TAcharts 0.0.1

TA Charting tool

TA-charts

By: Carter Carlson

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.

Basic tools (ta.py):

• rolling(src, n=2, fn=None, axis=1): rolling sum, max, min, or mean of src across n periods
• sma(src, n=2): simple moving average of src across n periods
• ema(src, n=2): exponential moving average for a list of src across n periods
• atr(high, low, close, n=2): average true range from candlestick data
• roc(src, n=2): rate of change of src across n periods

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Momentum tools (momentum.py):

• macd(src, slow=25, fast=13): moving average convergence/divergence of src
• rsi(src, n=2): relative strength index of src across n periods
  • Used to measure the velocity and magnitude of directional price movement
• tsi(src, slow=25, fast=13): true strength indicator of src
  • Used to determine overbought/oversold conditions, and warning of trend weakness through divergence

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Technical indicators (indicators.py):

• td_sequential(src, n=2): TD sequential of src across n periods
• chaikin_money_flow(df, n=2): Chaikin Money Flow of an OHLCV dataset
• murrey_math_oscillator(src, n=2): Murrey Math oscillator of src

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Chart indicators:

• bollinger.py: Bollinger Bands
• ichimoku.py: Ichimoku Cloud
• renko.py: Renko Chart

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Additional tools (located in utils.py):

• group_candles(df, interval): 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).
• 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.
• crossover(x1, x2): find all instances of intersections between two lines
• intersection(a0, a1, b0, b1): find the intersection coordinates between vector A and vector B
• area_between(line1, line2): find the area between line1 and line2

How it works

import pandas as pd
%matplotlib inline

# NOTE: File should contain the columns 'date', 'open', 'high', 'low', and 'close'
df = pd.read_csv('../Daily.csv')

Bollinger Bands

from bollinger import Bollinger

b = Bollinger(df)
b.build(n=20)
b.plot()

Ichimoku

from ichimoku import Ichimoku

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

i.plot()

Renko

from renko import Renko

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

r.plot()