Metadata-Version: 2.1
Name: tuneta
Version: 0.1.14
Summary: Optimize financial technical indicators for machine learning
Home-page: https://github.com/jmrichardson/tuneta
Author: John Richardson
Author-email: jmrichardson@gmail.com
License: UNKNOWN
Description: <p align="center">
          <a href="https://github.com/jmrichardson/tuneta">
            <img src="images/logo.png" alt="tuneTA">
          </a>
        </p>
        
        TuneTA optimizes a broad set of technical indicators to maximize its correlation to a user defined target variable.  The set of tuned indicators can be reduced by choosing the most correlated with the target while minimizing correlation with each other. TuneTA maintains its state to add each tuned indicator to multiple data sets (train, validation, test).
        
        ### Features
        
        * Given financial prices (OHLCV) and a target variable such as return, optimizes technical indicator parameters to maximize the correlation to the target variable.
        * Select top x optimized indicators with most correlation to return with the least correlation to each other
        * Persist state to generate identical indicators on multiple datasets (train, validation, test)
        * Split multi-objective optimization
        * Early stopping
        * Correlation report of target and features
        * Parallel processing
        * Supports technical indicators produced from the following packages
          * [Pandas TA](https://github.com/twopirllc/pandas-ta)
          * [TA-Lib](https://github.com/mrjbq7/ta-lib)
          * [FinTA](https://github.com/peerchemist/finta)
        
        ### Overview
        
        TuneTA simplifies the process of optimizing technical indicators and selecting the best (measured by correlation) while minimizing the correlation between each other (optional).  Generally speaking, machine learning models perform better when provided informative inputs that are not strongly intercorrelated.  At a high level, TuneTA performs the following steps:
        
        1.  For each indicator, use an intelligent algorithm to find the best parameters which maximizes its correlation to the user defined target (ie next x day return).  TuneTA supports parameter optimization across multiple time periods.  Note the target can be a subset of X which is common for financial labeling such as with [Triple Barrier Labels](https://towardsdatascience.com/financial-machine-learning-part-1-labels-7eeed050f32e).
        2.  Optionally, the tuned parameters can be reduced by selecting the top x indicators measured by correlation, then selecting the least intercorrelated.
        3.  Finally, TuneTA will generate each indicator with the best parameters
        
        To illustrate using a toy example, 19 indicators from Pandas-TA are optimized using the "length" parameter (constrained by "length" parameter to graph in 2D).  The dotted black line indicates the "length" that optimizes the given indicator (max correlation to next day return).  Internally, TuneTA uses [Optuna](https://optuna.org) to efficiently search for the optimal "length" value:
        
        <p align="center">
          <a href="https://github.com/jmrichardson/tuneta">
            <img src="images/step1.jpg" alt="tuneTA" width="600">
          </a>
        </p>
        
        The following chart shows that of the top 10 strongest correlated indicators, 5 are selected which are the least correlated with each other (10 and 5 are user defined).
        
        <p align="center">
          <a href="https://github.com/jmrichardson/tuneta">
            <img src="images/step2.jpg" alt="tuneTA" width="600">
          </a>
        </p>
        
        ### Multi-Time Period Optimization
        
        TuneTA supports the optimization of parameters over an entire dataset or split into multiple time periods.  Technical indicators may not perform consistently across larger time frames due to market fluctuation.  Optimizing technical indicators over distinct time periods can help avoid correlation skew while providing insight into inconsistent performance.  TuneTA uses multi-objective optimization (pareto optimal) to maximize correlation in each time period with respect to deviation.
        
        The figure below illustrates the possibility of achieving high indicator correlation at the expense of inconsistency across the entire dataset (initial high correlation attenuating over time).  Splitting the dataset into parts and maximizing each split with the same set of parameters could potentially provide more consistent correlation.  Note, indicator parameters do not differ across splits, rather tuneta tries to select parameter(s) which provide maximum correlation to the target while minimizing deviation of all splits.  Note, multi-time period optimization may yield lower overall correlation as it tries to minimize variance especially over longer time periods. 
        
        <p align="center">
          <a href="https://github.com/jmrichardson/tuneta">
            <img src="images/multi.jpg" alt="Multi-Objective Optimization" width="600">
          </a>
        </p>
        
        ### Early Stopping
        
        Early stopping can be enabled to limit the number of optimization trials due to resource and time constraints:
        
        * Single time period optimization:  Stop after x number of trials performed without improvement in correlation.
        * Multi-time period optimization:  TuneTA finds the best non-dominated candidates of trials to maximize correlation of all time periods along the pareto-front.  A second pareto is generated among theses candidates to minimize deviation with respect to correlation within a pre-defined epsilon.  Stop if there are no changes in the best trial after x number of trials.
        
        ### Installation
        
        Install the latest code(recommended):
        
        ```python
        pip install -U git+https://github.com/jmrichardson/tuneta
        ```
        
        Install the latest release:
        
        ```python
        pip install -U tuneta
        ```
        
        
        ### Example Usage
        
        ```python
        import yfinance as yf
        import pandas as pd
        from pandas_ta import percent_return
        from tuneta.tune_ta import TuneTA
        from sklearn.model_selection import train_test_split
        import numpy as np
        
        
        if __name__ == "__main__":
            # Download data set from yahoo, calculate next day return and split into train and test
            X = yf.download("SPY", period="10y", interval="1d", auto_adjust=True)
            y = percent_return(X.Close, offset=-1)
            X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.3, shuffle=False)
        
            # Initialize with 2 cores and show trial results
            tt = TuneTA(n_jobs=2, verbose=True)
        
            # Optimize indicators
            tt.fit(X_train, y_train,
                           # Indicators to tune / optimize
                           # ":1" means optimize column index 1 vs default 0 if indicator returns dataframe
                           indicators=["tta.MACD", "tta.ULTOSC", "tta.AROON:1", "pta.rsi", "pta.kst", "pta.apo", "pta.zlma", "fta.ADX"],
                           ranges=[(2, 260)],  # Period range(s) to tune for each indicator
                           trials=200,  # Number of optimization trials per indicator per range
                           split=np.linspace(0, len(X_train), num=3).astype(int),  # Define split points
                           early_stop=30,  # Stop after number of trials without improvement
                           spearman=True,  # Type of correlation metric (Set False for Pearson)
                           weights=None,  # Optional weights for correlation evaluation
                           )
        
            # Show correlation of indicators to target
            tt.report(target_corr=True, features_corr=False)
        
            # Take top x tuned indicators, and select y with the least intercorrelation
            tt.prune(top=6, studies=4)
        
            # Show correlation of indicators to target and among themselves
            tt.report(target_corr=True, features_corr=True)
        
            # Add indicators to X_train
            features = tt.transform(X_train)
            X_train = pd.concat([X_train, features], axis=1)
        
            # Add same indicators to X_test
            features = tt.transform(X_test)
            X_test = pd.concat([X_test, features], axis=1)
        ```
        
        ***
        
        
        ### FAQ
        
        * The first split for mutli-time period optimization tends to have significantly different correlation than the remaining splits?
        
          * Calculating a technical indicator typically results in leading NANs until it has enough data.  These NANs are ignored when calculating correlation.  If you have too many splits or a small dataset, these NANs can lead to inflated or deflated correlation of the first split compared to the rest.  Adjust the split points to account for leading NANs or increase the dataset size to minimize the effect of leading NANs.
        
        Simple tests performed on the following indicators:
        
        #### TA-Lib
        * tta.BBANDS
        * tta.DEMA
        * tta.EMA
        * tta.HT_TRENDLINE
        * tta.KAMA
        * tta.MA
        * tta.MIDPOINT
        * tta.MIDPRICE
        * tta.SAR
        * tta.SAREXT
        * tta.SMA
        * tta.T3
        * tta.TEMA
        * tta.TRIMA
        * tta.WMA
        * tta.ADX
        * tta.ADXR
        * tta.APO
        * tta.AROON:1
        * tta.AROONOSC
        * tta.BOP
        * tta.CCI
        * tta.CMO
        * tta.DX
        * tta.MACD
        * tta.MACDEXT
        * tta.MACDFIX
        * tta.MFI
        * tta.MINUS_DI
        * tta.MINUS_DM
        * tta.MOM
        * tta.PLUS_DI
        * tta.PLUS_DM
        * tta.PPO
        * tta.ROC
        * tta.ROCP
        * tta.ROCR
        * tta.ROCR100
        * tta.RSI
        * tta.STOCH
        * tta.STOCHF
        * tta.STOCHRSI
        * tta.TRIX
        * tta.ULTOSC
        * tta.WILLR
        * tta.AD
        * tta.ADOSC
        * tta.OBV
        * tta.HT_DCPERIOD
        * tta.HT_DCPHASE
        * tta.HT_PHASOR
        * tta.HT_SINE
        * tta.HT_TRENDMODE
        * tta.AVGPRICE
        * tta.MEDPRICE
        * tta.TYPPRICE
        * tta.WCLPRICE
        * tta.ATR
        * tta.NATR
        * tta.TRANGE
        * tta.CDL2CROWS
        * tta.CDL3BLACKCROWS
        * tta.CDL3INSIDE
        * tta.CDL3LINESTRIKE
        * tta.CDL3OUTSIDE
        * tta.CDL3STARSINSOUTH
        * tta.CDL3WHITESOLDIERS
        * tta.CDLABANDONEDBABY
        * tta.CDLADVANCEBLOCK
        * tta.CDLBELTHOLD
        * tta.CDLBREAKAWAY
        * tta.CDLCLOSINGMARUBOZU
        * tta.CDLCONCEALBABYSWALL
        * tta.CDLCOUNTERATTACK
        * tta.CDLDARKCLOUDCOVER
        * tta.CDLDOJI
        * tta.CDLDOJISTAR
        * tta.CDLDRAGONFLYDOJI
        * tta.CDLENGULFING
        * tta.CDLEVENINGDOJISTAR
        * tta.CDLEVENINGSTAR
        * tta.CDLGAPSIDESIDEWHITE
        * tta.CDLGRAVESTONEDOJI
        * tta.CDLHAMMER
        * tta.CDLHANGINGMAN
        * tta.CDLHARAMI
        * tta.CDLHARAMICROSS
        * tta.CDLHIGHWAVE
        * tta.CDLHIKKAKE
        * tta.CDLHIKKAKEMOD
        * tta.CDLHOMINGPIGEON
        * tta.CDLIDENTICAL3CROWS
        * tta.CDLINNECK
        * tta.CDLINVERTEDHAMMER
        * tta.CDLKICKING
        * tta.CDLKICKINGBYLENGTH
        * tta.CDLLADDERBOTTOM
        * tta.CDLLONGLEGGEDDOJI
        * tta.CDLLONGLINE
        * tta.CDLMARUBOZU
        * tta.CDLMATCHINGLOW
        * tta.CDLMATHOLD
        * tta.CDLMORNINGDOJISTAR
        * tta.CDLMORNINGSTAR
        * tta.CDLONNECK
        * tta.CDLPIERCING
        * tta.CDLRICKSHAWMAN
        * tta.CDLRISEFALL3METHODS
        * tta.CDLSEPARATINGLINES
        * tta.CDLSHOOTINGSTAR
        * tta.CDLSHORTLINE
        * tta.CDLSPINNINGTOP
        * tta.CDLSTALLEDPATTERN
        * tta.CDLSTICKSANDWICH
        * tta.CDLTAKURI
        * tta.CDLTASUKIGAP
        * tta.CDLTHRUSTING
        * tta.CDLTRISTAR
        * tta.CDLUNIQUE3RIVER
        * tta.CDLUPSIDEGAP2CROWS
        * tta.CDLXSIDEGAP3METHODS
        * tta.LINEARREG
        * tta.LINEARREG_ANGLE
        * tta.LINEARREG_INTERCEPT
        * tta.LINEARREG_SLOPE
        * tta.STDDEV
        * tta.TSF
        * tta.VAR
        
        #### Pandas-TA
        * pta.cdl_doji
        * pta.cdl_inside
        * pta.ha
        * pta.ao
        * pta.apo
        * pta.bias
        * pta.bop
        * pta.brar
        * pta.cci
        * pta.cfo
        * pta.cg
        * pta.cmo
        * pta.coppock
        * pta.er
        * pta.eri
        * pta.fisher
        * pta.inertia
        * pta.kdj
        * pta.kst
        * pta.macd
        * pta.mom
        * pta.pgo
        * pta.ppo
        * pta.psl
        * pta.pvo
        * pta.qqe
        * pta.roc
        * pta.rsi
        * pta.rvgi
        * pta.slope
        * pta.smi
        * pta.squeeze
        * pta.stoch
        * pta.stochrsi
        * pta.trix
        * pta.tsi
        * pta.uo
        * pta.willr
        * pta.dema
        * pta.ema
        * pta.fwma
        * pta.hilo
        * pta.hl2
        * pta.hlc3
        * pta.hma
        * pta.kama
        * pta.linreg
        * pta.midpoint
        * pta.midprice
        * pta.ohlc4
        * pta.pwma
        * pta.rma
        * pta.sinwma
        * pta.sma
        * pta.ssf
        * pta.supertrend
        * pta.swma
        * pta.t3
        * pta.tema
        * pta.trima
        * pta.vidya
        * pta.vwap
        * pta.vwma
        * pta.wcp
        * pta.wma
        * pta.zlma
        * pta.entropy
        * pta.mad
        * pta.median
        * pta.quantile
        * pta.stdev
        * pta.variance
        * pta.zscore
        * pta.adx
        * pta.amat
        * pta.aroon
        * pta.chop
        * pta.cksp
        * pta.decay
        * pta.decreasing
        * pta.increasing
        * pta.psar
        * pta.qstick
        * pta.ttm_trend
        * pta.vortex
        * pta.aberration
        * pta.accbands
        * pta.atr
        * pta.bbands
        * pta.donchian
        * pta.kc
        * pta.massi
        * pta.natr
        * pta.pdist
        * pta.rvi
        * pta.thermo
        * pta.true_range
        * pta.ui
        * pta.ad
        * pta.adosc
        * pta.aobv
        * pta.cmf
        * pta.efi
        * pta.eom
        * pta.mfi
        * pta.nvi
        * pta.obv
        * pta.pvi
        * pta.pvol
        * pta.pvt
        
        #### FinTA
        * fta.SMA
        * fta.SMM
        * fta.SSMA
        * fta.EMA
        * fta.DEMA
        * fta.TEMA
        * fta.TRIMA
        * fta.TRIX
        * fta.VAMA
        * fta.ER
        * fta.KAMA
        * fta.ZLEMA
        * fta.WMA
        * fta.HMA
        * fta.EVWMA
        * fta.VWAP
        * fta.SMMA
        * fta.MACD
        * fta.PPO
        * fta.VW_MACD
        * fta.EV_MACD
        * fta.MOM
        * fta.ROC
        * fta.RSI
        * fta.IFT_RSI
        * fta.TR
        * fta.ATR
        * fta.SAR
        * fta.BBANDS
        * fta.BBWIDTH
        * fta.MOBO
        * fta.PERCENT_B
        * fta.KC
        * fta.DO
        * fta.DMI
        * fta.ADX
        * fta.PIVOT
        * fta.PIVOT_FIB
        * fta.STOCH
        * fta.STOCHD
        * fta.STOCHRSI
        * fta.WILLIAMS
        * fta.UO
        * fta.AO
        * fta.MI
        * fta.VORTEX
        * fta.KST
        * fta.TSI
        * fta.TP
        * fta.ADL
        * fta.CHAIKIN
        * fta.MFI
        * fta.OBV
        * fta.WOBV
        * fta.VZO
        * fta.PZO
        * fta.EFI
        * fta.CFI
        * fta.EBBP
        * fta.EMV
        * fta.CCI
        * fta.COPP
        * fta.BASP
        * fta.BASPN
        * fta.CMO
        * fta.CHANDELIER
        * fta.WTO
        * fta.FISH
        * fta.APZ
        * fta.SQZMI
        * fta.VPT
        * fta.FVE
        * fta.VFI
        * fta.MSD
        * fta.STC
        
        
Keywords: technical analysis optimize tune indicators machine learning optuna tune hyperparameter optimization AI correlation
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Development Status :: 4 - Beta
Classifier: Natural Language :: English
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
