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TA Charting tool

Project description

TAcharts

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 when possible.


indicators

  • atr(high, low, close, n=2): average true range from candlestick data
  • bollinger(df=None, filename=None, interval=None, n=20, ndev=2): Bollinger bands for the close of an instrument
  • 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
  • ichimoku(df=None, filename=None, interval=None): Ichimoku Cloud
  • macd(src, slow=25, fast=13): moving average convergence/divergence of src
  • mmo(src, n=2): Murrey Math oscillator of src
  • renko(df=None, filename=None, interval=None): Renko Chart
  • roc(src, n=2): rate of change of src across n periods
  • rolling(src, n=2, fn=None, axis=1): rolling sum, max, min, or mean 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
  • demo_df: provide BTC's hourly OHLCV data in case no data is provided
  • 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

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
    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
    

How it works

Create your DataFrame variable

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

Bollinger Bands

from TAcharts.indicators.bollinger import bollinger
from TAcharts.plot import plot

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

png

Ichimoku

from TAcharts.indicators.ichimoku import Ichimoku
from TAcharts.plot import plot

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

i.plot()

png

Renko

from TAcharts.indicators.renko import Renko
from TAcharts.plot import plot


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

r.plot()

png

Project details


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