GithubHelp home page GithubHelp logo

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

Indicators

Oscillators

Bands

Statistics

Chart Types

Miscellaneous

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]

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

vultwo avatar

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. 📊📈🎉

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google ❤️ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.