pyindicators 0.2.0

PyIndicators is a powerful and user-friendly Python library for technical analysis indicators and metrics. Written entirely in Python, it requires no external dependencies, ensuring seamless integration and ease of use.

PyIndicators

PyIndicators is a powerful and user-friendly Python library for technical analysis indicators and metrics. Written entirely in Python, it requires no external dependencies, ensuring seamless integration and ease of use.

Installation

PyIndicators can be installed using pip:

pip install pyindicators

Features

• Native Python implementation, no external dependencies needed except for Polars or Pandas
• Dataframe first approach, with support for both pandas dataframes and polars dataframes
• Trend indicators
  • Simple Moving Average (SMA)
  • Exponential Moving Average (EMA)
• Momentum indicators
  • Relative Strength Index (RSI)
  • Relative Strength Index Wilders method (RSI)

Indicators

Trend Indicators

Simple Moving Average (SMA)

from investing_algorithm_framework import CSVOHLCVMarketDataSource

from pyindicators import sma

# For this example the investing algorithm framework is used for dataframe creation,
csv_path = "./tests/test_data/OHLCV_BTC-EUR_BINANCE_15m_2023-12-01:00:00_2023-12-25:00:00.csv"
data_source = CSVOHLCVMarketDataSource(csv_file_path=csv_path)

pl_df = data_source.get_data()
pd_df = data_source.get_data(pandas=True)

# Calculate SMA for Polars DataFrame
pl_df = sma(pl_df, source_column="Close", period=200, result_column="SMA_200")
pl_df.show(10)

# Calculate SMA for Pandas DataFrame
pd_df = sma(pd_df, source_column="Close", period=200, result_column="SMA_200")
pd_df.tail(10)

Exponential Moving Average (EMA)

from investing_algorithm_framework import CSVOHLCVMarketDataSource

from pyindicators import ema

# For this example the investing algorithm framework is used for dataframe creation,
csv_path = "./tests/test_data/OHLCV_BTC-EUR_BINANCE_15m_2023-12-01:00:00_2023-12-25:00:00.csv"
data_source = CSVOHLCVMarketDataSource(csv_file_path=csv_path)

pl_df = data_source.get_data()
pd_df = data_source.get_data(pandas=True)

# Calculate EMA for Polars DataFrame
pl_df = ema(pl_df, source_column="Close", period=200, result_column="EMA_200")
pl_df.show(10)

# Calculate EMA for Pandas DataFrame
pd_df = ema(pd_df, source_column="Close", period=200, result_column="EMA_200")
pd_df.tail(10)

Momentum Indicators

Relative Strength Index (RSI)

from investing_algorithm_framework import CSVOHLCVMarketDataSource

from pyindicators import rsi

# For this example the investing algorithm framework is used for dataframe creation,
csv_path = "./tests/test_data/OHLCV_BTC-EUR_BINANCE_15m_2023-12-01:00:00_2023-12-25:00:00.csv"
data_source = CSVOHLCVMarketDataSource(csv_file_path=csv_path)

pl_df = data_source.get_data()
pd_df = data_source.get_data(pandas=True)

# Calculate RSI for Polars DataFrame
pl_df = rsi(pl_df, source_column="Close", period=14, result_column="RSI_14")
pl_df.show(10)

# Calculate RSI for Pandas DataFrame
pd_df = rsi(pd_df, source_column="Close", period=14, result_column="RSI_14")
pd_df.tail(10)

Wilders Relative Strength Index (Wilders RSI)

from investing_algorithm_framework import CSVOHLCVMarketDataSource

from pyindicators import wilders_rsi

# For this example the investing algorithm framework is used for dataframe creation,
csv_path = "./tests/test_data/OHLCV_BTC-EUR_BINANCE_15m_2023-12-01:00:00_2023-12-25:00:00.csv"
data_source = CSVOHLCVMarketDataSource(csv_file_path=csv_path)

pl_df = data_source.get_data()
pd_df = data_source.get_data(pandas=True)

# Calculate Wilders RSI for Polars DataFrame
pl_df = wilders_rsi(pl_df, source_column="Close", period=14, result_column="RSI_14")
pl_df.show(10)

# Calculate Wilders RSI for Pandas DataFrame
pd_df = wilders_rsi(pd_df, source_column="Close", period=14, result_column="RSI_14")
pd_df.tail(10)

Indicator helpers

Is Crossover

from polars import DataFrame as plDataFrame
from pandas import DataFrame as pdDataFrame

from pyindicators import is_crossover

# Polars DataFrame
pl_df = plDataFrame({
    "EMA_50": [200, 201, 202, 203, 204, 205, 206, 208, 208, 210],
    "EMA_200": [200, 201, 202, 203, 204, 205, 206, 207, 209, 209],
    "DateTime": pd.date_range("2021-01-01", periods=10, freq="D")
})
# Pandas DataFrame
pd_df = pdDataFrame({
    "EMA_50": [200, 201, 202, 203, 204, 205, 206, 208, 208, 210],
    "EMA_200": [200, 201, 202, 203, 204, 205, 206, 207, 209, 209],
    "DateTime": pd.date_range("2021-01-01", periods=10, freq="D")
})

if is_crossover(
    pl_df, first_column="EMA_50", second_column="EMA_200", data_points=3
):
    print("Crossover detected in Polars DataFrame")


if is_crossover(
    pd_df, first_column="EMA_50", second_column="EMA_200", data_points=3
):
    print("Crossover detected in Pandas DataFrame")