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

vsaveris/trading-technical-indicators

trading-technical-indicators (tti)

Usage Example