Historical Financial Data Evaluation

Cleaning out my old drive led to rediscovering some old projects, and I thought, why not share them? Here's the first one: a Python implementation of Value at Risk (VaR) & Conditional Value at Risk (CVaR)! This project walks through calculating portfolio risks using three methods: 1️⃣ Historical VaR – No assumptions about return distributions, straight from historical data. 2️⃣ Parametric VaR – Leveraging assumptions like normal and t-distributions to model risk. 3️⃣ Monte Carlo Simulations – Simulating portfolio dynamics for robust risk estimation. It also includes functions for portfolio performance evaluation and comparison of results across these methods. Whether you're into risk management or just curious about how quantitative finance works under the hood, this repo is a great starting point. I’d love feedback from anyone who takes a look or ideas for further improvement. Check it out here: https://lnkd.in/gVgbDkT7

Day 2: Calculating VaR by using the Historical Simulation Method Using historical simulation for Value at Risk (VaR) on a multi-asset portfolio involves evaluating historical price changes for all assets in the portfolio, applying those changes to the current portfolio values, and determining the potential losses based on the confidence level. Here's a step-by-step explanation with a formula and example: Steps for Historical Simulation VaR on a Multi-Asset Portfolio 1. Collect Historical Data Gather historical price or return data for all assets in the portfolio over a specific period (e.g., 1 year of daily data). 2. Calculate Portfolio Returns for Each Day Use the historical percentage changes in the prices of individual assets to calculate the portfolio’s total return on each historical day. 3. Apply Historical Returns to Current Portfolio Value For each historical day, calculate the hypothetical portfolio value 4. Calculate Portfolio Losses Determine the loss relative to the current portfolio value 5. Rank Losses Rank all the historical losses from smallest to largest. 6. Determine the VaR Identify the loss at the desired confidence level (e.g., the 5th percentile for 95% confidence). This methodology can be extended to larger portfolios with more assets and longer time periods using similar steps, often implemented in tools like Python or R for automation. Let me know if you'd like help with coding or deeper analysis! #Quant #Risk #Riskmanagement #VaR #Marketrisk #Financialmathematics #Finance #Derivatives #Equity #Bonds #Fixedincome

Day 4 of 7: Unlocking Quant Knowledge – Historical Simulation Method for VaR Hey LinkedIn community! I’m thrilled to continue our 7-day deep dive into quantitative finance, focusing this week on Value at Risk (VaR). Let’s recap: Day 2 covered the Variance-Covariance Method, using stats (normal returns, volatility) to estimate VaR, like a formulaic picnic rain prediction. Day 3 explored the Monte Carlo Method, simulating random market scenarios, like forecasting storms for your financial ship. Today, we’ll tackle the Historical Simulation Method—it skips assumptions, using actual past data for a reality-based risk view. What’s the Historical Simulation Method for VaR? Imagine using 10 years of weather data to predict picnic rain risk, relying on real rainy days. This method uses past stock price data—without assuming normal returns—to estimate VaR. You take historical daily returns for your portfolio’s stocks over 1-3 years, apply them to today’s value, calculate hypothetical profits/losses, and sort them to find VaR at a confidence level like 95% over a time (e.g., one day). A Real-Life Example for Everyone Consider a $1 million equity portfolio in Feb 2025: 40% Tesla, 35% Apple, 25% Microsoft. Using daily returns from the past 3 years (750 trading days), you calculate gains/losses if those days happened today. Sorting the 750 results, the 95th percentile loss (worst 5%) is $40,000 for a 1-day horizon—a 5% chance of losing more than $40,000, based on history, helping you prepare for market dips. Why Use Historical Simulation for Equity Portfolios? It captures real events—like the 2020 tech crash—without assuming normal returns, ideal for volatile stocks. It reflects actual correlations (e.g., Tesla and Apple moving together in downturns). But, it assumes the past predicts the future, missing new risks (e.g., a new crisis), and needs enough data—less than a year may skew results. Real-World Applications Portfolio managers assess risk with real market history, risk analysts use it for regulatory reporting (e.g., Basel III), and hedge funds stress-test portfolios against past crises. Fun Fact Historical simulation gained prominence post-2008 crisis, as its real data focus helped firms capture turbulent market risks, though it couldn’t predict the crisis’s scale. Over the next 3 days, I’ll explore why VaR matters, its limits, and more. Follow along, share your thoughts, and let’s master VaR together—how does historical data shape your risk strategy? #QuantFinance #RiskManagement #ValueAtRisk #HistoricalSimulation #EquityPortfolio #FinancialModeling #Finance #Investment #MarketRisk #RiskAnalysis #PortfolioManagement #FinancialRisk #DataDrivenFinance #InvestmentStrategy #QuantitativeAnalysis #RiskAssessment #FinanceCareers #StockMarket #Trading #FinTech

The Process of Backtesting Trading Strategies Backtesting is a critical process in developing and validating trading strategies. It involves simulating the strategy on historical data to evaluate its performance. Here's a detailed guide to backtesting, including data preparation, model selection, and performance evaluation. Step 1: Data Preparation 1. Data Collection: Gather historical price data, trading volumes, and other relevant market data. Sources include financial databases, APIs like Yahoo Finance or Alpha Vantage, and brokerage platforms. 2. Data Cleaning: Ensure data accuracy by removing outliers, handling missing values, and correcting any inconsistencies. 3. Data Formatting: Structure the data in a way that aligns with your strategy requirements, typically in a time-series format with columns for Adj Close. Step 2: Model Selection 1. Define the Strategy: Clearly define the trading rules and parameters. For example, a moving average crossover strategy. 2. Implement the Strategy: Write the code to execute the trading rules on historical data. Step 3: Performance Evaluation 1. Calculate Returns: Compute the strategy's returns based on the generated signals. 2. Key Metrics: Evaluate the strategy using metrics like Sharpe ratio, maximum drawdown, and cumulative return. 3. Visualization: Plot performance graphs to visualize the strategy's effectiveness. Example of a Backtested Strategy Strategy: Moving Average Crossover 1. Data Preparation:   - Historical data for SBIN from 2020 to 2023.   - Cleaned and formatted in a time-series format. 2. Model Selection:   - Short-term moving average window: 40 days.   - Long-term moving average window: 100 days.   - Buy signal: Short-term MA crosses above long-term MA.   - Sell signal: Short-term MA crosses below long-term MA. 3. Performance Evaluation:   - Calculate daily returns and strategy returns.   - Evaluate using Sharpe ratio and maximum drawdown.   - Visualize cumulative returns of the strategy against market returns. Results: - Sharpe Ratio: Indicates risk-adjusted return. A higher Sharpe ratio means better risk-adjusted performance. - Max Drawdown: Measures the maximum observed loss from a peak to a trough. Lower values indicate better performance. Conclusion Backtesting trading strategies involves thorough data preparation, precise model implementation, and rigorous performance evaluation. By simulating the strategy on historical data and analyzing key metrics, traders can gain insights into the strategy's effectiveness and potential risks before deploying it in live markets. Have you backtested any trading strategies? Share your experiences and insights in the comments below! #Backtesting #TradingStrategies #DataAnalysis #ModelSelection #PerformanceEvaluation #AlgorithmicTrading #Finance #Investing #QuantitativeFinance #StockMarket