How to Calculate Vectorized Supertrend & Intraday VWAP in Python (Plus 125+ Free Quant Scripts)

ayushman4you-ctrl — Wed, 04 Nov 2026 05:10:52 +0000

When backtesting algorithmic trading strategies or building financial models in Python, the biggest bottleneck is almost always slow, iterative for loops over Pandas DataFrames.

Whether you are running an intraday strategy on index options or scanning hundreds of equities, vectorizing your technical indicators using pandas and numpy can speed up execution by 50x to 100x.

To save developers and quants from rewriting boilerplate code, I built PyQuant Library — a free, open-access collection of 125+ production-ready, vectorized Python scripts with an embedded browser sandbox.

Here is a quick look at how we handle two classic institutional indicators—Intraday VWAP (with daily session resets) and Supertrend—using clean, vectorized Python.

1. Intraday VWAP with Automatic Daily Session Reset

Standard cumulative VWAP calculations break on multi-day intraday data (like 5-minute candles) unless you group by the trading session date. Here is the clean vectorized way to do it:


python
import pandas as pd
import numpy as np

def calculate_intraday_vwap(df: pd.DataFrame) -> pd.DataFrame:
    """
    Expects a DataFrame with DatetimeIndex and 'High', 'Low', 'Close', 'Volume' columns.
    Resets calculation automatically at the start of each new trading day.
    """
    df = df.copy()

    # Calculate Typical Price
    df['Typical_Price'] = (df['High'] + df['Low'] + df['Close']) / 3
    df['TP_Volume'] = df['Typical_Price'] * df['Volume']

    # Group by trading date for session reset
    session_date = df.index.date
    df['Cum_TP_Vol'] = df.groupby(session_date)['TP_Volume'].cumsum()
    df['Cum_Vol'] = df.groupby(session_date)['Volume'].cumsum()

    df['VWAP'] = df['Cum_TP_Vol'] / np.where(df['Cum_Vol'] == 0, np.nan, df['Cum_Vol'])

    return df.drop(columns=['Typical_Price', 'TP_Volume', 'Cum_TP_Vol', 'Cum_Vol'])

Building a Production-Ready Financial & Quant Library in Python: Lessons from 100+ Scripts

ayushman4you-ctrl — Tue, 03 Nov 2026 04:52:00 +0000

When building software for quantitative finance, banking analytics, or algorithmic trading, there's often a temptation to over-engineer. We pull in massive, heavy frameworks when all we really need is clean, modular, and dependency-light Python code.

Over the past few months, I set out to solve this exact problem by building and curating a comprehensive repository of 100+ standalone, copy-paste ready Python scripts spanning core banking, credit risk scoring, quantitative risk metrics, and technical indicators.

🌐 Live Library: PyQuant Library

📦 GitHub Master Index: Awesome Python Quant Scripts

Why Focus on Standalone Scripts?

Most quants, developers, and financial analysts don't want a monolithic framework that takes hours to configure. They need modular code that can be dropped directly into Jupyter Notebooks, automated trading bots, or internal banking dashboards.

By sticking to standard, robust libraries (pandas, numpy, scipy, yfinance), these scripts remain fast, transparent, and easy to audit.

Core Modules Covered in the Library

1. Core Banking & Credit Risk Modeling

In traditional banking and lending, having quick access to core calculation scripts saves hours of spreadsheet chaos. The library includes:

Credit Risk Scoring & Probability of Default (PD) Models (Basel-aligned expected loss frameworks)
Corporate Credit Appraisal & Financial Ratio Calculators (DSCR, ICR, Leverage)
Loan Amortization & Prepayment Cashflow Engines
NPA & Delinquency Bucket Migration (DPD) Trackers

2. Quantitative Finance & Portfolio Risk

For portfolio managers and quants looking to measure tail risk and optimize allocations:

Markowitz Portfolio Optimization & Efficient Frontier
Value at Risk (VaR) & Expected Shortfall
Monte Carlo Stock Price & Portfolio Simulations
Option Pricing & Greeks (Black-Scholes bisection solvers)

3. Technical Indicators & Algo-Trading Signals

Clean calculation scripts for trend, momentum, and volatility:

Supertrend & ATR Trailing Stop Confluence
200/50 EMA Trend Filters & Crossovers
Intraday VWAP & Anchor VWAP Calculators
Bollinger Band Squeeze Breakout Scanners

Check Out the Complete Collection

Every single script in the library is 100% free to read, copy, and adapt for your own projects.

Browse all 101 pages live: pyquant-library.netlify.app
Explore code & star the repo: GitHub Repository

If you're building financial tools or trading automation in Python, check it out and let me know what scripts you'd like to see added next!

DEV Community: ayushman4you-ctrl