PyIndicators is a powerful and user-friendly Python library for technical analysis indicators, metrics and helper functions. Written entirely in Python, it requires no external dependencies, ensuring seamless integration and ease of use.
PyIndicators can be installed using pip:
pip install pyindicators- Native Python implementation, no external dependencies needed except for Polars or Pandas
- Dataframe first approach, with support for both pandas dataframes and polars dataframes
- Supports python version 3.9 and above.
- Trend indicators
- Momentum indicators
- Indicator helpers
Indicators that help to determine the direction of the market (uptrend, downtrend, or sideways) and confirm if a trend is in place.
A Weighted Moving Average (WMA) is a type of moving average that assigns greater importance to recent data points compared to older ones. This makes it more responsive to recent price changes compared to a Simple Moving Average (SMA), which treats all data points equally. The WMA does this by using linear weighting, where the most recent prices get the highest weight, and weights decrease linearly for older data points.
def wma(
data: Union[PandasDataFrame, PolarsDataFrame],
source_column: str,
period: int,
result_column: Optional[str] = None
) -> Union[PandasDataFrame, PolarsDataFrame]:Example
from investing_algorithm_framework import CSVOHLCVMarketDataSource
from pyindicators import wma
# 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 = wma(pl_df, source_column="Close", period=200, result_column="WMA_200")
pl_df.show(10)
# Calculate SMA for Pandas DataFrame
pd_df = wma(pd_df, source_column="Close", period=200, result_column="WMA_200")
pd_df.tail(10)
A Simple Moving Average (SMA) is the average of the last N data points, recalculated as new data comes in. Unlike the Weighted Moving Average (WMA), SMA treats all values equally, giving them the same weight.
def sma(
data: Union[PdDataFrame, PlDataFrame],
source_column: str,
period: int,
result_column: str = None,
) -> Union[PdDataFrame, PlDataFrame]:Example
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)
The Exponential Moving Average (EMA) is a type of moving average that gives more weight to recent prices, making it more responsive to price changes than a Simple Moving Average (SMA). It does this by using an exponential decay where the most recent prices get exponentially more weight.
def ema(
data: Union[PdDataFrame, PlDataFrame],
source_column: str,
period: int,
result_column: str = None,
) -> Union[PdDataFrame, PlDataFrame]:Example
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)
Indicators that measure the strength and speed of price movements rather than the direction.
The Moving Average Convergence Divergence (MACD) is used to identify trend direction, strength, and potential reversals. It is based on the relationship between two Exponential Moving Averages (EMAs) and includes a histogram to visualize momentum.
def macd(
data: Union[PdDataFrame, PlDataFrame],
source_column: str,
short_period: int = 12,
long_period: int = 26,
signal_period: int = 9,
macd_column: str = "macd",
signal_column: str = "macd_signal",
histogram_column: str = "macd_histogram"
) -> Union[PdDataFrame, PlDataFrame]:Example
from investing_algorithm_framework import CSVOHLCVMarketDataSource
from pyindicators import macd
# 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 MACD for Polars DataFrame
pl_df = macd(pl_df, source_column="Close", short_period=12, long_period=26, signal_period=9)
# Calculate MACD for Pandas DataFrame
pd_df = macd(pd_df, source_column="Close", short_period=12, long_period=26, signal_period=9)
pl_df.show(10)
pd_df.tail(10)
The Relative Strength Index (RSI) is a momentum oscillator that measures the speed and change of price movements. It moves between 0 and 100 and is used to identify overbought or oversold conditions in a market.
def rsi(
data: Union[pd.DataFrame, pl.DataFrame],
source_column: str,
period: int = 14,
result_column: str = None,
) -> Union[pd.DataFrame, pl.DataFrame]:Example
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)
The Wilders Relative Strength Index (RSI) is a momentum oscillator that measures the speed and change of price movements. It moves between 0 and 100 and is used to identify overbought or oversold conditions in a market. The Wilders RSI uses a different calculation method than the standard RSI.
def wilders_rsi(
data: Union[pd.DataFrame, pl.DataFrame],
source_column: str,
period: int = 14,
result_column: str = None,
) -> Union[pd.DataFrame, pl.DataFrame]:Example
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)
Williams %R (Williams Percent Range) is a momentum indicator used in technical analysis to measure overbought and oversold conditions in a market. It moves between 0 and -100 and helps traders identify potential reversal points.
def willr(
data: Union[pd.DataFrame, pl.DataFrame],
period: int = 14,
result_column: str = None,
high_column: str = "High",
low_column: str = "Low",
close_column: str = "Close"
) -> Union[pd.DataFrame, pl.DataFrame]:Example
from investing_algorithm_framework import CSVOHLCVMarketDataSource
from pyindicators import willr
# 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 Williams%R for Polars DataFrame
pl_df = willr(pl_df, result_column="WILLR")
pl_df.show(10)
# Calculate Williams%R for Pandas DataFrame
pd_df = willr(pd_df, result_column="WILLR")
pd_df.tail(10)
The crossover function is used to calculate the crossover between two columns in a DataFrame. It returns a new DataFrame with an additional column that contains the crossover values. A crossover occurs when the first column crosses above or below the second column. This can happen in two ways, a strict crossover or a non-strict crossover. In a strict crossover, the first column must cross above or below the second column. In a non-strict crossover, the first column must cross above or below the second column, but the values can be equal.
def crossover(
data: Union[PdDataFrame, PlDataFrame],
first_column: str,
second_column: str,
result_column="crossover",
data_points: int = None,
strict: bool = True,
) -> Union[PdDataFrame, PlDataFrame]:Example
from polars import DataFrame as plDataFrame
from pandas import DataFrame as pdDataFrame
from investing_algorithm_framework import CSVOHLCVMarketDataSource
from pyindicators import crossover, 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 and crossover for Polars DataFrame
pl_df = ema(pl_df, source_column="Close", period=200, result_column="EMA_200")
pl_df = ema(pl_df, source_column="Close", period=50, result_column="EMA_50")
pl_df = crossover(
pl_df,
first_column="EMA_50",
second_column="EMA_200",
result_column="Crossover_EMA"
)
pl_df.show(10)
# Calculate EMA and crossover for Pandas DataFrame
pd_df = ema(pd_df, source_column="Close", period=200, result_column="EMA_200")
pd_df = ema(pd_df, source_column="Close", period=50, result_column="EMA_50")
pd_df = crossover(
pd_df,
first_column="EMA_50",
second_column="EMA_200",
result_column="Crossover_EMA"
)
pd_df.tail(10)
The is_crossover function is used to determine if a crossover occurred in the last N data points. It returns a boolean value indicating if a crossover occurred in the last N data points. The function can be used to check for crossovers in a DataFrame that was previously calculated using the crossover function.
def is_crossover(
data: Union[PdDataFrame, PlDataFrame],
first_column: str = None,
second_column: str = None,
crossover_column: str = None,
number_of_data_points: int = None,
strict=True,
) -> bool:Example
from polars import DataFrame as plDataFrame
from pandas import DataFrame as pdDataFrame
from investing_algorithm_framework import CSVOHLCVMarketDataSource
from pyindicators import crossover, 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 and crossover for Polars DataFrame
pl_df = ema(pl_df, source_column="Close", period=200, result_column="EMA_200")
pl_df = ema(pl_df, source_column="Close", period=50, result_column="EMA_50")
pl_df = crossover(
pl_df,
first_column="EMA_50",
second_column="EMA_200",
result_column="Crossover_EMA"
)
# If you want the function to calculate the crossovors in the function
if is_crossover(
pl_df, first_column="EMA_50", second_column="EMA_200", data_points=3
):
print("Crossover detected in Pandas DataFrame in the last 3 data points")
# If you want to use the result of a previous crossover calculation
if is_crossover(pl_df, crossover_column="Crossover_EMA", data_points=3):
print("Crossover detected in Pandas DataFrame in the last 3 data points")
# Calculate EMA and crossover for Pandas DataFrame
pd_df = ema(pd_df, source_column="Close", period=200, result_column="EMA_200")
pd_df = ema(pd_df, source_column="Close", period=50, result_column="EMA_50")
pd_df = crossover(
pd_df,
first_column="EMA_50",
second_column="EMA_200",
result_column="Crossover_EMA"
)
# If you want the function to calculate the crossovors in the function
if is_crossover(
pd_df, first_column="EMA_50", second_column="EMA_200", data_points=3
):
print("Crossover detected in Pandas DataFrame in the last 3 data points")
# If you want to use the result of a previous crossover calculation
if is_crossover(pd_df, crossover_column="Crossover_EMA", data_points=3):
print("Crossover detected in Pandas DataFrame in the last 3 data points")The crossunder function is used to calculate the crossunder between two columns in a DataFrame. It returns a new DataFrame with an additional column that contains the crossunder values. A crossunder occurs when the first column crosses below the second column. This can happen in two ways, a strict crossunder or a non-strict crossunder. In a strict crossunder, the first column must cross below the second column. In a non-strict crossunder, the first column must cross below the second column, but the values can be equal.
def crossunder(
data: Union[PdDataFrame, PlDataFrame],
first_column: str,
second_column: str,
result_column="crossunder",
data_points: int = None,
strict: bool = True,
) -> Union[PdDataFrame, PlDataFrame]:Example
from polars import DataFrame as plDataFrame
from pandas import DataFrame as pdDataFrame
from investing_algorithm_framework import CSVOHLCVMarketDataSource
from pyindicators import crossunder, 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 and crossunder for Polars DataFrame
pl_df = ema(pl_df, source_column="Close", period=200, result_column="EMA_200")
pl_df = ema(pl_df, source_column="Close", period=50, result_column="EMA_50")
pl_df = crossunder(
pl_df,
first_column="EMA_50",
second_column="EMA_200",
result_column="Crossunder_EMA"
)
pl_df.show(10)
# Calculate EMA and crossunder for Pandas DataFrame
pd_df = ema(pd_df, source_column="Close", period=200, result_column="EMA_200")
pd_df = ema(pd_df, source_column="Close", period=50, result_column="EMA_50")
pd_df = crossunder(
pd_df,
first_column="EMA_50",
second_column="EMA_200",
result_column="Crossunder_EMA"
)
pd_df.tail(10)
The is_crossunder function is used to determine if a crossunder occurred in the last N data points. It returns a boolean value indicating if a crossunder occurred in the last N data points. The function can be used to check for crossunders in a DataFrame that was previously calculated using the crossunder function.
def is_crossunder(
data: Union[PdDataFrame, PlDataFrame],
first_column: str = None,
second_column: str = None,
crossunder_column: str = None,
number_of_data_points: int = None,
strict: bool = True,
) -> bool:Example
from polars import DataFrame as plDataFrame
from pandas import DataFrame as pdDataFrame
from investing_algorithm_framework import CSVOHLCVMarketDataSource
from pyindicators import crossunder, ema, is_crossunder
# 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 and crossunders for Polars DataFrame
pl_df = ema(pl_df, source_column="Close", period=200, result_column="EMA_200")
pl_df = ema(pl_df, source_column="Close", period=50, result_column="EMA_50")
pl_df = crossunder(
pl_df,
first_column="EMA_50",
second_column="EMA_200",
result_column="Crossunder_EMA"
)
# If you want the function to calculate the crossunders in the function
if is_crossunder(
pl_df, first_column="EMA_50", second_column="EMA_200", data_points=3
):
print("Crossunder detected in Pandas DataFrame in the last 3 data points")
# If you want to use the result of a previous crossunders calculation
if is_crossunder(pl_df, crossunder_column="Crossunder_EMA", data_points=3):
print("Crossunder detected in Pandas DataFrame in the last 3 data points")
# Calculate EMA and crossunders for Pandas DataFrame
pd_df = ema(pd_df, source_column="Close", period=200, result_column="EMA_200")
pd_df = ema(pd_df, source_column="Close", period=50, result_column="EMA_50")
# If you want the function to calculate the crossunders in the function
if is_crossunder(
pd_df, first_column="EMA_50", second_column="EMA_200", data_points=3
):
print("Crossunders detected in Pandas DataFrame in the last 3 data points")
# If you want to use the result of a previous crossover calculation
if is_crossunder(pd_df, crossover_column="Crossunder_EMA", data_points=3):
print("Crossunder detected in Pandas DataFrame in the last 3 data points")