statistical-stocks-ta 0.1.2

A python package for Statistical Stocks TA (computing patterns, ma, indicators) and data fetching.

Statistical Stocks TA

The statistical-stocks-ta provides a comprehensive suite of tools for technical analysis and visualization of financial time series data. This package supports the calculation of various technical indicators, pattern detection, plotting analysis, and more.

Installation

To install the package, you can use pip:

pip install statistical-stocks-ta

Usage

Fetching Data

First, fetch the OHLCV data using the fetch_data function:

from statistical_ta import fetch_data

data = fetch_data(symbol='SOL/USDT', timeframe='1h', limit=300)

Functions

analyze_candles

Analyzes the given OHLCV data using various technical indicators and patterns.

import pandas as pd
from statistical_ta import analyze_candles

candles = pd.read_csv('data.csv')
candles = analyze_candles(candles)

Parameters:

• candles (DataFrame): DataFrame containing the OHLCV data.
• calc_bollinger_bands (bool, optional): Whether to calculate Bollinger Bands. Default is True.
• bb_settings (dict, optional): Settings for Bollinger Bands calculation. Default is {"window": 20, "std_dev": 2}.
• calc_moving_averages (bool, optional): Whether to calculate moving averages. Default is True.
• ma_settings (dict, optional): Settings for moving averages calculation. Default is {"short_window": 20, "long_window": 50}.
• calc_rsi (bool, optional): Whether to calculate RSI. Default is True.
• rsi_settings (dict, optional): Settings for RSI calculation. Default is {"window": 14}.
• calc_macd (bool, optional): Whether to calculate MACD. Default is True.
• macd_settings (dict, optional): Settings for MACD calculation. Default is {}.
• detect_patterns (bool, optional): Whether to detect patterns. Default is True.
• pattern_settings (dict, optional): Settings for pattern detection. Default is {}.
• plot (bool, optional): Whether to plot the analysis. Default is True.
• plot_settings (dict, optional): Settings for plotting. Default is {}.

Response Data Structure/Type

{
  "index": {
    "type": "datetime64[ns]",
    "description": "Index representing the datetime of each observation"
  },
  "columns": {
    "open": {
      "type": "float64",
      "description": "Opening price of the candle"
    },
    "high": {
      "type": "float64",
      "description": "Highest price of the candle"
    },
    "low": {
      "type": "float64",
      "description": "Lowest price of the candle"
    },
    "close": {
      "type": "float64",
      "description": "Closing price of the candle"
    },
    "volume": {
      "type": "float64",
      "description": "Volume traded during the candle"
    },
    "FVG": {
      "type": "int64",
      "description": "Fair Value Gap indicator, 1 for up, -1 for down, 0 otherwise"
    },
    "Swings": {
      "type": "int64",
      "description": "Swing indicator, 1 for high, -1 for low, 0 otherwise"
    },
    "BOS": {
      "type": "int64",
      "description": "Break of Structure indicator, 1 for up, -1 for down, 0 otherwise"
    },
    "OB": {
      "type": "int64",
      "description": "Order Blocks indicator, positive values indicate presence"
    },
    "Liquidity": {
      "type": "int64",
      "description": "Liquidity indicator, 1 for up, -1 for down, 0 otherwise"
    },
    "BB_high": {
      "type": "float64",
      "description": "Upper Bollinger Band value"
    },
    "BB_low": {
      "type": "float64",
      "description": "Lower Bollinger Band value"
    },
    "RSI": {
      "type": "float64",
      "description": "Relative Strength Index value"
    },
    "SMA_Short": {
      "type": "float64",
      "description": "Short-term Simple Moving Average"
    },
    "SMA_Long": {
      "type": "float64",
      "description": "Long-term Simple Moving Average"
    },
    "EMA_Short": {
      "type": "float64",
      "description": "Short-term Exponential Moving Average"
    },
    "EMA_Long": {
      "type": "float64",
      "description": "Long-term Exponential Moving Average"
    },
    "MACD": {
      "type": "float64",
      "description": "MACD value"
    },
    "MACD_Signal": {
      "type": "float64",
      "description": "MACD signal line value"
    },
    "Support_Q": {
      "type": "float64",
      "description": "Support level value"
    },
    "Resistance_Q": {
      "type": "float64",
      "description": "Resistance level value"
    },
    "Bullish_Divergence": {
      "type": "int64",
      "description": "Bullish Divergence pattern indicator"
    },
    "Bearish_Divergence": {
      "type": "int64",
      "description": "Bearish Divergence pattern indicator"
    },
    "Bullish_Reversal": {
      "type": "int64",
      "description": "Bullish Reversal pattern indicator"
    },
    "Bearish_Reversal": {
      "type": "int64",
      "description": "Bearish Reversal pattern indicator"
    },
    "Doji": {
      "type": "int64",
      "description": "Doji pattern indicator"
    },
    "Bearish_Engulfing": {
      "type": "int64",
      "description": "Bearish Engulfing pattern indicator"
    },
    "Bullish_Engulfing": {
      "type": "int64",
      "description": "Bullish Engulfing pattern indicator"
    },
    "Flag": {
      "type": "int64",
      "description": "Flag pattern indicator"
    },
    "Triangle": {
      "type": "int64",
      "description": "Triangle pattern indicator"
    },
    "Channel_Up": {
      "type": "int64",
      "description": "Channel Up pattern indicator"
    },
    "Channel_Down": {
      "type": "int64",
      "description": "Channel Down pattern indicator"
    }
  }
}

calculate_bollinger_bands

Calculates Bollinger Bands for the given data.

import pandas as pd
from indicators import calculate_bollinger_bands

candles = pd.read_csv('data.csv')
candles = calculate_bollinger_bands(candles)

Parameters:

• candles (DataFrame): DataFrame containing the OHLCV data.
• window (int, optional): The number of periods to use for the moving average. Default is 20.
• std_dev (int, optional): The number of standard deviations to use for the bands. Default is 2.

calculate_moving_averages

Calculates simple and exponential moving averages for the given data.

import pandas as pd
from indicators import calculate_moving_averages

candles = pd.read_csv('data.csv')
candles = calculate_moving_averages(candles)

Parameters:

• candles (DataFrame): DataFrame containing the OHLCV data.
• short_window (int, optional): The number of periods for the short moving average. Default is 20.
• long_window (int, optional): The number of periods for the long moving average. Default is 50.

calculate_rsi

Calculates the Relative Strength Index (RSI) for the given data.

import pandas as pd
from indicators import calculate_rsi

candles = pd.read_csv('data.csv')
candles = calculate_rsi(candles)

Parameters:

• candles (DataFrame): DataFrame containing the OHLCV data.
• window (int, optional): The number of periods to use for the RSI calculation. Default is 14.

calculate_macd

Calculates the Moving Average Convergence Divergence (MACD) for the given data.

import pandas as pd
from indicators import calculate_macd

candles = pd.read_csv('data.csv')
candles = calculate_macd(candles)

Parameters:

• candles (DataFrame): DataFrame containing the OHLCV data.

detect_flags

Detects flag patterns in the given data.

import pandas as pd
from patterns import detect_flags

candles = pd.read_csv('data.csv')
candles = detect_flags(candles)

Parameters:

• candles (DataFrame): DataFrame containing the OHLCV data.
• trend_window (int, optional): The number of periods to define the prior trend. Default is 20.
• flag_window (int, optional): The number of periods for the flag pattern. Default is 5.
• breakout_threshold (float, optional): The threshold for breakout detection. Default is 0.1.

identify_patterns

Identifies various candlestick patterns in the given data.

import pandas as pd
from patterns import identify_patterns

candles = pd.read_csv('data.csv')
candles = identify_patterns(candles)

Parameters:

• candles (DataFrame): DataFrame containing the OHLCV data.
• doji_threshold (float, optional): The threshold for detecting Doji candles. Default is 0.1.
• buffer_factor (float, optional): The factor for adjusting buffer in channel detection. Default is 0.5.
• rolling_window (int, optional): The window size for rolling calculations. Default is 10.
• channel_window_range (tuple, optional): The range for channel window size. Default is (10, 15).
• flag_sensitivity (float, optional): Sensitivity for flag detection. Default is 0.8.

plot_analysis

Plots the analysis of the given data using mplfinance.

import pandas as pd
from plotting import plot_analysis

candles = pd.read_csv('data.csv')
plot_analysis(candles)

Parameters:

• candles (DataFrame): DataFrame containing the OHLCV data.

quantile_regression_support_resistance

Calculates support and resistance levels using quantile regression.

import pandas as pd
from support_resistance import quantile_regression_support_resistance

candles = pd.read_csv('data.csv')
candles = quantile_regression_support_resistance(candles)

Parameters:

• candles (DataFrame): DataFrame containing the OHLCV data.
• quantiles (list, optional): List of quantiles to calculate. Default is [0.10, 0.90].