Metadata-Version: 2.1
Name: TAcharts
Version: 0.0.2
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: # 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
        
        ---
        
        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
        
        ---
        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`
        ---
        Chart indicators:
        * `bollinger.py`: Bollinger Bands
        * `ichimoku.py`: Ichimoku Cloud
        * `renko.py`: Renko Chart
        ---
        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
        
        ```python
        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
        ```python
        from bollinger import Bollinger
        
        b = Bollinger(df)
        b.build(n=20)
        b.plot()
        ```
        ![png](img/bollinger.PNG)
        
        #### Ichimoku
        ```python
        from ichimoku import Ichimoku
        
        i = Ichimoku(df)
        i.build(20, 60, 120, 30)
        
        i.plot()
        ```
        ![png](img/ichimoku.PNG)
        
        
        #### Renko
        ```python
        from renko import Renko
        
        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
