TAcharts 0.0.30

By: Carter Carlson

Contributors: @juanfrcaliz, @rnarciso, @t3ch9

This repository provides technical tools to analyze OHLCV data, along with several TA chart functionalities. These functions are optimized for speed and utilize numpy vectorization over built-in pandas methods when possible.

Methods

Indicators With Chart Functionality

• Bollinger(df=None, filename=None, interval=None, n=20, ndev=2): Bollinger Bands
• Ichimoku(df=None, filename=None, interval=None): Ichimoku Cloud
• Renko(df=None, filename=None, interval=None): Renko Chart

Indicators Without Chart Functionality

• atr(high, low, close, n=2): average true range from candlestick data
• cmf(df, n=2): Chaikin Money Flow of an OHLCV dataset
• double_smooth(src, n_slow, n_fast): The smoothed value of two EMAs
• ema(src, n=2): exponential moving average for a list of src across n periods
• macd(src, slow=25, fast=13): moving average convergence/divergence of src
• mmo(src, n=2): Murrey Math oscillator of src
• roc(src, n=2): rate of change of src across n periods
• rolling(src, n=2, fn=None, axis=1): rolling sum, max, min, mean, or median of src across n periods
• rsi(src, n=2): relative strength index of src across n periods
• sdev(src, n=2): standard deviation across n periods
• sma(src, n=2): simple moving average of src across n periods
• td_sequential(src, n=2): TD sequential of src across n periods
• tsi(src, slow=25, fast=13): true strength indicator

utils

• area_between(line1, line2): find the area between line1 and line2
• crossover(x1, x2): find all instances of intersections between two lines
• draw_candlesticks(ax, df): add candlestick visuals to a matplotlib chart
• fill_values(averages, interval, target_len): Fill missing values with evenly spaced samples.
  • Example: You're using 15-min candlestick data to find the 1-hour moving average and want a value at every 15-min mark, and not every 1-hour mark.
• group_candles(df, interval=4): combine candles so instead of needing a different dataset for each time interval, you can form time intervals using more precise data.
  • Example: you have 15-min candlestick data but want to test a strategy based on 1-hour candlestick data (interval=4).
• intersection(a0, a1, b0, b1): find the intersection coordinates between vector A and vector B

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How it works

Create your DataFrame

# NOTE: we are using 1-hour BTC OHLCV data from 2019.01.01 00:00:00 to 2019.12.31 23:00:00
from TAcharts.utils.ohlcv import OHLCV

df = OHLCV().btc

df.head()

  | date | open | high | low | close | volume --------|-------|------|-------|------|-------|--------
0 | 2019-01-01 00:00:00 | 3699.95 | 3713.93 | 3697.00 | 3703.56 | 660.279771 1 | 2019-01-01 01:00:00 | 3703.63 | 3726.64 | 3703.34 | 3713.83 | 823.625491 2 | 2019-01-01 02:00:00 | 3714.19 | 3731.19 | 3707.00 | 3716.70 | 887.101362 3 | 2019-01-01 03:00:00 | 3716.98 | 3732.00 | 3696.14 | 3699.95 | 955.879034 4 | 2019-01-01 04:00:00 | 3699.96 | 3717.11 | 3698.00 | 3713.07 | 534.113945

Bollinger Bands

from TAcharts.indicators.bollinger import Bollinger

b = Bollinger(df)
b.build(n=20, ndev=2)

b.plot()

Ichimoku

from TAcharts.indicators.ichimoku import Ichimoku

i = Ichimoku(df)
i.build(20, 60, 120, 30)

i.plot()

Renko

from TAcharts.indicators.renko import Renko

r = Renko(df)
r.set_brick_size(auto=True, atr_interval=2)
r.build()

r.plot()

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wrappers

• @args_to_dtype(dtype): Convert all function arguments to a specific data type

  from TAcharts.wrappers import args_to_dtype
  
  # Example: `src` is converted to a list
  @args_to_dtype(list)
  def rsi(src, n=2):
      pass
  

• @pd_series_to_np_array: Convert function arguments from pd.Series to np.array using pd.Series.values. This wrapper is 10x quicker than using @args_to_dtype(np.array) when working with Pandas series.

  from TAcharts.wrappers import pd_series_to_np_array
  
  # Example: `high`, `low`, and `close` are all converted into `np.array` data types
  @pd_series_to_np_array
  def atr(high, low, close, n=14):
      pass