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Financial Technical Analysis in Python

License: MIT License

Python 100.00%

ta.py's Introduction

Technical Analysis (ta.py)

ta.py is a Python package for dealing with financial technical analysis.

Installation

pip

Use the package manager pip to install ta.py.

pip install ta_py

Usage

import ta_py as ta;

Examples

Moving Averages

Simple Moving Average (SMA)

data = [1, 2, 3, 4, 5, 6, 10];
length = 6; # default = 14
ta.sma(data, length);
# output (array)
# [3.5, 5]

Smoothed Moving Average (SMMA)

data = [1, 2, 3, 4, 5, 6, 10];
length = 5; # default = 14
ta.smma(data, length);
# output (array)
# [3.4, 4.92]

Weighted Moving Average (WMA)

data = [69, 68, 66, 70, 68];
length = 4; # default = 14
ta.wma(data, length);
# output (array)
# [68.3, 68.2]

Exponential Moving Average (EMA)

data = [1, 2, 3, 4, 5, 6, 10];
length = 6; # default = 12
ta.ema(data, length);
# output (array)
# [3.5, 5.357]

Hull Moving Average

data = [6, 7, 5, 6, 7, 4, 5, 7];
length = 6; # default = 14
ta.hull(data, length);
# output (array)
# [4.76, 5.48]

Least Squares Moving Average (LSMA)

data = [5, 6, 6, 3, 4, 6, 7];
length = 6; # default = 25
ta.lsma(data, length);
# output (array)
# [4.714, 5.761]

Volume Weighted Moving Average (VWMA)

data = [[1, 59], [1.1, 82], [1.21, 27], [1.42, 73], [1.32, 42]]; # [price, volume (quantity)]
length = 4; # default = 20
ta.vwma(data, length);
# output (array)
# [1.185, 1.259]

Volume Weighted Weighted Moving Average (VWWMA)

data = [[1,59],[1.1,82],[1.21,27],[1.42,73],[1.32,42]];
length = 4;
ta.vwwma(data, length);
# output (array)
# [1.262, 1.316]

Wilder's Smoothing Moving Average

data = [1, 2, 3, 4, 5, 6, 10];
length = 6; # default = 14
ta.wsma(data, length);
# output (array)
# [3.5, 4.58]

Parabolic Weighted Moving Average

 data = [17, 26, 23, 29, 20];
 length = 4; # default = 14
ta.pwma(data, length);
# output (array)
# [24.09, 25.18]

Hyperbolic Weighted Moving Average

data = [54, 51, 86, 42, 47];
length = 4; # default = 14
ta.hwma(data, length);
# output (array)
# [56.2, 55.0]

Kaufman Adaptive Moving Average (KAMA)

data = [8, 7, 8, 9, 7, 9];
length1 = 2; # default = 10
length2 = 4; # default = 2
length3 = 8; # default = 30
ta.kama(data, length1, length2, length3);
# output (array)
# [8, 8.64, 8.57, 8.57]

Custom Weighted Moving Average

data = [69,68,66,70,68,69];
weights = [1,2,3,5,8];
ta.cwma(data, weights);
# output (array)
# [68.26315789473684, 68.52631578947368]

Indicators

Moving Average Convergence / Divergence (MACD)

data = [1, 2, 3, 4, 5, 6, 14];
length1 = 3; # default = 12
length2 = 6; # default = 26
ta.macd(data, length1, length2);
# output (array)
# [1.5, 3]

Relative Strength Index (RSI)

data = [1, 2, 3, 4, 5, 6, 7, 5];
length = 6; # default = 14
ta.rsi(data, length);
# output (array)
# [100, 100, 66.667]

Wilder's Relative Strength Index

data = [1, 2, 3, 4, 5, 6, 7, 5, 6];
length = 6; # default = 14
ta.wrsi(data, length);
# output (array)
# [100, 71.43, 75.61]

True Strength Index (TSI)

data = [1.32, 1.27, 1.42, 1.47, 1.42, 1.45, 1.59];
longlength = 3; # default = 25
shortlength = 2; # default = 13
signallength = 2; # default = 13
ta.tsi(data, longlength, shortlength, signallength);
# output (array)
# [[0.327, 0.320], [0.579, 0.706]]
# [strength line, signal line]

Balance Of Power

data = [[4, 5, 4, 5], [5, 6, 5, 6], [6, 8, 5, 6]]; # [open, high, low, close]
length = 2; # default = 14
ta.bop(data, length);
# output (array)
# [1, 0.5]

Force Index

data = [[1.4, 200], [1.5, 240], [1.1, 300], [1.2, 240], [1.5, 400]]; # [close, volume]
length = 4; # default = 13
ta.fi(data, length);
# output (array)
# [0.0075]

Accumulative Swing Index

data = [[7, 6, 4], [9, 7, 5], [9, 8, 6]]; # [high, close, low]
ta.asi(data);
# output (array)
# [0, -12.5]

Alligator Indicator

data = [8,7,8,9,7,8,9,6,7,8,6,8,10,8,7,9,8,7,9,6,7,9];
# defaults shown
jawlength = 13;
teethlength = 8;
liplength = 5;
jawshift = 8;
teethshift = 5;
lipshift = 3;
ta.alligator(data, jawlength, teethlength, liplength, jawshift, teethshift, lipshift);
# output (array)
# [jaw, teeth, lips]

Williams %R

data = [2, 1, 3, 1, 2];
length = 3; # default = 14
ta.pr(data, length);
# output (array)
# [-0, -100, -50]

Stochastics

data = [[3,2,1], [2,2,1], [4,3,1], [2,2,1]]; # [high, close, low]
length = 2; # default = 14
smoothd = 1; # default = 3
smoothk = 1; # default = 3
ta.stoch(data, length, smoothd, smoothk);
# output (array)
# [[66.667, 66.667], [33.336, 33.336]]
# [kline, dline]

Fibonacci Retracement

start = 1;
end = 2;
ta.fib(start, end);
# output (array)
# [1, 1.236, 1.382, 1.5, 1.618, 1.786, 2, 2.618, 3.618, 4.618, 5.236]

Bollinger Bandwidth

data = [1, 2, 3, 4, 5, 6];
length = 5; # default = 14
deviations = 2; # default = 1
ta.bandwidth(data, length, deviations);
# output (array)
# [1.886, 1.344]

Ichimoku Cloud

data = [[6, 3, 2], [5, 4, 2], [5, 4, 3], [6, 4, 3], [7, 6, 4], [6, 5, 3]]; # [high, close, low]
length1 = 9; # default = 9
length2 = 26; # default = 26
length3 = 52; # default = 52
displacement = 26; # default = 26
ta.ichimoku(data, length1, length2, length3, displacement);
# output (array)
# [conversion line, base line, leading span A, leading span B, lagging span]

Average True Range (ATR)

data = [[3,2,1], [2,2,1], [4,3,1], [2,2,1]]; # [high, close, low]
length = 3; # default = 14
ta.atr(data, length);
# output (array)
# [2, 1.667, 2.111, 1.741]

Aroon Up

data = [5, 4, 5, 2];
length = 3; # default = 10
ta.aroon_up(data, length);
# output (array)
# [100.0, 50.0]

Aroon Down

data = [2, 5, 4, 5];
length = 3; # default = 10
ta.aroon_down(data, length);
# output (array)
# [0.0, 50.0]

Money Flow Index

data = [[19, 13], [14, 38], [21, 25], [32, 17]]; # [buy volume, sell volume]
length = 3; # default = 14
ta.mfi(data, length);
# output (array)
# [41.54, 45.58]

Rate Of Change

data = [1, 2, 3, 4];
length = 3; # default = 14
ta.roc(data, length);
# output (array)
# [2, 1]

Coppock Curve

data = [3, 4, 5, 3, 4, 5, 6, 4, 7, 5, 4, 7, 5];
length1 = 4; # (ROC period 1) default = 11
length2 = 6; # (ROC period 2) default = 14
length3 = 5; # (WMA smoothing period) default = 10
ta.cop(data, length1, length2, length3);
# output (array)
# [0.376, 0.237]

Know Sure Thing

data = [8, 6, 7, 6, 8, 9, 7, 5, 6, 7, 6, 8, 6, 7, 6, 8, 9, 9, 8, 6, 4, 6, 5, 6, 7, 8, 9];
# roc sma #1
r1 = 5; # default = 10
s1 = 5; # default = 10
# roc sma #2
r2 = 7; # default = 15
s2 = 5; # default = 10
# roc sma #3
r3 = 10; # default = 20
s3 = 5; # default = 10
# roc sma #4
r4 = 15; # default = 30
s4 = 7; # default = 15
# signal line
sig = 4; # default = 9
ta.kst(data, r1, s1, r2, s2, r3, s3, r4, s4, sig);
# output (array)
# [[-0.68, -0.52], [-0.29, -0.58], [0.35, -0.36]]
# [kst line, signal line]

On-Balance Volume

data = [[25200, 10], [30000, 10.15], [25600, 10.17], [32000, 10.13]]; # [asset volume, close price]
ta.obv(data);
# output (array)
# [0, 30000, 55600, 23600]

Volume-Weighted Average Price

data = [[127.21, 89329], [127.17, 16137], [127.16, 23945]]; # [average price, volume (quantity)]
length = 2; # default = len(length)
ta.vwap(data, length);
# output (array)
# [127.204, 127.164]

Fractals

data = [[7,6],[8,6],[9,6],[8,5],[7,4],[6,3],[7,4],[8,5]]; # [high, low]
ta.fractals(data);
# output (array, same length as input)
# [[false, false],[false,false],[true,false],[false,false],[false,false],[false,true],[false,false],[false,false]]
# [upper fractal, lower fractal]

Crossover (golden cross)

fastdata = [3,4,5,4,3]; # short period gets spliced when longer
slowdata = [4,3,2,3,4];
ta.cross(fastdata, slowdata);
# output (array)
# [{index: 1, cross True}, {index: 4, cross: False}]
# cross is true when fastdata is greater than the slowdata

Momentum

data = [1, 1.1, 1.2, 1.24, 1.34];
length = 4; # default = 10
percentage = false; # default = false (true returns percentage)
ta.mom(data, length, percentage);
# output (array)
# [0.24, 0.24]

HalfTrend

# experimental (untested) function (may change in the future), ported from:
# https://www.tradingview.com/script/U1SJ8ubc-HalfTrend/
# data = [high, close, low]
data = [[100,97,90],[101,98,94],[103,96,92],[106,100,95],[110,101,100],[112,110,105],[110,100,90],[103,100,97],[95,90,85],[94,80,80],[90,82,81],[85,80,70]];
atrlen = 6;
amplitude = 3;
deviation = 2;
ta.halftrend(data, atrlen, amplitude, deviation);
# output (array)
# [
#   [ 115.14, 105, 94.86, 'long' ],
#   [ 100.77, 90, 79.22, 'long' ],
#   [ 116.32, 105, 93.68, 'long' ],
#   [ 101.1, 90, 78.89, 'long' ],
#   [ 116.25, 105, 93.75, 'long' ],
#   [ 99.77, 90, 80.23, 'long' ]
# ]

ZigZag

# Based on high / low
data = [[10,9], [12,10], [14,12], [15,13], [16,15], [11,10], [18,15]]; # [high, low]
percentage = 0.25; # default = 0.05
ta.zigzag(data, percentage);
# output (array)
# [9, 10.75, 12.5, 14.25, 16, 10, 18]

# Based on close
data = [6,7,8,9,10,12,9,8,5,3,3,3,5,7,8,9,11];
percentage = 0.05;
ta.zigzag(data, percentage);
# output (array)
# [6, 7.2, 8.4, 9.6, 10.8, 12.0, 10.5, 9.0, 7.5, 6.0, 4.5, 3.0, 4.6, 6.2, 7.8, 9.4, 11.0]

Parabolic SAR

data = [[82.15,81.29],[81.89,80.64],[83.03,81.31],[83.30,82.65],[83.85,83.07],[83.90,83.11],[83.33,82.49],[84.30,82.3],[84.84,84.15],[85,84.11],[75.9,74.03],[76.58,75.39],[76.98,75.76],[78,77.17],[70.87,70.01]];
step = 0.02;
max = 0.2;
ta.psar(data, step, max);
# output (array)
# [81.29,82.15,80.64,80.64,80.7464,80.932616,81.17000672,81.3884061824,81.67956556416,82.0588176964608,85,85,84.7806,84.565588,84.35487624000001]

SuperTrend

data = [[3,2,1], [2,2,1], [4,3,1], [2,2,1]]; # [high, close, low]
length = 3;
multiplier = 0.5;
ta.supertrend(data, length, multiplier);
# output (array)
# [[5.56,1.44],[3.37,0.63]]
# [up, down]

Elder Ray Index

data = [6,5,4,7,8,9,6,8];
length = 7;
ta.elderray(data, length);
# output (array)
# [[2.57,-2.43],[2.29,-2.71]]
# [bull, bear]

Historical Volatility

data = [7,6,5,7,8,9,7,6,5];
length = 8;
ta.hv(data, length);
# output (array)
# [0.642, 0.682]

Relative Vigor Index

# data = [[open,high,low,close]] (requires at least 4 + length values)
data = [[4,6,3,3], [3,5,2,2], [2,5,2,4], [4,6,4,5], [5,7,4,4], [4,6,3,4], [4,7,3,5], [5,7,5,6], [6,8,6,6], [6,9,5,6], [6,8,6,7], [7,9,5,6],[6,7,4,5],[5,6,5,6],[6,8,5,5],[5,7,2,6]];
length = 8;
ta.rvi(data, length);
# output (array)
# [0.29,0.21,0.15,0.16,0.09,0.05]

Relative Vigor Index Signal

rvi = [0.29,0.21,0.15,0.16,0.09,0.05]; # requires at least 4 values
ta.rvi_signal(rvi);
# output (array)
# [0.20,0.15,0.12]

RSI Divergence

Experimental function: Bitvested/ta.js#18

data = [74,83,66,78,69,70,84,73,74,73,83];
rsi_length = 5;
rsi_function = ta.wrsi; # default (the tradingview rsi indicator)
ta.rsi_divergence(data, rsi_length, rsi_function);
# output (array)
# 1 = RSI is in divergence
# 0 = RSI is not in divergence
# [0, 0, 1, 0, 1, 0] (better to quantify if needed)

Universal Divergence

data1 = [48,34,43,54,56,64,43];
data2 = [76,74,43,55,34,32,45,47];
ta.divergence(data1, data2);
# output (array)
# 1 = RSI is in divergence
# 0 = RSI is not in divergence
# [0, 0, 1, 1, 0, 1] (better to quantify if needed)

Oscillators

Alligator Oscillator

data = [8,7,8,9,7,8,9,6,7,8,6,8,10,8,7,9,8,7,9,6,7,9];
# defaults shown
jawlength = 13;
teethlength = 8;
liplength = 5;
jawshift = 8;
teethshift = 5;
lipshift = 3;
ta.gator(data, jawlength, teethlength, liplength, jawshift, teethshift, lipshift);
# output (array)
# [upper gator, lower gator]

Chande Momentum Oscillator

data = [1, 1.2, 1.3, 1.3, 1.2, 1.4];
length = 4; # default = 9
ta.mom_osc(data, length);
# output (array)
# [0.0, 3.85]

Chaikin Oscillator

data = [[2,3,4,6],[5,5,5,4],[5,4,3,7],[4,3,3,4],[6,5,4,6],[7,4,3,6]]; # [high, close, low, volume]
length1 = 2; # default = 3
length2 = 4; # default = 10
ta.chaikin_osc(data, length1, length2);
# output (array)
# [-1.667, -0.289, -0.736]

Aroon Oscillator

data = [2, 5, 4, 5];
length = 3; # default = 25
ta.aroon_osc(data, length);
# output (array)
# [50.0, 50.0]

Awesome Oscillator

data = [[6, 5], [8, 6], [7, 4], [6, 5], [7, 6], [9, 8]]; # [high, low]
shortlength = 2; # default = 5
longlength = 5; # default = 35
ta.ao(data, shortlength, longlength);
# output (array)
# [0, 0.9]

Accelerator Oscillator

data = [[6, 5], [8, 6], [7, 4], [6, 5], [7, 6], [9, 8]]; # [high, low]
shortlength = 2; # default = 5
longlength = 4; # default = 35
ta.ac(data, shortlength, longlength);
# output (array)
# [-5.875, -6.125, -6.5]

Fisher Transform

data = [8,6,8,9,7,8,9,8,7,8,6,7]; # high + low / 2
length = 9;
ta.fisher(data, length);
# output (array)
# [[-0.318, -0.11], [-0.449, -0.318], [-0.616, -0.449]] # [fisher, trigger]

Bands

Bollinger Bands

data = [1, 2, 3, 4, 5, 6];
length = 5; # default = 14
deviations = 2; # default = 1
ta.bands(data, length, deviations);
# output (array)
# [[5.828, 3, 0.172], [6.828, 4, 1.172]]
# [upper band, middle band, lower band]

Keltner Channels

data = [[3,2,1], [2,2,1], [4,3,1], [2,2,1], [3,3,1]]; # [high, close, low]
length = 5; # default = 14
deviations = 1; # default = 1
ta.keltner(data, length, deviations);
# output (array)
# [[3.93, 2.06, 0.20]]
# [upper band, middle band, lower band]

Donchian Channels

data = [[6, 2], [5, 2], [5, 3], [6, 3], [7, 4], [6, 3]]; # [high, low]
length = 5; # default = 20
ta.don(data, length);
# output (array)
# [[7, 4.5, 2], [7, 4.5, 2]]
# [upper band, base line, lower band]

Fibonacci Bollinger Bands

data = [[1,59],[1.1,82],[1.21,27],[1.42,73],[1.32,42]];
length = 4;
deviations = 3;
ta.fibbands(data, length, deviations);
# output (array)
# [[highest band -> fibonacci levels -> lowest band]]

Envelope

data = [6,7,8,7,6,7,8,7,8,7,8,7,8];
length = 11, # default = 10
percentage = 0.05; # default = 0.005
ta.envelope(data, length, percentage);
# output (array)
# [[7.541, 7.182, 6.823], [7.636, 7.273, 6.909]]
# [upper band, base line, lower band]

Statistics

Standard Deviation

data = [1, 2, 3];
length = 3; # default = len(length)
ta.std(data, length);
# output (float)
# 0.81649658092773

Variance

data = [6, 7, 2, 3, 5, 8, 6, 2];
length = 7; # default = len(data)
ta.variance(data, length);
# output (array)
# [3.918, 5.061]

Normal CDF

sample = 13;
mean = 10;
stdv = 2;
ta.ncdf(sample, mean, stdv);
# output (float)
# 0.9331737996110652

zscore = 1.5;
ta.ncdf(zscore);
# output (float)
# 0.9331737996110652

Inverse Normal Distribution

data = 0.4732;
ta.normsinv(data);
# output (float)
# -0.06722824471054376

Monte Carlo Simulation

data = [6, 4, 7, 8, 5, 6];
length = 2; # default = 50
simulations = 100; # default = 1000
percentile = 0.5; # default = -1 (returns all raw simulations)
ta.sim(data, length, simulations, percentile)
# output (array)
# [6, 4, 7, 8, 5, 6, 5.96, 5.7]

Percentile

data = [[6,4,7], [5,3,6], [7,5,8]];
percentile = 0.5;
ta.percentile(data, percentile);
# output (array)
# [6, 4, 7]

Correlation

data1 = [1, 2, 3, 4, 5, 2];
data2 = [1, 3, 2, 4, 6, 3];
ta.cor(data1, data2);
# output (float)
# 0.8808929232684737

Covariance

data1 = [12,13,25,39];
data2 = [67,45,32,21];
length = 4;
ta.covariance(data1, data2, 4);
# output (array)
# [-165.8125]

Percentage Difference

newval = 0.75;
oldval = 0.5;
ta.dif(newval, oldval);
# output (float)
# 0.5

Expected Return

data = [0.02, -0.01, 0.03, 0.05, -0.03]; # historical return data
ta.er(data);
# output (float)
# 0.0119

Abnormal Return

data = [0.02, -0.01, 0.03, 0.05, -0.03]; # historical return data
length = 3;
ta.ar(data, length);
# output (array)
# [0.037, -0.053]

Kelly Criterion

data = [0.01, 0.02, -0.01, -0.03, -0.015, 0.045, 0.005];
ta.kelly(data);
# output (float)
# 0.1443

Permutations

data = [10,10,10];
ta.permutations(data);
# output (int)
# 1000

Winratio

var data = [0.01, 0.02, -0.01, -0.03, -0.015, 0.005];
ta.winratio(data);
# output (float)
# 0.5

Average Win

data = [0.01, 0.02, -0.01, -0.03, -0.015, 0.005];
ta.avgwin(data);
# output (float)
# 0.012

Average Loss

data = [0.01, 0.02, -0.01, -0.03, -0.015, 0.005];
ta.avgloss(data);
# output (float)
# -0.018

Drawdown

data = [1, 2, 3, 4, 2, 3];
ta.drawdown([1,2,3,4,2,3]);
# output (float)
# -0.5

Median

data = [4, 6, 3, 1, 2, 5];
length = 4; # default = len(data)
ta.median(data, length);
# output (array)
# [3, 2, 2]

Recent High

data = [4,5,6,7,8,9,8,7,8,9,10,3,2,1];
lookback = 3; # No higher values after 3 periods? resets after each new high
ta.recent_high(data, lookback);
# output (dictionary)
# {'index': 10, 'value': 10}

Recent Low

data = [1,4,5,6,4,3,2,3,4,3,5,7,8,8,5];
lookback = 4; # No lower values after 4 periods? resets after each new low
ta.recent_low(data, lookback);
# output (dictionary)
# {'index': 6, 'value': 2}

Median Absolute Deviation

data = [3, 7, 5, 4, 3, 8, 9];
length = 6; # default = len(data)
ta.mad(data, length);
# output (array)
# [1, 2]

Average Absolute Deviation

data = [4, 6, 8, 6, 8, 9, 10, 11];
length = 7; # default = len(data)
ta.aad(data, length);
# output (array)
# [1.673, 1.469]

Standard Error

data = [34, 54, 45, 43, 57, 38, 49];
size = 10; # default = len(data)
ta.se(data, size);
# output (float)
# 2.424

Sum Squared Differences

data = [7, 6, 5, 7, 9, 8, 3, 5, 4];
length = 7; # default = len(length)
ta.ssd(data, length);
# output (array)
# [4.87, 4.986, 5.372]

Logarithm

data = [5, 14, 18, 28, 68, 103];
ta.log(data);
# output (array)
# [1.61, 2.64, 2.89, 3.33, 4.22, 4.63]

Exponent

data = [1.6, 2.63, 2.89, 3.33, 4.22, 4.63];
ta.exp(data);
# output (array)
# [4.95, 13.87, 17.99, 27.94, 68.03, 102.51]

Normalize

data = [5,4,9,4];
margin = 0.1; # margin % (default = 0)
ta.normalize(data, margin);
# output (array)
# [0.22, 0.06, 0.86, 0.06]

Denormalize

data = [5,4,9,4]; # original data || [highest, lowest]
norm = [0.22, 0.06, 0.86, 0.06, 0.44]; # normalized data
margin = 0.1; # margin % (default = 0)
ta.denormalize(data, norm, margin);
# output (array)
# [5 ,4, 9, 4, 6.4]

Normalize Pair

pair1 = [10,12,11,13];
pair2 = [100,130,100,140];
ta.normalize_pair(pair1, pair2);
# output (array)
# [[55, 55], [66, 71.5], [60.5, 54.99], [71.5, 76.99]]

Normalize From

data = [8, 12, 10, 11];
baseline = 100;
ta.normalize_from(data, baseline);
# output (array)
# [100, 150, 125, 137.5]

Standardize

data = [6,4,6,8,6];
ta.standardize(data);
# output (array)
# [0, -1.581, 0, 1.581, 0]

Z-Score

data = [34,54,45,43,57,38,49];
length = 5;
ta.zscore(data, length);
# output (array)
# [1.266, -1.331, 0.408]

K-means Clustering

data = [2, 3, 4, 5, 3, 5, 7, 8, 6, 8, 6, 4, 2, 6];
length = 4;
ta.kmeans(data, length);
# output (array)
# [[ 4, 5, 5, 4 ], [ 7, 6, 6, 6 ], [ 8, 8 ], [ 2, 3, 3, 2 ]]

Mean Squared Error

data1 = [7,8,7,8,6,9];
data2 = [6,8,8,9,6,8];
ta.mse(data1, data2);
# output (float)
# 0.6666666666666666

Cumulative

data = [3,5,7,5,10];
length = 4;
ta.cum(data, length);
# output (array)
# [20, 27]

Chart types

Heikin Ashi

data = [[3, 4, 2, 3], [3, 6, 3, 5], [5, 5, 2, 3]]; # [open, high, low, close]
ta.ha(data);
# output (array)
# [open, high, low, close]
# first 7-10 candles are unreliable

Renko

data = [[8, 6], [9, 7], [9, 8]]; # [high, low]
bricksize = 3;
ta.ren(data, bricksize);
# output (array)
# [open, high, low, close]

Miscellaneous

Times Up

data = [5,6,7,8,7,6,5];
length = 3;
ta.times_up(data, length);
# output (array)
# [1, 0, 0, 0]

Times Down

data = [5,6,7,8,7,6,5];
length = 3;
ta.times_down(data, length);
# output (array)
# [0, 0, 0, 1]

Experimental Functions

Support Line

data = [4,3,2,5,7,6,5,4,7,8,5,4,6,7,5];
start = {"index": 2, "value": 2}; # default = recent_low(data, 25)
support = ta.support(data, start);
# output (dictionary)
# ['calculate'] = function(x) // calculates line at position x from start['index'] (= 0)
# ['slope'] = delta y per x
# ['lowest'] = lowest (start) value at x = 0
# ['index'] = (start) index of lowest value
# to get the line at the current candle / chart period
current = support['calculate'](len(data)-support['index']);

Resistance Line

data = [5,7,5,5,4,6,5,4,6,5,4,3,2,4,3,2,1];
start = {"index": 1, "value": 7}; # default = recent_high(data, 25)
resistance = ta.resistance(data, start);
# output (dictionary)
# ['calculate'] = function(x) // calculates line at position x from start['index'] (= 0)
# ['slope'] = delta y per x
# ['highest'] = highest (start) value
# ['index'] = (start) index of highest value
# to get the line at the current candle / chart period
current = resistance['calculate'](len(data)-resistance['index']);

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

Please make sure to update tests as appropriate.

License

MIT

ta.py's People

Contributors

Stargazers

Watchers

ta.py's Issues

pandas

Great Job on there indictor, if you could write like pandas extenstion like pandas-ta or even directly applicable to pandas i would be easier and faster adoptable as most data is handled in pandas now a days

new indicators

Hi, thx for this lightweight repo)
need more indicators, such as:

Chaikin Oscillator
Envelopes
Fractals

add:
Accelerator Oscillator (AC)

thx!)

ta.fibbands

Hi
Can't understand how to plot corectly with price

input:
highlow_data = [[19963.24, 20009.0], [19975.0, 20018.5], [19995.62, 20046.32], [19982.15, 20029.52], [19983.7, 20001.22]]
close_data = [19990.16, 19997.24, 20028.58, 19988.03, 20000.71]

length = 4
deviations = 3
x=ta.fibbands(highlow_data , length, deviations)
plt.plot(x)
plt.plot(close_data)

plot result bad. thx

Heikin ashi and supertrend functionality returning duplicate values | 27-Aug-2022

Supertrend is returning duplicate values while processing a stock's OHLC (open, high, low, close) data.
Have attached unprocessed OHLC data df.csv file below.
First: I am processing this OHLC csv data frame to Heikin Ashi candle data using ta.ha from pandas_ta
Then: I am processing this Heikin Ashi processed data with ta.supertrend from pandas_ta.

In both pandas_ta methods, I am getting duplicate values.
Though SuperTrend signal is provided accurately. Values are duplicate.

Unprocessed CSV FILE - df.csv

bug :)

HI)

[ [11.835999999999999, 11.867, 11.747, 11.843], [11.85, 11.855, 11.815999999999999, 11.819], [11.819, 11.827, 11.8, 11.811], [11.81, 11.818, 11.780999999999999, 11.818], [11.817, 11.818, 11.751, 11.806], [11.805, 11.824000000000002, 11.770999999999999, 11.81], [11.814, 11.821, 11.776, 11.786], [11.783, 11.855, 11.78, 11.845], [11.845, 11.853, 11.793, 11.828], [11.834000000000001, 11.834000000000001, 11.792, 11.824000000000002], [11.827, 11.843, 11.754000000000001, 11.787], [11.780999999999999, 11.794, 11.759, 11.774000000000001], [11.767999999999999, 11.841, 11.741, 11.805], [11.807, 11.835, 11.751, 11.767000000000001], [11.765999999999998, 11.774000000000001, 11.673, 11.699000000000002], [11.702, 11.776, 11.694, 11.770999999999999], [11.767999999999999, 11.794, 11.707, 11.715], [11.713, 11.745, 11.71, 11.739], [11.735, 11.790999999999999, 11.735, 11.751], [11.751, 11.844000000000001, 11.751, 11.823], [11.818, 11.873, 11.806, 11.87], [11.87, 11.889000000000001, 11.83, 11.883], [11.888, 11.937999999999999, 11.87, 11.918], [11.902999999999999, 11.974, 11.88, 11.964], [11.964, 12.042, 11.964, 11.977], [11.977, 11.989, 11.917, 11.939], [11.945, 11.989, 11.927, 11.937000000000001], [11.939, 11.949000000000002, 11.865, 11.87], [11.865, 11.898, 11.84, 11.885], [11.889000000000001, 11.892999999999999, 11.850999999999999, 11.859000000000002], [11.857999999999999, 11.894, 11.852, 11.888], [11.887, 11.953, 11.869000000000002, 11.948], [11.948, 11.952, 11.870999999999999, 11.892000000000001], [11.887, 11.923, 11.882, 11.886], [11.880999999999998, 11.883, 11.839, 11.841], [11.843, 11.914000000000001, 11.821, 11.913], [11.915, 11.915, 11.88, 11.905999999999999], [11.905, 11.923, 11.882, 11.923], [11.923, 11.972000000000001, 11.905999999999999, 11.972000000000001], [11.975, 11.999, 11.764000000000001, 11.799000000000001], [11.788, 11.822000000000001, 11.748, 11.8], [11.798, 11.866, 11.700999999999999, 11.764000000000001], [11.765999999999998, 11.856, 11.72, 11.837], [11.834000000000001, 11.852, 11.808, 11.817], [11.806, 11.819, 11.668, 11.702], [11.698, 11.747, 11.66, 11.73], [11.717, 11.824000000000002, 11.717, 11.777000000000001], [11.777000000000001, 11.819, 11.752, 11.8], [11.804, 11.815999999999999, 11.71, 11.71], [11.713, 11.812000000000001, 11.707, 11.772], [11.774000000000001, 11.795, 11.716, 11.734000000000002], [11.719000000000001, 11.719000000000001, 11.652999999999999, 11.664000000000001], [11.662, 11.675, 11.571, 11.585], [11.582, 11.595999999999998, 11.545, 11.587], [11.588, 11.619000000000002, 11.52, 11.595], [11.592, 11.612, 11.52, 11.533], [11.524000000000001, 11.632, 11.515, 11.617], [11.622, 11.647, 11.61, 11.644], [11.647, 11.672, 11.629000000000001, 11.652999999999999], [11.655, 11.673, 11.636, 11.65], [11.65, 11.724, 11.618, 11.713], [11.718, 11.748, 11.683, 11.734000000000002], [11.722999999999999, 11.738, 11.686, 11.727], [11.720999999999998, 11.743, 11.687000000000001, 11.687000000000001], [11.686, 11.788, 11.685, 11.776], [11.772, 11.855, 11.772, 11.821], [11.825, 11.9, 11.822000000000001, 11.890999999999998], [11.890999999999998, 11.984000000000002, 11.889000000000001, 11.950999999999999], [11.947000000000001, 11.968, 11.9, 11.948], [11.950999999999999, 12.079, 11.931, 12.06], [12.065999999999999, 12.255, 12.065999999999999, 12.220999999999998], [12.23, 12.25, 12.142999999999999, 12.209000000000001], [12.214, 12.277999999999999, 12.174000000000001, 12.22], [12.22, 12.232999999999999, 12.182, 12.186], [12.186, 12.251, 12.175999999999998, 12.225], [12.22, 12.309000000000001, 12.205, 12.254000000000001], [12.258, 12.258, 12.21, 12.222000000000001], [12.222999999999999, 12.279000000000002, 12.213, 12.277999999999999], [12.274000000000001, 12.345, 12.269, 12.322000000000001], [12.318, 12.343, 12.3, 12.311], [12.312000000000001, 12.355, 12.264000000000001, 12.270999999999999], [12.284, 12.287, 12.204, 12.217], [12.219000000000001, 12.338, 12.217, 12.293], [12.290999999999999, 12.295, 12.239, 12.265], [12.27, 12.283, 12.164000000000001, 12.202], [12.207, 12.217, 12.123, 12.152000000000001], [12.152999999999999, 12.184000000000001, 12.117, 12.177999999999999], [12.184000000000001, 12.2, 12.142999999999999, 12.172], [12.174000000000001, 12.314, 12.147, 12.23], [12.23, 12.288, 12.215, 12.249], [12.243, 12.329, 12.23, 12.314], [12.312999999999999, 12.327, 12.265999999999998, 12.284], [12.288, 12.296, 12.243, 12.295], [12.297, 12.367, 12.209000000000001, 12.235999999999999], [12.232999999999999, 12.315, 12.228, 12.29], [12.29, 12.349, 12.265999999999998, 12.283], [12.287, 12.287, 12.175999999999998, 12.175999999999998], [12.179, 12.254000000000001, 12.147, 12.253], [12.252, 12.27, 12.171, 12.197000000000001], [12.193, 12.214, 12.055, 12.069], [12.078, 12.134, 12.023, 12.049000000000001], [12.039000000000001, 12.174000000000001, 12.038, 12.130999999999998], [12.128, 12.17, 12.115, 12.158], [12.158, 12.161, 12.071, 12.087], [12.085, 12.158, 12.064, 12.151], [12.151, 12.17, 12.110999999999999, 12.142999999999999], [12.148, 12.165, 12.12, 12.155], [12.165, 12.294, 12.161, 12.265999999999998], [12.265999999999998, 12.305, 12.193, 12.197000000000001], [12.196, 12.231, 12.177, 12.217], [12.22, 12.293, 12.208, 12.287], [12.284, 12.327, 12.257, 12.307], [12.319, 12.344000000000001, 12.209000000000001, 12.265999999999998], [12.265999999999998, 12.3, 12.238, 12.288], [12.288, 12.318, 12.265, 12.31], [12.315999999999999, 12.327, 12.2, 12.213], [12.213, 12.225999999999999, 12.171, 12.190999999999999], [12.187000000000001, 12.35, 12.187000000000001, 12.35], [12.35, 12.35, 12.282, 12.292], [12.292, 12.44, 12.288, 12.385], [12.39, 12.449000000000002, 12.342, 12.383], [12.385, 12.452, 12.354000000000001, 12.424000000000001], [12.42, 12.42, 12.277999999999999, 12.315], [12.31, 12.34, 12.282, 12.317], [12.322000000000001, 12.324000000000002, 12.19, 12.229000000000001], [12.232000000000001, 12.25, 12.204, 12.216], [12.217, 12.227, 12.187999999999999, 12.218], [12.222000000000001, 12.225, 12.198, 12.198], [12.198, 12.202, 12.173, 12.179], [12.181, 12.204, 12.142000000000001, 12.19], [12.19, 12.261, 12.186, 12.232000000000001], [12.232000000000001, 12.239, 12.175, 12.190999999999999], [12.190999999999999, 12.214, 12.165999999999999, 12.206], [12.212, 12.287, 12.212, 12.286], [12.286, 12.360999999999999, 12.272, 12.329], [12.335999999999999, 12.385, 12.32, 12.355], [12.362, 12.388, 12.331, 12.388], [12.383, 12.394, 12.305, 12.343], [12.334000000000001, 12.34, 12.25, 12.28], [12.279000000000002, 12.279000000000002, 12.123, 12.175999999999998], [12.169, 12.169, 12.075, 12.135], [12.13, 12.222999999999999, 12.125, 12.213], [12.218, 12.245, 12.2, 12.241], [12.24, 12.334000000000001, 12.22, 12.307], [12.312000000000001, 12.312000000000001, 12.119000000000002, 12.172], [12.19, 12.224, 12.154000000000002, 12.18], [12.162, 12.252, 12.162, 12.208], [12.204, 12.204, 12.112, 12.122], [12.110999999999999, 12.192, 12.1, 12.192], [12.190999999999999, 12.2, 11.96, 12.012], [12.017999999999999, 12.052, 11.927, 11.972999999999999], [11.975, 12.039000000000001, 11.965, 11.969000000000001], [11.984000000000002, 12.071, 11.956, 12.027999999999999], [12.029000000000002, 12.120999999999999, 12.0, 12.103], [12.097999999999999, 12.127, 12.052999999999999, 12.08], [12.08, 12.123, 12.003, 12.123], [12.114, 12.285, 12.057, 12.257], [12.265, 12.448, 12.25, 12.421], [12.413, 12.6, 12.383, 12.6], [12.6, 12.685, 12.502, 12.667], [12.655, 12.77, 12.515999999999998, 12.617], [12.61, 12.623, 12.515999999999998, 12.620999999999999], [12.622, 12.829, 12.62, 12.829], [12.825999999999999, 12.825999999999999, 12.665, 12.681], [12.674000000000001, 12.905, 12.659, 12.744000000000002], [12.747, 12.819, 12.599, 12.618], [12.614, 12.662, 12.6, 12.62], [12.625, 12.722000000000001, 12.488, 12.71], [12.714, 12.825, 12.58, 12.815999999999999], [12.815999999999999, 12.84, 12.722000000000001, 12.722999999999999], [12.722999999999999, 12.844000000000001, 12.684000000000001, 12.844000000000001], [12.844000000000001, 12.882, 12.77, 12.825999999999999], [12.82, 12.821, 12.661, 12.677999999999999], [12.685, 12.74, 12.587, 12.632], [12.639000000000001, 12.712, 12.635, 12.651], [12.647, 12.754000000000001, 12.62, 12.737], [12.738, 12.832, 12.604000000000001, 12.81], [12.806, 12.95, 12.69, 12.729000000000001], [12.729000000000001, 12.800999999999998, 12.7, 12.752], [12.751, 12.839, 12.747, 12.806], [12.805, 12.994000000000002, 12.787, 12.957], [12.954, 12.964, 12.868, 12.905999999999999], [12.909, 12.943, 12.817, 12.864], [12.870999999999999, 12.907, 12.831, 12.869000000000002], [12.868, 12.969000000000001, 12.84, 12.922], [12.927, 13.275, 12.865, 13.273], [13.262, 13.388, 13.162, 13.298], [13.296, 13.329, 13.182, 13.24], [13.231, 13.527000000000001, 13.203, 13.352], [13.35, 13.419, 12.882, 13.036], [13.040999999999999, 13.040999999999999, 12.725999999999999, 12.821], [12.822000000000001, 13.046, 12.499, 12.811], [12.805, 12.975999999999999, 12.729000000000001, 12.782], [12.799000000000001, 12.874, 12.695, 12.715], [12.715, 12.81, 12.681, 12.764000000000001], [12.774000000000001, 12.782, 12.615, 12.629000000000001], [12.629000000000001, 12.82, 12.620999999999999, 12.777000000000001], [12.785, 12.895, 12.755, 12.885], [12.892999999999999, 12.963, 12.833, 12.952], [12.950999999999999, 13.068, 12.948, 13.030999999999999], [13.033, 13.043, 12.929, 13.032], [13.032, 13.154000000000002, 13.0, 13.011], [13.0, 13.07, 12.944, 12.972000000000001], [12.97, 13.007, 12.943, 12.985999999999999], [12.995, 13.0, 12.888, 12.982999999999999], [12.98, 12.985999999999999, 12.919, 12.94], [12.932, 12.955, 12.782, 12.879000000000001], [12.868, 12.931, 12.644, 12.652999999999999], [12.655, 12.787, 12.644, 12.78], [12.78, 12.8, 12.642999999999999, 12.66], [12.662, 12.709000000000001, 12.511, 12.515], [12.526, 12.67, 12.513, 12.645], [12.63, 12.73, 12.595999999999998, 12.633], [12.633, 12.638, 12.421, 12.431], [12.423, 12.482999999999999, 12.231, 12.298], [12.298, 12.593, 12.255, 12.571], [12.573, 12.690999999999999, 12.565, 12.668], [12.662, 12.780999999999999, 12.640999999999998, 12.758], [12.745999999999999, 12.765999999999998, 12.654000000000002, 12.700999999999999], [12.692, 12.774000000000001, 12.655999999999999, 12.710999999999999], [12.710999999999999, 12.912, 12.71, 12.82], [12.824000000000002, 12.888, 12.77, 12.838], [12.829, 13.2, 12.828, 13.092], [13.097000000000001, 13.357000000000001, 12.993, 13.244000000000002], [13.242, 13.52, 13.232000000000001, 13.51], [13.515999999999998, 13.87, 13.51, 13.748], [13.753, 13.859000000000002, 13.550999999999998, 13.817], [13.812999999999999, 13.894, 13.569, 13.604000000000001], [13.591, 13.725, 13.591, 13.66], [13.661, 13.966, 13.661, 13.859000000000002], [13.857999999999999, 13.883, 13.634, 13.655], [13.645, 13.727, 13.585999999999999, 13.707], [13.714, 13.787, 13.597000000000001, 13.767999999999999], [13.772, 13.845, 13.696, 13.754000000000001], [13.758, 13.78, 13.642999999999999, 13.69], [13.699000000000002, 13.699000000000002, 13.433, 13.522], [13.522, 13.6, 13.489, 13.498], [13.491, 13.645, 13.485999999999999, 13.569], [13.565, 13.707, 13.561, 13.655], [13.647, 13.770999999999999, 13.497, 13.530999999999999], [13.540999999999999, 14.0, 13.535, 13.97], [13.969000000000001, 14.049000000000001, 13.855, 13.877], [13.874, 13.905, 13.728, 13.751], [13.751, 13.785, 13.696, 13.724], [13.71, 13.78, 13.600999999999999, 13.606], [13.602, 13.718, 13.56, 13.63], [13.63, 13.659, 13.51, 13.540999999999999], [13.540999999999999, 13.6, 13.482000000000001, 13.526], [13.513, 13.569, 13.503, 13.533], [13.524000000000001, 13.628, 13.512, 13.533], [13.532, 13.582, 13.478, 13.489], [13.49, 13.523, 13.312999999999999, 13.395], [13.395999999999999, 13.440999999999999, 13.345, 13.417], [13.414000000000001, 13.458, 13.374, 13.392999999999999], [13.389000000000001, 13.397, 13.312999999999999, 13.315], [13.317, 13.359000000000002, 13.265, 13.288], [13.289000000000001, 13.57, 13.277999999999999, 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11.408, 11.494000000000002], [11.497, 11.55, 11.44, 11.492], [11.493, 11.599, 11.493, 11.589], [11.591, 11.599, 11.45, 11.548], [11.527999999999999, 11.575, 11.454, 11.489], [11.487, 11.487, 11.383, 11.42], [11.423, 11.457, 11.344000000000001, 11.349], [11.355, 11.433, 11.331, 11.414000000000001], [11.41, 11.41, 11.311, 11.347999999999999], [11.347999999999999, 11.425999999999998, 11.333, 11.387], [11.365, 11.454, 11.36, 11.446], [11.433, 11.55, 11.427, 11.527999999999999], [11.536, 11.655999999999999, 11.503, 11.623], [11.620999999999999, 11.7, 11.59, 11.65], [11.654000000000002, 11.659, 11.559000000000001, 11.565], [11.561, 11.585, 11.517000000000001, 11.546], [11.542, 11.545, 11.415999999999999, 11.417], [11.415, 11.443, 11.35, 11.4], [11.404000000000002, 11.440999999999999, 11.386, 11.392999999999999], [11.395999999999999, 11.470999999999998, 11.344000000000001, 11.36], [11.357999999999999, 11.43, 11.324000000000002, 11.423], [11.423, 11.493, 11.405999999999999, 11.493], [11.488, 11.52, 11.468, 11.504000000000001], [11.504000000000001, 11.515999999999998, 11.445, 11.452], [11.444, 11.477, 11.427999999999999, 11.45], [11.440999999999999, 11.594000000000001, 11.436, 11.572000000000001], [11.57, 11.605, 11.538, 11.593], [11.59, 11.609000000000002, 11.556, 11.56], [11.559000000000001, 11.571, 11.513, 11.523], [11.514000000000001, 11.55, 11.477, 11.505], [11.505999999999998, 11.613, 11.48, 11.495], [11.493, 11.549000000000001, 11.474, 11.513], [11.512, 11.54, 11.505, 11.54], [11.537, 11.55, 11.49, 11.503], [11.509, 11.509, 11.459000000000001, 11.487], [11.488, 11.52, 11.45, 11.482999999999999], [11.478, 11.512, 11.463, 11.505], [11.517999999999999, 11.56, 11.485999999999999, 11.558], [11.56, 11.599, 11.534, 11.540999999999999], [11.547, 11.61, 11.530999999999999, 11.594000000000001], [11.593, 11.609000000000002, 11.577, 11.595], [11.599, 11.63, 11.575999999999999, 11.63], [11.63, 11.683, 11.588, 11.675999999999998], [11.683, 11.7, 11.605, 11.624], [11.618, 11.675, 11.618, 11.67]]
Traceback (most recent call last):
  File "/indicators.py", line 37, in AddIndicators
    df['bop'] = ta.bop(data, length)
  File "/usr/local/lib/python3.7/dist-packages/ta_py/ta.py", line 448, in bop
    bo.append((data[i][3] - data[i][0]) / (data[i][1] - data[i][2]));
ZeroDivisionError: float division by zero

code:

    data = []
    for index, row in df.iterrows():
        data.append([row["Open"], row["High"], row["Low"], row["Close"]])
    print(data)    
    length = 14; # default = 14
    df['bop'] = ta.bop(data, length)

Half trend

The half trend code is not working as it should very different from TV

import ta_py as tapy
import pandas as pd
import pandas_ta as ta

df = pd.read_csv('/mnt/f/python/stocks/neo/data/bn_5min.csv')
# %%
df = df[:5000]
df.date = pd.to_datetime(df.date)
df.ta.ha(append=True)
data = df[['HA_high', 'HA_close', 'HA_low']].values.tolist()
[df2.csv](https://github.com/Bitvested/ta.py/files/9432276/df2.csv)

datahs = tapy.halftrend(data, 100, 2, 2)
df1 = pd.DataFrame(datahs, columns=['atrHigh', 'HalfTrend', 'atrLow', 'HS'])
df1.index=(df1.index+(len(df)-len(df1)))
df2 = pd.merge(df, df1, left_index=True, right_index=True)

Attaching the ouptu file

please check on tv by symbol BANKNIFTY 5 min HS(2,2)

The values and signals are very different

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bitvested / ta.py Goto Github PK

ta.py's Introduction

Technical Analysis (ta.py)

Installation

pip

Usage

Examples

Moving Averages

Indicators

Oscillators

Bands

Statistics

Chart Types

Miscellaneous

Experimental

Moving Averages

Simple Moving Average (SMA)

Smoothed Moving Average (SMMA)

Weighted Moving Average (WMA)

Exponential Moving Average (EMA)

Hull Moving Average

Least Squares Moving Average (LSMA)

Volume Weighted Moving Average (VWMA)

Volume Weighted Weighted Moving Average (VWWMA)

Wilder's Smoothing Moving Average

Parabolic Weighted Moving Average

Hyperbolic Weighted Moving Average

Kaufman Adaptive Moving Average (KAMA)

Custom Weighted Moving Average

Indicators

Moving Average Convergence / Divergence (MACD)

Relative Strength Index (RSI)

Wilder's Relative Strength Index

True Strength Index (TSI)

Balance Of Power

Force Index

Accumulative Swing Index

Alligator Indicator

Williams %R

Stochastics

Fibonacci Retracement

Bollinger Bandwidth

Ichimoku Cloud

Average True Range (ATR)

Aroon Up

Aroon Down

Money Flow Index

Rate Of Change

Coppock Curve

Know Sure Thing

On-Balance Volume

Volume-Weighted Average Price

Fractals

Crossover (golden cross)

Momentum

HalfTrend

ZigZag

Parabolic SAR

SuperTrend

Elder Ray Index

Historical Volatility

Relative Vigor Index

Relative Vigor Index Signal

RSI Divergence

Universal Divergence

Oscillators

Alligator Oscillator

Chande Momentum Oscillator

Chaikin Oscillator

Aroon Oscillator

Awesome Oscillator

Accelerator Oscillator

Fisher Transform

Bands

Bollinger Bands

Keltner Channels

Donchian Channels

Fibonacci Bollinger Bands

Envelope

Statistics