pyfin-trading 1.0.0

Making Finance easy in Python!

Package for analyzing and visualizing financial time-series data

Developed by Poonam Deshmukh (c) 2023.

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Requirements

pyfin_trading would need the following libraries to be installed

• pandas
• numpy
• yfinance
• datetime

Installation

You can install it via pip:

pip install pyfin-trading

Modules

• stock_data
• stats
• indicators

1. stock_data

This module majorly helps with getting stock data and can be accessed as follows:

from pyfin_trading import stock_data as sd

df = sd.get_data(ticker, start_date, end_date, interval)

- Input ticker symbol along with its corresponding exchange.
- Format for start_date & end_date must be "%Y-%m-%d"
- By default end_date is set to today
- Interval can take values like "1m", "2m", "5m", "15m", "30m", "60m", "90m", "1h", "1d", "5d", "1wk", "1mo", "3mo"
  where m = minute, h = hour, d = day, wk = week, mo = month
- By default interval is set to "1d"

Example

df = sd.get_data("ITC.NS", "2023-11-06")

This will return ITC's data from NSE spanning from 6th Nov 2023 to present day with interval set to daily

2. stats

It helps with calculating returns as well as some financial ratios:

from pyfin_trading import stats as st

returns = st.calculate_returns(df) # Calculates Daily & Cumulative Returns
st.beta(df, market, interval)
st.volatility(df, trading_days = 252) # Calculates SD & Annualized Volatility
st.sharpe(df, risk, trading_days = 252) # Sharpe Ratio
st.sortino(df, risk, trading_days = 252) # Sortino Ratio
st.alpha(df, market, interval, beta, risk)
st.treynor(df, beta, risk, trading_days = 252) # Treynor Ratio
st.info(df, market, interval, risk, trading_days = 252) # Information Ratio

- By default Nifty 50 is used as a proxy for the market i.e. "^NSEI"

3. indicators

For calculating technical indicators:

from pyfin_trading import indicators as pt

pt.sma(df, sma_period = 15) 
pt.ema(df, ema_period = 15, adjust = False)
pt.rsi(df, rsi_period = 14)
pt.connors_rsi(df, rsi_period = 14, updown_length = 2)
pt.stoch(df, k = 14, d = 3) # Stochastic Oscillator
pt.bollinger_bands(df, sma_period = 20, std_dev = 2) # Bollinger Bands
pt.aroon(df, aroon_period = 25) # Aroon Oscillator
pt.macd(df, short_period = 12, long_period = 26, signal = 9, adjust = False) # Moving Average Convergence Divergence
pt.ichimoku_cloud(df, conversion = 9, base = 26, lead_b = 52) # Ichimoku Cloud

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