Strategy System

Overview

A Pandas TA Classic Strategy is a named group of indicators to be run by the strategy method. The Strategy Class is a simple way to name and group your favorite TA Indicators using a Data Class.

Pandas TA comes with two prebuilt basic Strategies:

AllStrategy - Runs all available indicators
CommonStrategy - Runs commonly used indicators

Strategy Requirements

name: Some short memorable string. Note: Case-insensitive “All” is reserved.
ta: A list of dicts containing keyword arguments to identify the indicator and the indicator’s arguments
Note: A Strategy will fail when consumed by Pandas TA if there is no {"kind": "indicator name"} attribute.

Optional Parameters

description: A more detailed description of what the Strategy tries to capture. Default: None
created: A datetime string of when it was created. Default: Automatically generated.

Types of Strategies

Builtin Strategies

# Running the Builtin CommonStrategy
df.ta.strategy(ta.CommonStrategy)

# The Default Strategy is the ta.AllStrategy. The following are equivalent:
df.ta.strategy()
df.ta.strategy("All")
df.ta.strategy(ta.AllStrategy)

Categorical Strategies

# List of indicator categories
df.ta.categories

# Running a Categorical Strategy only requires the Category name
df.ta.strategy("Momentum")  # Default values for all Momentum indicators
df.ta.strategy("overlap", length=42)  # Override all Overlap 'length' attributes

Custom Strategies

# Create your own Custom Strategy
CustomStrategy = ta.Strategy(
    name="Momo and Volatility",
    description="SMA 50,200, BBANDS, RSI, MACD and Volume SMA 20",
    ta=[
        {"kind": "sma", "length": 50},
        {"kind": "sma", "length": 200},
        {"kind": "bbands", "length": 20},
        {"kind": "rsi"},
        {"kind": "macd", "fast": 8, "slow": 21},
        {"kind": "sma", "close": "volume", "length": 20, "prefix": "VOLUME"},
    ]
)
# To run your "Custom Strategy"
df.ta.strategy(CustomStrategy)

Multiprocessing

The Pandas TA Classic strategy method utilizes multiprocessing for bulk indicator processing of all Strategy types with ONE EXCEPTION! When using the col_names parameter to rename resultant column(s), the indicators in ta array will be ran in order.

Basic Usage

# VWAP requires the DataFrame index to be a DatetimeIndex.
df.set_index(pd.DatetimeIndex(df["datetime"]), inplace=True)

# Runs and appends all indicators to the current DataFrame by default
df.ta.strategy()

# Use verbose if you want to make sure it is running
df.ta.strategy(verbose=True)

# Use timed if you want to see how long it takes to run
df.ta.strategy(timed=True)

# Choose the number of cores to use. Default is all available cores.
df.ta.cores = 4

# For no multiprocessing, set this value to 0.
df.ta.cores = 0

Excluding Indicators

# Maybe you do not want certain indicators
df.ta.strategy(exclude=["bop", "mom", "percent_return", "wcp", "pvi"], verbose=True)

# Perhaps you want to use different values for indicators
df.ta.strategy(fast=10, slow=50, verbose=True)

Custom Strategy without Multiprocessing

Remember: These will not be utilizing multiprocessing

NonMPStrategy = ta.Strategy(
    name="EMAs, BBs, and MACD",
    description="Non Multiprocessing Strategy by rename Columns",
    ta=[
        {"kind": "ema", "length": 8},
        {"kind": "ema", "length": 21},
        {"kind": "bbands", "length": 20, "col_names": ("BBL", "BBM", "BBU")},
        {"kind": "macd", "fast": 8, "slow": 21, "col_names": ("MACD", "MACD_H", "MACD_S")}
    ]
)
# Run it
df.ta.strategy(NonMPStrategy)