technical-analysis 0.0.4

Technical Analysis with Python

Technical Analysis for Python

Technical Analysis (TA) is the study of price movements.

This package aims to provide an extensible framework for working with various TA tools. This includes, but is not limited to: candlestick patterns, technical overlays, technical indicators, statistical analysis, and automated strategy backtesting.

Why Use This Library?

The Technical Analysis Library is still in its early days, but already has the following:

1. Recognition for 30+ Candlestick Patterns
2. 10+ technical indicators
3. 10+ moving average types (including adaptive)
4. Technical overlays
5. Automated backtests and strategies
6. Statistical tools to analyze price action
7. A highly extensible framework to create custom indicators, backtests, and strategies

Installation

Pypi link: https://pypi.org/project/technical-analysis/

pip install technical-analysis

Overview

This package assumes you're working with pandas dataframes. If you're not familiar with pandas, see the docs here https://pandas.pydata.org/docs/

Technical Overlays Example Usage

Technical Overlays are indicators placed directly on a chart. These include moving averages and volatility bands.

Moving Averages

>>> from technical_analysis import moving_average
>>> # simple moving average
>>> df["sma9"] = moving_average.sma(df.close, 9)
>>>
>>> # exponentially-weighted moving average
>>> df["ema9"] = moving_average.ema(df.close, period=9)
>>>
>>> # triangular moving average
>>> df["tma9"] = moving_average.tma(df.close, 9)
>>>
>>> # linearly-weighted moving average
>>> df["lwma9"] = moving_average.lwma(df.close, 9)
>>>
>>> # kaufman adaptive moving average
>>> df["kama9"] = moving_average.kama(df.close, 9, min_smoothing_constant=3, max_smoothing_constant=30)
>>>
>>> # wilder moving average
>>> df["wilder9"] = moving_average.wilder_ma(df.close, 9)

Bands

>>> from technical_analysis import overlays
>>> # bollinger bands
>>> df["bband_lower"], df["bband_upper"] = overlays.bbands(df.close, period=20)
>>>
>>> # donchian bands
>>> df["dband_lower"], df["dband_upper"] = overlays.dbands(df.close, period=20)
>>>
>>> # keltner bands
>>> df["kband_lower"], df["kband_upper"] = overlays.kbands(df.high, df.low, df.close, period=20)

Technical Indicators Example Usage

>>> from technical_analysis import indicators
>>> # average true range
>>> df["atr"] = indicators.atr(df.high, df.low, df.close, period=14)
>>>
>>> # relative strength index
>>> df["rsi"] = indicators.rsi(df.close, period=14)
>>>
>>> # Williams' %R
>>> df["perc_r"] = indicators.perc_r(df.high, df.low, df.close, period=14)
>>>
>>> # true strength index
>>> df["tsi"] = indicators.tsi(df.close, period1=25, period2=13)
>>>
>>> # TRIX
>>> df["trix"] = indicators.trix(df.close, period=15)
>>>
>>> # stochastic %k, %d (fast, slow, or full)
>>> df["stoch_k"], df["stoch_d"] = indicators.stochastic(df.high, df.low, df.close, period=14, perc_k_smoothing=3)
>>>
>>> # macd histogram
>>> df["macd_histogram"] = indicators.macd(df.close, return_histogram=True)

Candlestick Pattern Recognition Example Usage

>>> from technical_analysis import candles
>>> df["gap_down"] = candles.is_gap_down(df.high, df.low, min_gap_size=0.003)
>>> df["gap_up"] = candles.is_gap_down(df.high, df.low, min_gap_size=0.003)
>>> df["long_body"] = candles.is_long_body(df.open, df.high, df.low, df.close, min_body_size=0.7)
>>> df["doji"] = candles.is_doji(df.open, df.high, df.low, df.close, relative_threshold=0.1)
>>> df["outside"] = candles.is_outside(df.high, df.low)
>>> df["inside"] = candles.is_inside(df.high, df.low)
>>> df["spinning_top"] = candles.spinning_top(df.open, df.high, df.low, df.close)
>>> df["marubozu"] = candles.is_marubozu(df.open, df.high, df.low, df.close, max_shadow_size=0.2)
>>> df["bullish_engulfing"] = candles.bullish_engulfing(df.open, df.high, df.low, df.close)
>>> df["bearish_engulfing"] = candles.bearish_engulfing(df.open, df.high, df.low, df.close)

Automatic Backtesting Example Usage

The technical-analysis library comes with an extensible framework to backtest trading strategies.

>>> from technical_analysis.backtest import Backtest
>>> from technical_analysis.backtest.strategy import MovingAverageCrossover
>>> from technical_analysis import overlays
>>>
>>> # test an exponential moving average crossover strategy
>>> df["ema9"] = overlays.ema(df.close, period=9)
>>> df["ema20"] = overlays.ema(df.close, period=20)
>>> df = df.dropna().reset_index(drop=True)
>>> entry_criteria=[MovingAverageCrossover("ema9", "ema20", "bullish")]
>>> exit_criteria=[MovingAverageCrossover("ema9", "ema20", "bearish")]
>>> backtest = Backtest(entry_criteria, exit_criteria, max_positions=1, use_next_open=True)
>>> backtest.run(df)
>>> backtest.results
{'benchmark': 3.925821463626707,
 'strategy': 1.2970321301363634,
 'max_drawdown': -0.10934780434803487,
 'max_profit': 0.20020259422562683,
 'avg_return': 0.015817465001662968,
 'std_return': 0.057687131745236445,
 'returns': [0.01751003732275545, ...]}

Timeseries Analysis

The technical-analysis library comes with useful timeseries analysis tools.

>>> from technical_analysis.stats import autocorr_coef, period
>>> # auto-correlation
>>> corr = autocorr_coef(df.close.pct_change())
>>> np.argsort(corr)[::-1][:10]
array([199,  62,  72,  71,  70,  69,  68,  67,  66,  65])
>>>
>>> # periodicity
>>> period(df.close, top_n=10)
array([ 1,  2,  5,  4,  3,  7, 16, 10, 25, 38])
>>>
>>> # hurst exponent
>>> hurst_exp(df.close)
0.3238867311092554

BSD 3-Clause License

Copyright (c) 2023 Trevor McGuire. All rights reserved.

Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:

• Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.

• Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.

• Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.