trading-technical-indicators (tti)

Trading Technical Indicators python library, where Traditional Technical Analysis and AI are met. Version 0.2.2 (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

Change Log

Stable Releases

• 0.2.2: Incompatibilities with the latest pandas release 1.2.0 fixed (#20)
• 0.2.1: Bug fixes, new pandas release causes an exception in some indicators calculation (#20)
• 0.2.0: First stable release, updates described in the following github issues (#2, #3, #14, #15)

Beta Releases

• 0.1.b3: Updates described in the following github issues (#11, #7, #8)
• 0.1.b2: Bugs fixes (#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

"""
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