Metadata-Version: 2.1
Name: TAcharts
Version: 0.0.6
Summary: TA Charting tool
Home-page: https://github.com/carlfarterson/TAcharts
Author: Carter Carlson
Author-email: carlfarterson@gmail.com
License: MIT
Download-URL: https://github.com/carlfarterson/TAcharts/archive/v_0.0.1.tar.gz
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
          ```python
          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`
          ```python
          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
        ```python
        # NOTE: File should contain the columns 'date', 'open', 'high', 'low', and 'close'
        import pandas as pd
        df = pd.read_csv('../Daily.csv')
        ```
        
        #### Bollinger Bands
        ```python
        from TAcharts.indicators.bollinger import bollinger
        from TAcharts.plot import plot
        
        b = Bollinger(df)
        b.build(n=20)
        b.plot()
        ```
        ![png](img/bollinger.PNG)
        
        #### Ichimoku
        ```python
        from TAcharts.indicators.ichimoku import Ichimoku
        from TAcharts.plot import plot
        
        i = Ichimoku(df)
        i.build(20, 60, 120, 30)
        
        i.plot()
        ```
        ![png](img/ichimoku.PNG)
        
        
        #### Renko
        ```python
        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](img/renko.PNG)
        
Keywords: TA,mathematics,algorithms
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Intended Audience :: Developers
Requires-Python: >=3.6
Description-Content-Type: text/markdown
