving Average Assigns more weight to recent prices and less weight to older prices based on a linear weighting function. |
lwma = qnt.ta.lwma(close, 20) |
| VWMA | Volume Weighted Moving Average Assigns more weight to periods with higher volume. |
vwma = qnt.ta.vwma(close, volume, 20) |
Oscillators¶
| Short Name | Full Name and Description | Code Example |
|---|---|---|
| Stochastic K | Stochastic Oscillator %K Measures the current price relative to the range of prices over a specified number of periods. |
stochastic_k = qnt.ta.stochastic_k(high, low, close, 14) |
| Stochastic | Stochastic Oscillator Compares the current price to the range of prices over a specified number of periods to identify overbought and oversold conditions. |
stochastic = qnt.ta.stochastic(high, low, close, 14) |
| Slow Stochastic | Slow Stochastic Oscillator Smooths the stochastic oscillator to reduce false signals. |
slow_stochastic = qnt.ta.slow_stochastic(high, low, close) |
| RSI | Relative Strength Index Measures the strength of price movement over a specified number of periods. |
rsi = qnt.ta.rsi(close, 14) |
| ROC | Rate of Change Measures the percentage change in price over a specified number of periods. |
roc = qnt.ta.roc(close, 14) |
| SROC | Smoothed Rate of Change Smooths out the rate of change to reduce false signals. |
sroc = qnt.ta.sroc(close, 14) |
| MACD | Moving Average Convergence Divergence Compares two moving averages to identify buying and selling opportunities. |
macd, signal, hist = qnt.ta.macd(close, 12, 26, 9) |
| TRIX | Triple Exponential Average Measures the percentage change in a triple exponential moving average over a specified number of periods. |
trix = qnt.ta.trix(close, 14) |
Index Indicators¶
| Short Name | Full Name and Description | Code Example |
|---|---|---|
| ATR | Average True Range Measures the volatility of an asset's price over a specified period of time. |
atr = qnt.ta.atr(high, low, close, 14) |
| TR | True Range Measures the greatest distance between the high and low over a specified period of time. |
tr = qnt.ta.tr(high, low, close) |
| DMS | Directional Movement System Measures the strength of a trend and identifies whether a market is trending or not. |
dms = qnt.ta.dms(high, low, close, 14, 14, 14) |
| OBV | On-Balance Volume Tracks the positive and negative flow of volume in a security. |
obv = qnt.ta.obv(close, volume) |
| Chaikin ADL | Chaikin Accumulation/Distribution Line Measures the cumulative flow of money into and out of a security. |
chaikin_adl = qnt.ta.chaikin_adl(high, low, close, volume) |
| Chaikin OSC | Chaikin Oscillator Measures the momentum of the Accumulation/Distribution Line. |
chaikin_osc = qnt.ta.chaikin_osc(chaikin_adl_line, 3, 10) |
| AD Line | Accumulation/Distribution Line Measures the cumulative flow of money into and out of a security. |
ad_line = qnt.ta.ad_line(close * data.sel(field="is_liquid")) |
| AD Ratio | Accumulation/Distribution Ratio Measures the buying and selling pressure on a security. |
ad_ratio = qnt.ta.ad_ratio(close * data.sel(field="is_liquid")) |
| Pivot Points | Pivot Points Identifies key levels of support and resistance in a market. |
pivot_points = qnt.ta.pivot_points(data, 2, 3) |
| Top Pivot Points | Top Pivot Points Identifies key levels of resistance in a market. |
top_pivot_points = qnt.ta.top_pivot_points(data) |
| Bottom Pivot Points | Bottom Pivot Points Identifies key levels of support in a market. |
bottom_pivot_points = qnt.ta.bottom_pivot_points(data) |
Other functions¶
| Short Name | Full Name and Description | Code Example |
|---|---|---|
| Change | Change Calculates the percentage change between the current and previous values. |
change = qnt.ta.change(close) |
| Shift | Shift Shifts a series forward or backward in time. |
shifted = qnt.ta.shift(close, 5) |
| Standard Deviation | Standard Deviation Measures the dispersion of a set of values from their mean. |
std = qnt.ta.std(close, 20) |
| Variance | Variance Measures the variability of a set of values from their mean. |
variance = qnt.ta.variance(close, 20) |
| Covariance | Covariance Measures the joint variability of two sets of values. |
covariance = qnt.ta.covariance(close1, close2, 20) |
| Beta | Beta Measures the sensitivity of a stock's returns to the market. |
beta = qnt.ta.beta(close1, close2, 20) |
| Correlation | Correlation Measures the strength of the linear relationship between two sets of values. |
correlation = qnt.ta.correlation(close1, close2, 252) |
TA-Lib Technical Indicators¶
The xr_talib.py is a module from the qnt library that serves as a wrapper for using the popular TA-Lib library with xarray. It provides an extensive collection of technical analysis indicators for analyzing financial market data.
Indicator groups:
Overlap Studies: Smooth or filter price data for identifying trends and support/resistance levels. (e.g., moving averages, Bollinger Bands)
Momentum Indicators: Measure price change rate to identify trend strength or reversals. (e.g., RSI, MACD, Stochastic Oscillator)
Volume Indicators: Analyze the relationship between trading volume and price movements. (e.g., OBV, CMF, MFI)
Volatility Indicators: Measure price fluctuations to identify high volatility periods. (e.g., ATR, Bollinger Bands, Standard Deviation)
Price Transform: Apply mathematical transformations to price data. (e.g., Weighted Close, Typical Price, Median Price)
Cycle Indicators: Identify and measure repeating price patterns and cycles. (e.g., Hilbert Transform, Sine Wave)
Pattern Recognition: Identify price patterns or candlestick formations signaling trend reversals or continuations. (e.g., Engulfing, Doji, Hammer)
Overlap Studies¶
| Short Name | Full Name and Description | Code Example |
|---|---|---|
| SMA | Simple Moving Average Calculates the average price over a specified number of periods. |
sma = qnt.xr_talib.SMA(close, 20) |
| EMA | Exponential Moving Average Calculates the average price over a specified number of periods, giving more weight to recent prices. |
ema = qnt.xr_talib.EMA(close, 20) |
| WMA | Weighted Moving Average Calculates the average price over a specified number of periods, with each price weighted proportionally to its age. |
wma = qnt.xr_talib.WMA(close, 20) |
| DEMA | Double Exponential Moving Average Averages the price data with two exponential moving averages, reducing lag. |
dema = qnt.xr_talib.DEMA(close, 20) |
| TEMA | Triple Exponential Moving Average Averages the price data with three exponential moving averages, further reducing lag. |
tema = qnt.xr_talib.TEMA(close, 20) |
| TRIMA | Triangular Moving Average Averages the price data with a triangular weighting, emphasizing the middle values of the range. |
trima = qnt.xr_talib.TRIMA(close, 20) |
| KAMA | Kaufman Adaptive Moving Average An adaptive moving average that adjusts its length based on market volatility. |
kama = qnt.xr_talib.KAMA(close, 20) |
| MAMA | MESA Adaptive Moving Average An adaptive moving average that adjusts its length based on the dominant cycle period in the data. |
mama, fama = qnt.xr_talib.MAMA(close, 0.5, 0.05) |
| T3 | Triple Exponential Moving Average (T3) A smoother version of the TEMA with reduced lag. |
t3 = qnt.xr_talib.T3(close, 20, 0.7) |
| BBANDS | Bollinger Bands Calculates a moving average with standard deviation bands above and below the average. |
upper, middle, lower = qnt.xr_talib.BBANDS(close, 20, 2, 2, 0) |
import qnt.xr_talib as xr_talib
import qnt.data as qndata
data = qndata.stocks.load_ndx_data(min_date="2005-06-01")
close_prices = data.sel(field='close')
sma = xr_talib.SMA(close_prices, timeperiod=20)
ema = xr_talib.EMA(close_prices, timeperiod=20)
wma = xr_talib.WMA(close_prices, timeperiod=20)
dema = xr_talib.DEMA(close_prices, timeperiod=20)
tema = xr_talib.TEMA(close_prices, timeperiod=20)
trima = xr_talib.TRIMA(close_prices, timeperiod=20)
kama = xr_talib.KAMA(close_prices, timeperiod=20)
mama, fama = xr_talib.MAMA(close_prices, fastlimit=0.5, slowlimit=0.05)
t3 = xr_talib.T3(close_prices, timeperiod=20, vfactor=0.7)
upper, middle, lower = xr_talib.BBANDS(close_prices, timeperiod=20, nbdevup=2, nbdevdn=2)
sar = xr_talib.SAR(data, acceleration=0.02, maximum=0.2)
typprice = xr_talib.TYPPRICE(data)
wclprice = xr_talib.WCLPRICE(data)
print(sma, ema, wma, dema, tema, trima, kama, mama, fama, t3, upper, middle, lower, sar, typprice, wclprice)
Momentum Indicators¶
| Short Name | Full Name and Description | Code Example |
|---|---|---|
| ADX | Average Directional Movement Index Measures the strength of a prevailing trend. |
adx = qnt.xr_talib.ADX(data, 14) |
| ADXR | Average Directional Movement Index Rating Smoothed version of ADX. |
adxr = qnt.xr_talib.ADXR(data, 14) |
| APO | Absolute Price Oscillator Difference between two moving averages. |
apo = qnt.xr_talib.APO(close, 14, 26, 1) |
| AROON | Aroon Measures the time between the highest high and the lowest low in a given period. |
aroon_up, aroon_down = qnt.xr_talib.AROON(data, 50) |
| AROONOSC | Aroon Oscillator Difference between Aroon-Up and Aroon-Down. |
aroonosc = qnt.xr_talib.AROONOSC(data, 14) |
| BOP | Balance of Power Measures the strength of buyers and sellers in the market. |
bop = qnt.xr_talib.BOP(data) |
| CCI | Commodity Channel Index Measures the deviation of the price from its average in relation to the volatility. |
cci = qnt.xr_talib.CCI(data, 14) |
| CMO | Chande Momentum Oscillator Measures the momentum of price changes. |
cmo = qnt.xr_talib.CMO(close, 14) |
| DX | Directional Movement Index Measures the strength of a trend. |
dx = qnt.xr_talib.DX(data, 14) |
| MACD | Moving Average Convergence Divergence Measures the difference between two moving averages, with a signal line to detect trends. |
macd, macdsignal, macdhist = qnt.xr_talib.MACD(close, 12, 26, 9) |
| MFI | Money Flow Index Measures the buying and selling pressure based on price and volume. |
mfi = qnt.xr_talib.MFI(data, 14) |
| MINUS_DI | Minus Directional Indicator Measures the strength of downward price movement. |
minus_di = qnt.xr_talib.MINUS_DI(data, 14) |
| MINUS_DM | Minus Directional Movement Measures the strength of the lowest lows. |
minus_dm = qnt.xr_talib.MINUS_DM(data, 14) |
| MOM | Momentum Measures the rate of change in the price. |
mom = qnt.xr_talib.MOM(close, 10) |
| PLUS_DI | Plus Directional Indicator Measures the strength of upward price movement. |
plus_di = qnt.xr_talib.PLUS_DI(data, 14) |
| PLUS_DM | Plus Directional Movement Measures the strength of the highest highs. |
plus_dm = qnt.xr_talib.PLUS_DM(data, 14) |
| PPO | Percentage Price Oscillator Measures the percentage difference between two moving averages. |
ppo = qnt.xr_talib.PPO(close, 14, 26, 1) |
| ROC | Rate of Change Measures the percentage change in price over a specified number of periods. |
roc = qnt.xr_talib.ROC(close, 10) |
| ROCP | Rate of Change Percentage Measures the percentage change in price over a specified number of periods. |
rocp = qnt.xr_talib.ROCP(close, 10) |
| ROCR | Rate of Change Ratio Measures the ratio of the current price to the price a specified number of periods ago. |
rocr = qnt.xr_talib.ROCR(close, 10) |
| ROCR100 | Rate of Change Ratio 100 Scale Measures the ratio of the current price to the price a specified number of periods ago, multiplied by 100. |
rocr100 = qnt.xr_talib.ROCR100(close, 10) |
| RSI | Relative Strength Index Measures the speed and change of price movements. |
rsi = qnt.xr_talib.RSI(close, 14) |
| STOCH | Stochastic Oscillator Measures the level of the close relative to the high-low range over a specified number of periods. |
slowk, slowd = qnt.xr_talib.STOCH(data, 5, 3, 0, 3, 0) |
| STOCHF | Stochastic Fast Oscillator A faster version of the Stochastic Oscillator. |
fastk, fastd = qnt.xr_talib.STOCHF(data, 5, 3, 0) |
| STOCHRSI | Stochastic Relative Strength Index Applies the Stochastic Oscillator formula to RSI values. |
fastk, fastd = qnt.xr_talib.STOCHRSI(close, 14, 5, 3, 0) |
| TRIX | Triple Exponential Moving Average Rate of Change Smoothed rate of change of a triple exponential moving average. |
trix = qnt.xr_talib.TRIX(close, 30) |
| ULTOSC | Ultimate Oscillator Combines short-, medium-, and long-term price action into one oscillator. |
ultosc = qnt.xr_talib.ULTOSC(data, 7, 14, 28) |
| WILLR | Williams %R Measures the level of the close relative to the high-low range over a specified number of periods. |
willr = qnt.xr_talib.WILLR(data, 14) |
import qnt.xr_talib as xr_talib
import qnt.data as qndata
data = qndata.stocks.load_ndx_data(min_date="2005-06-01")
close_prices = data.sel(field='close')
adx = xr_talib.ADX(data, timeperiod=14)
adxr = xr_talib.ADXR(data, timeperiod=14)
apo = xr_talib.APO(close_prices, fastperiod=12, slowperiod=26, matype=0)
aroon_down, aroon_up = xr_talib.AROON(data, timeperiod=14)
aroonosc = xr_talib.AROONOSC(data, timeperiod=14)
bop = xr_talib.BOP(data)
cci = xr_talib.CCI(data, timeperiod=14)
cmo = xr_talib.CMO(close_prices, timeperiod=14)
dx = xr_talib.DX(data, timeperiod=14)
macd, macdsignal, macdhist = xr_talib.MACD(close_prices, fastperiod=12,
slowperiod=26, signalperiod=9)
macdext, macdsignalext, macdhistext = xr_talib.MACDEXT(close_prices, fastperiod=12,
slowperiod=26, signalperiod=9,
fastmatype=0, slowmatype=0,
signalmatype=0)
macdfix, macdsignalfix, macdhistfix = xr_talib.MACDFIX(close_prices, signalperiod=9)
mfi = xr_talib.MFI(data, timeperiod=14)
minus_di = xr_talib.MINUS_DI(data, timeperiod=14)
minus_dm = xr_talib.MINUS_DM(data, timeperiod=14)
mom = xr_talib.MOM(close_prices, timeperiod=10)
plus_di = xr_talib.PLUS_DI(data, timeperiod=14)
plus_dm = xr_talib.PLUS_DM(data, timeperiod=14)
ppo = xr_talib.PPO(close_prices, fastperiod=12, slowperiod=26, matype=0)
roc = xr_talib.ROC(close_prices, timeperiod=10)
rocp = xr_talib.ROCP(close_prices, timeperiod=10)
rocr = xr_talib.ROCR(close_prices, timeperiod=10)
rocr100 = xr_talib.ROCR100(close_prices, timeperiod=10)
rsi = xr_talib.RSI(close_prices, timeperiod=14)
stoch_slowk, stoch_slowd = xr_talib.STOCH(data, fastk_period=5, slowk_period=3,
slowk_matype=0, slowd_period=3, slowd_matype=0)
stochf_fastk, stochf_fastd = xr_talib.STOCHF(data, fastk_period=5,
fastd_period=3, fastd_matype=0)
stochrsi = xr_talib.STOCHRSI(close_prices, timeperiod=14, fastk_period=5,
fastd_period=3, fastd_matype=0)
trix = xr_talib.TRIX(close_prices, timeperiod=30)
ultosc = xr_talib.ULTOSC(data, timeperiod1=7, timeperiod2=14, timeperiod3=28)
willr = xr_talib.WILLR(data, timeperiod=14)
print(adx, adxr, apo, aroon_down, aroon_up, aroonosc, bop, cci, cmo, dx, macd,
macdsignal, macdhist, macdext, macdsignalext, macdhistext, macdfix, macdsignalfix,
macdhistfix, mfi, minus_di, minus_dm, mom, plus_di, plus_dm, ppo, roc, rocp, rocr,
rocr100, rsi, stoch_slowk, stoch_slowd, stochf_fastk, stochf_fastd, stochrsi, trix,
ultosc, willr)
Volume Indicators¶
| Short Name | Full Name and Description | Code Example |
|---|---|---|
| AD | Chaikin A/D Line Measures the cumulative flow of money into and out of a security. |
ad = qnt.xr_talib.AD(data) |
| ADOSC | Chaikin A/D Oscillator Measures the momentum of the Chaikin A/D Line. |
adosc = qnt.xr_talib.ADOSC(data, 3, 10) |
| OBV | On Balance Volume Measures the cumulative volume in relation to price changes. |
obv = qnt.xr_talib.OBV(data) |
import qnt.xr_talib as xr_talib
import qnt.data as qndata
data = qndata.stocks.load_ndx_data(min_date="2005-06-01")
ad = xr_talib.AD(data)
adosc = xr_talib.ADOSC(data, 3, 10)
obv = xr_talib.OBV(data)
print(ad, adosc, obv)
Cycle Indicators¶
| Short Name | Full Name and Description | Code Example |
|---|---|---|
| HT_DCPERIOD | Hilbert Transform - Dominant Cycle Period Estimates the dominant cycle period of a time series. |
ht_dcperiod = qnt.xr_talib.HT_DCPERIOD(close) |
| HT_DCPHASE | Hilbert Transform - Dominant Cycle Phase Estimates the phase of the dominant cycle in a time series. |
ht_dcphase = qnt.xr_talib.HT_DCPHASE(close) |
| HT_PHASOR | Hilbert Transform - Phasor Components Computes the in-phase and quadrature components of a time series. |
inphase, quadrature = qnt.xr_talib.HT_PHASOR(close) |
| HT_SINE | Hilbert Transform - SineWave Computes the sine and lead sine of a time series. |
sine, leadsine = qnt.xr_talib.HT_SINE(close) |
| HT_TRENDLINE | Hilbert Transform - Instantaneous Trendline Computes a smoothed, nearly lagless trendline of a time series. |
ht_trendline = qnt.xr_talib.HT_TRENDLINE(close) |
| HT_TRENDMODE | Hilbert Transform - Trend vs Cycle Mode Determines if the dominant cycle of a time series is trending or oscillating. |
ht_trendmode = qnt.xr_talib.HT_TRENDMODE(close) |
import qnt.xr_talib as xr_talib
import qnt.data as qndata
data = qndata.stocks.load_ndx_data(min_date="2005-06-01")
ht_dcperiod = xr_talib.HT_DCPERIOD(data)
ht_dcphase = xr_talib.HT_DCPHASE(data)
ht_phasor = xr_talib.HT_PHASOR(data)
ht_sine = xr_talib.HT_SINE(data)
ht_trendline = xr_talib.HT_TRENDLINE(data)
ht_trendmode = xr_talib.HT_TRENDMODE(data)
print(ht_dcperiod, ht_dcphase, ht_phasor, ht_sine, ht_trendline, ht_trendmode)
Volatility Indicators¶
| Short Name | Full Name and Description | Code Example |
|---|---|---|
| ATR | Average True Range Measures the market volatility by decomposing the entire range of an asset price for the period. |
atr = qnt.xr_talib.ATR(data, 14) |
| NATR | Normalized Average True Range Normalizes the Average True Range by dividing by the close price. |
natr = qnt.xr_talib.NATR(data, 14) |
| TRANGE | True Range Calculates the true range of a time series, accounting for gaps between periods. |
trange = qnt.xr_talib.TRANGE(data) |
import qnt.xr_talib as xr_talib
import qnt.data as qndata
data = qndata.stocks.load_ndx_data(min_date="2005-06-01")
atr = xr_talib.ATR(data, timeperiod=14)
natr = xr_talib.NATR(data, timeperiod=14)
trange = xr_talib.TRANGE(data)
print(atr, natr, trange)
Price Transform¶
| Short Name | Full Name and Description | Code Example |
|---|---|---|
| TYPPRICE | Typical Price Calculates the typical price for a specified period. |
typprice = qnt.xr_talib.TYPPRICE(data) |
| WCLPRICE | Weighted Close Price Calculates the weighted close price for a specified period. |
wclprice = qnt.xr_talib.WCLPRICE(data) |
import qnt.xr_talib as xr_talib
import qnt.data as qndata
data = qndata.stocks.load_ndx_data(min_date="2005-06-01")
typprice = xr_talib.TYPPRICE(data)
wclprice = xr_talib.WCLPRICE(data)
print(typprice, wclprice)
Pattern Recognition¶
| Short Name | Full Name and Description | Code Example |
|---|---|---|
| CDL2CROWS | Two Crows Identifies a bearish reversal pattern. |
cdl2crows = qnt.xr_talib.CDL2CROWS(data) |
| CDL3BLACKCROWS | Three Black Crows Identifies a bearish reversal pattern consisting of three consecutive black candles. |
cdl3blackcrows = qnt.xr_talib.CDL3BLACKCROWS(data) |
| CDL3INSIDE | Three Inside Up/Down Identifies a bullish or bearish reversal pattern. |
cdl3inside = qnt.xr_talib.CDL3INSIDE(data) |
| CDL3LINESTRIKE | Three Line Strike Identifies a bullish or bearish reversal pattern. |
cdl3linestrike = qnt.xr_talib.CDL3LINESTRIKE(data) |
| CDL3OUTSIDE | Three Outside Up/Down Identifies a bullish or bearish reversal pattern. |
cdl3outside = qnt.xr_talib.CDL3OUTSIDE(data) |
| CDL3STARSINSOUTH | Three Stars In The South Identifies a bullish reversal pattern. |
cdl3starsinsouth = qnt.xr_talib.CDL3STARSINSOUTH(data) |
| CDL3WHITESOLDIERS | Three Advancing White Soldiers Identifies a bullish reversal pattern. |
cdl3whitesoldiers = qnt.xr_talib.CDL3WHITESOLDIERS(data) |
| CDLABANDONEDBABY | Abandoned Baby Identifies a bullish or bearish reversal pattern. |
cdlabandonedbaby = qnt.xr_talib.CDLABANDONEDBABY(data, 0.3) |
| CDLADVANCEBLOCK | Advance Block Identifies a bearish reversal pattern. |
cdladvanceblock = qnt.xr_talib.CDLADVANCEBLOCK(data) |
| CDLBELTHOLD | Belt-hold Identifies a bullish or bearish opening pattern. |
cdlbelthold = qnt.xr_talib.CDLBELTHOLD(data) |
| CDLBREAKAWAY | Breakaway Identifies a bullish or bearish reversal pattern. |
cdlbreakaway = qnt.xr_talib.CDLBREAKAWAY(data) |
| CDLCLOSINGMARUBOZU | Closing Marubozu Identifies a bullish or bearish continuation pattern. |
cdlclosingmarubozu = qnt.xr_talib.CDLCLOSINGMARUBOZU(data) |
| CDLCONCEALBABYSWALL | Concealing Baby Swallow Identifies a bullish reversal pattern. |
cdlconcealbabyswall = qnt.xr_talib.CDLCONCEALBABYSWALL(data) |
| CDLCOUNTERATTACK | Counterattack Identifies a bullish or bearish reversal pattern. |
cdlcounterattack = qnt.xr_talib.CDLCOUNTERATTACK(data) |
| CDLDARKCLOUDCOVER | Dark Cloud Cover Identifies a bearish reversal pattern. |
cdldarkcloudcover = qnt.xr_talib.CDLDARKCLOUDCOVER(data, 0.5) |
| CDLDOJI | Doji Identifies a Doji candlestick pattern. |
cdldoji = qnt.xr_talib.CDLDOJI(data) |
| CDLDOJISTAR | Doji Star Identifies a Doji Star candlestick pattern. |
cdldojistar = qnt.xr_talib.CDLDOJISTAR(data) |
| CDLDRAGONFLYDOJI | Dragonfly Doji Identifies a Dragonfly Doji candlestick pattern. |
cdldragonflydoji = qnt.xr_talib.CDLDRAGONFLYDOJI(data) |
| CDLENGULFING | Engulfing Pattern Identifies a bullish or bearish engulfing pattern. |
cdlengulfing = qnt.xr_talib.CDLENGULFING(data) |
| CDLEVENINGDOJISTAR | Evening Doji Star Identifies a bearish reversal pattern. |
cdleveningdojistar = qnt.xr_talib.CDLEVENINGDOJISTAR(data, 0.3) |
| CDLEVENINGSTAR | Evening Star Identifies a bearish reversal pattern. |
cdleveningstar = qnt.xr_talib.CDLEVENINGSTAR(data, 0.3) |
| CDLGAPSIDESIDEWHITE | Up/Down-gap side-by-side white lines Identifies a bullish or bearish continuation pattern. |
cdlgapsidesidewhite = qnt.xr_talib.CDLGAPSIDESIDEWHITE(data) |
| CDLGRAVESTONEDOJI | Gravestone Doji Identifies a Gravestone Doji candlestick pattern. |
cdlgravestonedoji = qnt.xr_talib.CDLGRAVESTONEDOJI(data) |
| CDLHAMMER | Hammer Identifies a bullish reversal pattern. |
cdlhammer = qnt.xr_talib.CDLHAMMER(data) |
| CDLHANGINGMAN | Hanging Man Identifies a bearish reversal pattern. |
cdlhangingman = qnt.xr_talib.CDLHANGINGMAN(data) |
| CDLHARAMI | Harami Pattern Identifies a bullish or bearish reversal pattern. |
cdlharami = qnt.xr_talib.CDLHARAMI(data) |
| CDLHARAMICROSS | Harami Cross Pattern Identifies a bullish or bearish reversal pattern. |
cdlharamicross = qnt.xr_talib.CDLHARAMICROSS(data) |
| CDLHIGHWAVE | High-Wave Candle Identifies a High-Wave candlestick pattern. |
cdlhighwave = qnt.xr_talib.CDLHIGHWAVE(data) |
| CDLHIKKAKE | Hikkake Pattern Identifies a bullish or bearish reversal pattern. |
cdlhikkake = qnt.xr_talib.CDLHIKKAKE(data) |
| CDLHIKKAKEMOD | Modified Hikkake Pattern Identifies a bullish or bearish reversal pattern. |
cdlhikkakemod = qnt.xr_talib.CDLHIKKAKEMOD(data) |
| CDLHOMINGPIGEON | Homing Pigeon Identifies a bullish reversal pattern. |
cdlhomingpigeon = qnt.xr_talib.CDLHOMINGPIGEON(data) |
| CDLIDENTICAL3CROWS | Identical Three Crows Identifies a bearish reversal pattern. |
cdlidentical3crows = qnt.xr_talib.CDLIDENTICAL3CROWS(data) |
| CDLINNECK | In-Neck Pattern Identifies a bearish continuation pattern. |
cdlinneck = qnt.xr_talib.CDLINNECK(data) |
| CDLINVERTEDHAMMER | Inverted Hammer Identifies a bullish reversal pattern. |
cdlinvertedhammer = qnt.xr_talib.CDLINVERTEDHAMMER(data) |
| CDLKICKING | Kicking Identifies a bullish or bearish reversal pattern. |
cdlkicking = qnt.xr_talib.CDLKICKING(data) |
| CDLKICKINGBYLENGTH | Kicking - bull/bear determined by the longer marubozu Identifies a bullish or bearish reversal pattern. |
cdlkickingbylength = qnt.xr_talib.CDLKICKINGBYLENGTH(data) |
| CDLLADDERBOTTOM | Ladder Bottom Identifies a bullish reversal pattern. |
cdlladderbottom = qnt.xr_talib.CDLLADDERBOTTOM(data) |
| CDLLONGLEGGEDDOJI | Long Legged Doji Identifies a Long Legged Doji candlestick pattern. |
cdllongleggeddoji = qnt.xr_talib.CDLLONGLEGGEDDOJI(data) |
| CDLLONGLINE | Long Line Candle Identifies a Long Line candlestick pattern. |
cdllongline = qnt.xr_talib.CDLLONGLINE(data) |
| CDLMARUBOZU | Marubozu Identifies a Marubozu candlestick pattern. |
cdlmarubozu = qnt.xr_talib.CDLMARUBOZU(data) |
| CDLMATCHINGLOW | Matching Low Identifies a bullish reversal pattern. |
cdlmatchinglow = qnt.xr_talib.CDLMATCHINGLOW(data) |
| CDLMATHOLD | Mat Hold Identifies a bullish continuation pattern. |
cdlmathold = qnt.xr_talib.CDLMATHOLD(data, 0.5) |
| CDLMORNINGDOJISTAR | Morning Doji Star Identifies a bullish reversal pattern. |
cdlmorningdojistar = qnt.xr_talib.CDLMORNINGDOJISTAR(data, 0.3) |
| CDLMORNINGSTAR | Morning Star Identifies a bullish reversal pattern. |
cdlmorningstar = qnt.xr_talib.CDLMORNINGSTAR(data, 0.3) |
| CDLONNECK | On-Neck Pattern Identifies a bearish continuation pattern. |
cdlonneck = qnt.xr_talib.CDLONNECK(data) |
| CDLPIERCING | Piercing Pattern Identifies a bullish reversal pattern. |
cdlpiercing = qnt.xr_talib.CDLPIERCING(data) |
| CDLRICKSHAWMAN | Rickshaw Man Identifies a Rickshaw Man candlestick pattern. |
cdlrickshawman = qnt.xr_talib.CDLRICKSHAWMAN(data) |
| CDLRISEFALL3METHODS | Rising/Falling Three Methods Identifies a bullish or bearish continuation pattern. |
cdlrisefall3methods = qnt.xr_talib.CDLRISEFALL3METHODS(data) |
| CDLSEPARATINGLINES | Separating Lines Identifies a bullish or bearish continuation pattern. |
cdlseparatinglines = qnt.xr_talib.CDLSEPARATINGLINES(data) |
| CDLSHOOTINGSTAR | Shooting Star Identifies a bearish reversal pattern. |
cdlshootingstar = qnt.xr_talib.CDLSHOOTINGSTAR(data) |
| CDLSHORTLINE | Short Line Candle Identifies a Short Line candlestick pattern. |
cdlshortline = qnt.xr_talib.CDLSHORTLINE(data) |
| CDLSPINNINGTOP | Spinning Top Identifies a Spinning Top candlestick pattern. |
cdlspinningtop = qnt.xr_talib.CDLSPINNINGTOP(data) |
| CDLSTALLEDPATTERN | Stalled Pattern Identifies a bearish reversal pattern. |
cdlstalledpattern = qnt.xr_talib.CDLSTALLEDPATTERN(data) |
| CDLSTICKSANDWICH | Stick Sandwich Identifies a bullish reversal pattern. |
cdlsticksandwich = qnt.xr_talib.CDLSTICKSANDWICH(data) |
| CDLTAKURI | Takuri (Dragonfly Doji with very long lower shadow) Identifies a bullish reversal pattern. |
cdltakuri = qnt.xr_talib.CDLTAKURI(data) |
| CDLTASUKIGAP | Tasuki Gap Identifies a bullish or bearish continuation pattern. |
cdltasukigap = qnt.xr_talib.CDLTASUKIGAP(data) |
| CDLTHRUSTING | Thrusting Pattern Identifies a bearish continuation pattern. |
cdlthrusting = qnt.xr_talib.CDLTHRUSTING(data) |
| CDLTRISTAR | Tristar Pattern Identifies a Tristar candlestick pattern. |
cdltristar = qnt.xr_talib.CDLTRISTAR(data) |
| CDLUNIQUE3RIVER | Unique 3 River Identifies a bullish reversal pattern. |
cdlunique3river = qnt.xr_talib.CDLUNIQUE3RIVER(data) |
| CDLUPSIDEGAP2CROWS | Upside Gap Two Crows Identifies a bearish reversal pattern. |
cdlupsidegap2crows = qnt.xr_talib.CDLUPSIDEGAP2CROWS(data) |
| CDLXSIDEGAP3METHODS | Upside/Downside Gap Three Methods Identifies a bullish or bearish continuation pattern. |
cdlxsidegap3methods = qnt.xr_talib.CDLXSIDEGAP3METHODS(data) |
import qnt.xr_talib as xr_talib
import qnt.data as qndata
data = qndata.stocks.load_ndx_data(min_date="2005-06-01")
cdl2crows = xr_talib.CDL2CROWS(data)
cdl3blackcrows = xr_talib.CDL3BLACKCROWS(data)
cdl3inside = xr_talib.CDL3INSIDE(data)
cdl3linestrike = xr_talib.CDL3LINESTRIKE(data)
cdl3outside = xr_talib.CDL3OUTSIDE(data)
cdl3starsinsouth = xr_talib.CDL3STARSINSOUTH(data)
cdl3whitesoldiers = xr_talib.CDL3WHITESOLDIERS(data)
cdlabandonedbaby = xr_talib.CDLABANDONEDBABY(data, penetration=0.3)
cdladvanceblock = xr_talib.CDLADVANCEBLOCK(data)
cdlbelthold = xr_talib.CDLBELTHOLD(data)
cdlbreakaway = xr_talib.CDLBREAKAWAY(data)
cdlclosingmarubozu = xr_talib.CDLCLOSINGMARUBOZU(data)
cdlconcealbabyswall = xr_talib.CDLCONCEALBABYSWALL(data)
cdlcounterattack = xr_talib.CDLCOUNTERATTACK(data)
cdldarkcloudcover = xr_talib.CDLDARKCLOUDCOVER(data, penetration=0.5)
cdldoji = xr_talib.CDLDOJI(data)
cdldojistar = xr_talib.CDLDOJISTAR(data)
cdldragonflydoji = xr_talib.CDLDRAGONFLYDOJI(data)
cdlengulfing = xr_talib.CDLENGULFING(data)
cdleveningdojistar = xr_talib.CDLEVENINGDOJISTAR(data, penetration=0.3)
cdleveningstar = xr_talib.CDLEVENINGSTAR(data, penetration=0.3)
cdlgapsidesidewhite = xr_talib.CDLGAPSIDESIDEWHITE(data)
cdlgravestonedoji = xr_talib.CDLGRAVESTONEDOJI(data)
cdlhammer = xr_talib.CDLHAMMER(data)
cdlhangingman = xr_talib.CDLHANGINGMAN(data)
cdlharami = xr_talib.CDLHARAMI(data)
cdlharamicross = xr_talib.CDLHARAMICROSS(data)
cdlhighwave = xr_talib.CDLHIGHWAVE(data)
cdlhikkake = xr_talib.CDLHIKKAKE(data)
cdlhikkakemod = xr_talib.CDLHIKKAKEMOD(data)
cdlhomingpigeon = xr_talib.CDLHOMINGPIGEON(data)
cdlidentical3crows = xr_talib.CDLIDENTICAL3CROWS(data)
cdlinneck = xr_talib.CDLINNECK(data)
cdlinvertedhammer = xr_talib.CDLINVERTEDHAMMER(data)
cdlkicking = xr_talib.CDLKICKING(data)
cdlkickingbylength = xr_talib.CDLKICKINGBYLENGTH(data)
cdlladderbottom = xr_talib.CDLLADDERBOTTOM(data)
cdllongleggeddoji = xr_talib.CDLLONGLEGGEDDOJI(data)
cdllongline = xr_talib.CDLLONGLINE(data)
cdlmarubozu = xr_talib.CDLMARUBOZU(data)
cdlmatchinglow = xr_talib.CDLMATCHINGLOW(data)
cdlmathold = xr_talib.CDLMATHOLD(data, penetration=0.5)
cdlmorningdojistar = xr_talib.CDLMORNINGDOJISTAR(data, penetration=0.3)
cdlmorningstar = xr_talib.CDLMORNINGSTAR(data, penetration=0.3)
cdlonneck = xr_talib.CDLONNECK(data)
cdlpiercing = xr_talib.CDLPIERCING(data)
cdlrickshawman = xr_talib.CDLRICKSHAWMAN(data)
cdlrisefall3methods = xr_talib.CDLRISEFALL3METHODS(data)
cdlseparatinglines = xr_talib.CDLSEPARATINGLINES(data)
cdlshootingstar = xr_talib.CDLSHOOTINGSTAR(data)
cdlshortline = xr_talib.CDLSHORTLINE(data)
cdlspinningtop = xr_talib.CDLSPINNINGTOP(data)
cdlstalledpattern = xr_talib.CDLSTALLEDPATTERN(data)
cdlsticksandwich = xr_talib.CDLSTICKSANDWICH(data)
cdltakuri = xr_talib.CDLTAKURI(data)
cdltasukigap = xr_talib.CDLTASUKIGAP(data)
cdlthrusting = xr_talib.CDLTHRUSTING(data)
cdltristar = xr_talib.CDLTRISTAR(data)
cdlunique3river = xr_talib.CDLUNIQUE3RIVER(data)
cdlupsidegap2crows = xr_talib.CDLUPSIDEGAP2CROWS(data)
cdlxsidegap3methods = xr_talib.CDLXSIDEGAP3METHODS(data)
print(cdl2crows, cdl3blackcrows, cdl3inside, cdl3linestrike,
cdl3outside, cdl3starsinsouth, cdl3whitesoldiers,
cdlabandonedbaby, cdladvanceblock, cdlbelthold,
cdlbreakaway, cdlclosingmarubozu, cdlconcealbabyswall,
cdlcounterattack, cdldarkcloudcover, cdldoji, cdldojistar,
cdldragonflydoji, cdlengulfing, cdleveningdojistar, cdleveningstar,
cdlgapsidesidewhite, cdlgravestonedoji, cdlhammer, cdlhangingman,
cdlharami, cdlharamicross, cdlhighwave, cdlhikkake, cdlhikkakemod,
cdlhomingpigeon, cdlidentical3crows, cdlinneck, cdlinvertedhammer,
cdlkicking, cdlkickingbylength, cdlladderbottom, cdllongleggeddoji,
cdllongline, cdlmarubozu, cdlmatchinglow, cdlmathold,
cdlmorningdojistar, cdlmorningstar, cdlonneck, cdlpiercing,
cdlrickshawman, cdlrisefall3methods, cdlseparatinglines,
cdlshootingstar, cdlshortline, cdlspinningtop, cdlstalledpattern,
cdlsticksandwich, cdltakuri, cdltasukigap, cdlthrusting, cdltristar,
cdlunique3river, cdlupsidegap2crows, cdlxsidegap3methods)
Statistic Functions¶
| Short Name | Full Name and Description | Code Example |
|---|---|---|
| BETA | Beta Calculates the beta coefficient of a stock in relation to a benchmark index. |
beta, _ = talib.BETA(high, low, timeperiod=5) |
| CORREL | Correlation Coefficient Calculates the Pearson correlation coefficient between two datasets. |
correl, _ = talib.CORREL(high, low, timeperiod=30) |
| LINEARREG | Linear Regression Performs a linear regression on the given dataset. |
linearreg, _ = talib.LINEARREG(close, timeperiod=14) |
| LINEARREG_ANGLE | Linear Regression Angle Calculates the angle of the linear regression line. |
linearreg_angle, _ = talib.LINEARREG_ANGLE(close, timeperiod=14) |
| LINEARREG_INTERCEPT | Linear Regression Intercept Calculates the intercept of the linear regression line. |
linearreg_intercept, _ = talib.LINEARREG_INTERCEPT(close, timeperiod=14) |
| LINEARREG_SLOPE | Linear Regression Slope Calculates the slope of the linear regression line. |
linearreg_slope, _ = talib.LINEARREG_SLOPE(close, timeperiod=14) |
| STDDEV | Standard Deviation Calculates the standard deviation of the given dataset. |
stddev, _ = talib.STDDEV(close, timeperiod=5) |
| TSF | Time Series Forecast Calculates the Time Series Forecast value for the given dataset. |
tsf, _ = talib.TSF(close, timeperiod=14) |
| VAR | Variance Calculates the variance of the given dataset. |
var, _ = talib.VAR(close, timeperiod=5) |
import qnt.xr_talib as xr_talib
import qnt.data as qndata
data = qndata.stocks.load_ndx_data(min_date="2005-06-01")
close = data.sel(field="close")
is_liquid = data.sel(field="is_liquid")
beta = xr_talib.BETA(close, is_liquid, timeperiod=5)
correl = xr_talib.CORREL(close, is_liquid, timeperiod=30)
linearreg = xr_talib.LINEARREG(data, timeperiod=14)
linearreg_angle = xr_talib.LINEARREG_ANGLE(data, timeperiod=14)
linearreg_intercept = xr_talib.LINEARREG_INTERCEPT(data, timeperiod=14)
linearreg_slope = xr_talib.LINEARREG_SLOPE(data, timeperiod=14)
stddev = xr_talib.STDDEV(data, timeperiod=5)
tsf = xr_talib.TSF(data, timeperiod=14)
var = xr_talib.VAR(data, timeperiod=5)
print(beta, correl, linearreg, linearreg_angle, linearreg_intercept, linearreg_slope, stddev, tsf, var)
Examples¶
Q18 Technical Analysis using Index Data - This strategy uses the SP500 index data and technical indicators to trade NASDAQ stocks.
Technical Analysis using trix, ema - The template defines a trading strategy that combines TRIX and EMA technical indicators to calculate weights for NASDAQ 100 stocks.
Technical Analysis using atr, lwma - The template defines a trading strategy that combines ATR and LWMA technical indicators to calculate weights for NASDAQ 100 stocks.