Skip to main content

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

Project description

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

Indicators

Oscillators

Bands

Statistics

Chart Types

Experimental

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]

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]

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]

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)
# [[-0.207, 0.11], [-0.393, -0.207]] # [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]

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]

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

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

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]

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 ]]

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]

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

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distribution

ta_py-1.2.0-py3-none-any.whl (15.4 kB view hashes)

Uploaded Python 3

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page