Tag
#backtesting
30 articles
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Average Trade Profit, Explained: Per-Trade Economics and Why Averages Mislead
Average trade profit is total net result divided by number of trades. It is the per-trade economics of a strategy, and it is fragile whenever a few outcomes dominate the total.
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Best Backtesting Platforms in India: A Fair Roundup
A neutral guide to backtesting platforms available in India, the categories they fall into, and the four things to check before you trust any backtest result.
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Quant Investing Platforms in India: A Fair Roundup
A neutral guide to quant and rule-based investing platforms in India, what each category is built for, and how to work out which one fits your job.
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Buy and Hold vs Strategy Returns: The Baseline Every Backtest Must Beat
Buy and hold return is the benchmark result you would have earned doing nothing. It is the honest baseline for any strategy, and it is a hard bar to clear.
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Common Backtesting Mistakes That Make Results Look Better Than Reality
The most common backtesting mistakes are future information leaking into past decisions, survivorship in the universe, ignored costs, and testing so many variations that something looks good by luck.
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Gross Profit and Loss, Explained: What Sits Between Gross and Net
Gross P&L is the raw result of trades before costs. Net P&L is what reaches the account. The gap is brokerage, taxes, exchange charges, slippage and financing, and it is not small.
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How to Backtest a Stock Strategy in India: An End-to-End Walkthrough
Backtesting a stock strategy in India means testing explicit rules on historical data that was actually knowable at each date, with realistic costs, a fair benchmark, and honest reporting.
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How to Read a Backtest Report
Read a backtest report in reverse order: setup and assumptions first, then risk and turnover, and the return figure last. Here is the sequence and the red flags.
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In-Sample vs Out-of-Sample Testing: The Minimum Honest Backtest Check
In-sample data is where a strategy is built and tuned. Out-of-sample data is held back and used once to judge it. Splitting the two is the least you can do to avoid fooling yourself.
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Kalpi Alternatives for Deep Fundamental Research in India
Kalpi is an India-focused platform for building, backtesting and running rule-based baskets. Here is where it fits, and a research-first alternative for professional desks.
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Liquidity Constraints in Backtesting: Why a Paper Strategy Cannot Always Be Filled
Liquidity constraints decide whether a backtested trade could actually have happened. A guide to volume caps, capacity, thin stocks and the filters that keep a test honest.
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Portfolio and Backtest Metrics, Explained: The Complete Guide
A plain-language guide to the metrics behind portfolio and strategy analysis: risk-adjusted returns, drawdown, factors, backtesting, and the market data behind them.
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Rebalancing Frequency and Backtest Results: How Often You Trade Changes What You Measure
Rebalancing frequency changes turnover, cost and signal decay all at once. A guide to why backtest results move with frequency and how to test frequency without fooling yourself.
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Rule-Based Investing Platforms Compared: The Axes That Actually Matter
A fair comparison of India's rule-based and systematic investing platforms, including Kalpi and smallcase, on data depth, point-in-time history, backtest realism, execution and audience.
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Seasonality Analysis in Indian Markets: What the Studies Show and What They Hide
Seasonality analysis measures average returns by calendar period. In Indian markets the patterns are real in the sample but fragile out of it, and the data-mining risk is severe.
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Sector Rotation Strategy in India: How Rotation Is Measured and Analysed
Sector rotation is the observation that leadership moves between sectors over time. This is how rotation is measured in Indian markets, and where the analysis breaks down.
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Slippage and Impact Cost: The Gap Between the Modelled Price and the Real One
Slippage is the difference between the price a backtest assumes and the price a trade actually gets. A guide to spread, delay and market impact, and how to model each.
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Survivorship Bias in Backtests: Why Today's Index Lies About the Past
Survivorship bias is testing a strategy on companies that survived to today. Delisted, merged, and dropped names disappear from the sample, so historical results improve for reasons unrelated to the strategy.
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Systematic vs Discretionary Investing: An Honest Comparison
Systematic investing applies a fixed rule to every case; discretionary investing judges each case on its merits. Here is what each is genuinely good and bad at.
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Transaction Costs in Backtests: Brokerage, STT, Stamp Duty and GST
Transaction costs turn a paper strategy into a real one. A guide to the categories of Indian trading cost, how to model them in a backtest, and how turnover multiplies the drag.
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Walk-Forward Analysis Explained: Rolling Re-Estimation as a Stricter Test
Walk-forward analysis fits a strategy on a past window, applies it to the next unseen window, then rolls forward and repeats. It is a harder test than a single out-of-sample split.
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What Is Maximum Drawdown? The Largest Peak to Trough Fall, Explained
Maximum drawdown is the largest fall from a portfolio's peak value to the lowest point that follows. It measures the worst loss an investor actually had to sit through.
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What Is Overfitting in Backtesting? Curve-Fitting, Parameters and How to Detect It
Overfitting in backtesting is tuning a strategy until it describes the noise in one sample of history rather than any durable pattern. It looks like a great result and behaves like a coin flip.
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What Is Profit Factor? Gross Profit Over Gross Loss, Explained
Profit factor is total gross profit from winning trades divided by total gross loss from losing trades. It shows how many rupees a strategy won per rupee it lost.
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What Is Rule-Based Investing? Rules, Discretion, and What Rules Actually Buy You
Rule-based investing means the decision is made by a written rule applied consistently, not by judgement on the day. Here is what that buys you, and what it costs.
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Why Backtest Results Don't Repeat in Live Investing
Backtest results rarely repeat live because of overfitting, regime change, crowding, and the costs a test omits. Here is what actually causes the gap and what to expect.
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Win Rate in Investing: Why a High Hit Rate Can Still Lose Money
Win rate is the share of trades or positions that ended in profit. It is easy to read and easy to misread, because it says nothing about how large the wins and losses were.
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Why Restatements Break Models and Backtests
Restatements and reclassifications quietly rewrite a company's past, so a model or backtest built on today's numbers acts on figures nobody could have seen at the time.
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Why Point-in-Time Data Matters in Research and Backtests
Point-in-time data means using the numbers that were actually knowable on a given date, not today's restated version. Skip it and your research quietly looks smarter than it was.
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Lookahead Bias, Explained: The Silent Killer of Stock Backtests
Lookahead bias is when a backtest uses information it could not have known at the time. It quietly inflates results, and point in time data is the only real fix.
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