Metadata-Version: 2.1
Name: tti
Version: 0.2.1
Summary: Trading Technical Indicators, python library. Where Traditional Technical Analysis and AI are met.
Home-page: https://www.trading-technical-indicators.org/
Author: Vasileios Saveris
Author-email: vsaveris@gmail.com
License: MIT
Download-URL: https://pypi.org/project/tti/#files
Project-URL: Bug Tracker, https://github.com/vsaveris/trading-technical-indicators/issues
Project-URL: Documentation, https://www.trading-technical-indicators.org/api-docs
Project-URL: Source Code, https://github.com/vsaveris/trading-technical-indicators
Description: # trading-technical-indicators (tti)
        **Trading Technical Indicators python library, where Traditional Technical Analysis and AI are met. Version 0.2.1 (stable release)**
        - Calculate technical indicators (62 indicators supported).
        - Produce graphs for any technical indicator.
        - Get trading signals for each indicator.
        - Trading simulation based on trading signals.
        - Machine Learning integration for prices prediction (not included in this release).
        
        *Implementation based on the book 'Technical Analysis from A to Z, Steven B. Achelis'. Validation based on the 'A to Z Companion Spreadsheet, Steven B. Achelis and Jon C. DeBry'*
        
        API documentation and installation instructions can be found in the project's web-site:
        [Trading Technical Indicators](https://www.trading-technical-indicators.org/)
        
        **Change Log**
        
        *Stable Releases*
        - 0.2.1: Bug fixes, new pandas release causes an exception in some indicators calculation ([#20](https://github.com/vsaveris/trading-technical-indicators/issues/20))
        - 0.2.0: First stable release, updates described in the following github issues ([#2](https://github.com/vsaveris/trading-technical-indicators/issues/2), [#3](https://github.com/vsaveris/trading-technical-indicators/issues/3), [#14](https://github.com/vsaveris/trading-technical-indicators/issues/14), [#15](https://github.com/vsaveris/trading-technical-indicators/issues/15))
        
        *Beta Releases*
        - 0.1.b3: Updates described in the following github issues ([#11](https://github.com/vsaveris/trading-technical-indicators/issues/11), [#7](https://github.com/vsaveris/trading-technical-indicators/issues/7), [#8](https://github.com/vsaveris/trading-technical-indicators/issues/8))
        - 0.1.b2: Bugs fixes ([#1](https://github.com/vsaveris/trading-technical-indicators/issues/1))
        - 0.1.b1: Cosmetic changes in package building file applied (setup.py)
        - 0.1.b0: First beta release
        
        *Planned Releases*
        - 1.0.0: Full featured release, including machine learning related features (*planned for 01.03.2021*).
        
        **Indicators supported**
        - Accumulation Distribution Line
        - Average True Range
        - Bollinger Bands
        - Chaikin Money Flow
        - Chaikin Oscillator
        - Chande Momentum Oscillator
        - Commodity Channel Index
        - Detrended Price Oscillator
        - Directional Movement Index
        - Double Exponential Moving Average
        - Ease Of Movement
        - Envelopes
        - Fibonacci Retracement
        - Forecast Oscillator
        - Ichimoku Cloud
        - Intraday Movement Index
        - Klinger Oscillator
        - Linear Regression Indicator
        - Linear Regression Slope
        - Market Facilitation Index
        - Mass Index
        - Median Price
        - Momentum
        - Exponential Moving Average
        - Simple Moving Average
        - Time-Series Moving Average
        - Triangular Moving Average
        - Variable Moving Average
        - Moving Average Convergence Divergence
        - Negative Volume Index
        - On Balance Volume
        - Parabolic SAR
        - Performance
        - Positive Volume Index
        - Price And Volume Trend
        - Price Channel
        - Price Oscillator
        - Price Rate Of Change
        - Projection Bands
        - Projection Oscillator
        - Qstick
        - Range Indicator
        - Relative Momentum Index
        - Relative Strength Index
        - Relative Volatility Index
        - Standard Deviation
        - Stochastic Momentum Index
        - Fast Stochastic Oscillator
        - Slow Stochastic Oscillator
        - Swing Index
        - Time Series Forecast
        - Triple Exponential Moving Average
        - Typical Price
        - Ultimate Oscillator
        - Vertical Horizontal Filter
        - Volatility Chaikins
        - Volume Oscillator
        - Volume Rate Of Change
        - Weighted Close
        - Wilders Smoothing
        - Williams Accumulation Distribution
        - Williams %R
        
        ### Usage Example
        
        **Code example**
        ```python
        """
        Trading-Technical-Indicators (tti) python library
        
        File name: indicator_example.py
            Example code for the trading technical indicators, for the docs.
        
        Accumulation Distribution Line indicator and SCMN.SW.csv data file is used.
        """
        
        import pandas as pd
        from tti.indicators import AccumulationDistributionLine
        
        # Read data from csv file. Set the index to the correct column
        # (dates column)
        df = pd.read_csv('./data/SCMN.SW.csv', parse_dates=True, index_col=0)
        
        # Create indicator
        adl_indicator = AccumulationDistributionLine(input_data=df)
        
        # Get indicator's calculated data
        print('\nTechnical Indicator data:\n', adl_indicator.getTiData())
        
        # Get indicator's value for a specific date
        print('\nTechnical Indicator value at 2012-09-06:', adl_indicator.getTiValue('2012-09-06'))
        
        # Get the most recent indicator's value
        print('\nMost recent Technical Indicator value:', adl_indicator.getTiValue())
        
        # Get signal from indicator
        print('\nTechnical Indicator signal:', adl_indicator.getTiSignal())
        
        # Show the Graph for the calculated Technical Indicator
        adl_indicator.getTiGraph().show()
        
        # Execute simulation based on trading signals
        simulation_data, simulation_statistics, simulation_graph = \
            adl_indicator.getTiSimulation(
                close_values=df[['close']], max_exposure=None,
                short_exposure_factor=1.5)
        print('\nSimulation Data:\n', simulation_data)
        print('\nSimulation Statistics:\n', simulation_statistics)
        
        # Show the Graph for the executed trading signal simulation
        simulation_graph.show()
        ```
        
        **Output**
        ```
        Technical Indicator data:
                              adl
        Date
        1998-10-05  5.346066e+05
        1998-10-06  9.788753e+05
        1998-10-07  1.377338e+06
        1998-10-08  1.251994e+06
        1998-10-09  1.108012e+06
        ...                  ...
        2020-11-30  1.736986e+07
        2020-12-01  1.741746e+07
        2020-12-02  1.737860e+07
        2020-12-03  1.741683e+07
        2020-12-04  1.742771e+07
        
        [5651 rows x 1 columns]
        
        Technical Indicator value at 2012-09-06: [8617026.854250321]
        
        Most recent Technical Indicator value: [17427706.42639293]
        
        Technical Indicator signal: ('buy', -1)
        
        Simulation Data:
                    signal open_trading_action  ... earnings  balance
        Date                                   ...
        1998-10-05   hold                none  ...        0        0
        1998-10-06    buy                long  ...        0  385.138
        1998-10-07    buy                long  ...   13.264  411.666
        1998-10-08    buy                long  ...   13.264  777.644
        1998-10-09    buy                long  ...   19.159  795.329
        ...           ...                 ...  ...      ...      ...
        2020-11-30    buy                long  ...  19817.2  37577.2
        2020-12-01   hold                none  ...  19817.2  37577.2
        2020-12-02    buy                long  ...  19817.2  38019.2
        2020-12-03    buy                long  ...  19817.2  38385.1
        2020-12-04    buy                long  ...  19817.2  38837.2
        
        [5651 rows x 7 columns]
        
        Simulation Statistics:
         {'number_of_trading_days': 5651, 'number_of_buy_signals': 4767, 'number_of_ignored_buy_signals': 0, 'number_of_sell_signals': 601, 'number_of_ignored_sell_signals': 0, 'last_stock_value': 475.5, 'last_exposure': 22340.73, 'last_open_long_positions': 40, 'last_open_short_positions': 0, 'last_portfolio_value': 19020.0, 'last_earnings': 19817.21, 'final_balance': 38837.21}
        ```
        
        **Output graphs**
        
        ![](./examples/for_docs/figures/example_AccumulationDistributionLine.png?raw=true)
        
        ![](./examples/for_docs/figures/simulation_AccumulationDistributionLine.png?raw=true)
        
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Topic :: Software Development
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Office/Business :: Financial
Requires-Python: >=3.8
Description-Content-Type: text/markdown
