Algo trading used to be largely an institutional-only domain. But with growing demand from retail users, the regulatory landscape has evolved. In 2025, Securities and Exchange Board of India (SEBI) introduced a formal framework to regulate algorithmic trading by retail investors a significant shift toward transparency, accountability, and safer participation.

Trend following as a trading framework relies less on prediction and more on disciplined execution. The strategy works by identifying established market direction and holding positions as long as that direction remains intact. But trend followers don’t succeed because they find trends. They succeed because they size positions systematically

Trend identification is the base layer of rule-based trading. If you misread the trend, every downstream decision entries, exits, position sizing, and risk control becomes unreliable. For retail algo traders, the problem isn’t a lack of indicators; it’s figuring out which ones reliably capture directional behaviour without introducing

Moving average crossover systems remain one of the simplest ways to test trend following behaviour, but they are often misunderstood. A crossover is not a prediction tool. It is a rule-based mechanism for identifying when price transitions from one regime to another. When you test them properly, they reveal how

Backtesting a trend following strategy is not just about throwing a moving average on a chart and checking how it looks. A proper test has a sequence. You define what you are trying to trade, write rules that a machine can understand, run them on clean data, and then stress

Comparing two trading strategies is not as simple as checking which one made more money. Raw returns hide instability, hidden leverage, drawdown depth, execution friction, and volatility sensitivity. A strategy that shows lower returns on paper may actually be far superior once risk is factored in. This is why professional

A buy-and-hold portfolio works well when markets rise steadily and volatility stays contained. Trend-following works well when markets break into strong directional phases or slide into prolonged downturns. Most retail traders discover that each approach has weaknesses on its own, but together they create a portfolio that grows consistently while

Automated trading systems follow instructions exactly as written. They do not hesitate, they do not slow down when volatility rises, and they do not override a bad decision. This makes them powerful, but also dangerous when risk management is incomplete. A single missing rule, a max-loss stop, an exposure cap,

Comparing Trend Following and Mean Reversion Strategy PerformanceTrend following and mean reversion sit at opposite ends of the quantitative trading spectrum. One assumes markets persist in the same direction; the other assumes markets snap back toward an average. Both approaches have long track records globally, from CTAs running billions in

Reliable market data is the backbone of any automated trading system. The fastest code, the most refined strategy, and the strongest risk rules still fail if the data feeding the system is slow, noisy, incomplete, or out of sync with the exchange. For Indian retail algo traders, the last few

Not every market behaves the same way, and not every timeframe rewards the same trading style. A strategy that performs well on Nifty futures may fail on a banking stock; a mean-reversion setup that works beautifully on 5-minute candles may break down when tested on daily data. This is why

Algo trading results often look clean on spreadsheets and backtests. But the moment a strategy goes live, the numbers shift. The reason is simple: real markets never give you perfect fills. Brokerage, exchange charges, STT, spreads, and slippage all erode performance. For high-turnover strategies, these costs can erase the entire

Understanding how different trading strategies behave under stress is one of the most important parts of building a reliable algorithmic portfolio. Every strategy generates returns in its own way, but more importantly, every strategy bleeds in a different way. Drawdowns, volatility, and tail-risk signatures vary widely across trend-following, mean-reversion, option

Trend following and mean reversion sit on opposite sides of market behaviour. One profits from continuation, the other from reversal. One thrives in directional markets, the other in noisy ranges. When combined correctly, they create one of the most stable multi-strategy models available to retail algo traders. The goal is

Comparing a trend-following strategy with a mean-reversion strategy requires more than looking at returns. These two styles behave differently, trade at different frequencies, and react to volatility in opposite ways. To evaluate them fairly, you must use metrics that capture risk, stability, execution sensitivity, and regime dependency. A simple ROI

Algorithmic trading relies on rules, data, and repeatable logic rather than discretion. While every strategy may look different in code, most of them fall into a few major categories that describe how they respond to price behaviour. Understanding these categories helps traders decide what type of logic fits their risk

Getting started with algorithmic trading is far more structured today than it was just a few years ago. Indian markets now operate under clearer SEBI and exchange standards, brokers provide mature API infrastructure, and retail traders have access to tools that were once reserved for institutions. But before any automation

Algorithmic trading has grown faster in India than almost anywhere else. Retail traders now use broker APIs, condition-based rule engines, and backtesting tools in the same way that institutions did a decade ago. But this rapid shift has also created confusion. Many traders aren’t sure what is allowed, what

Algorithmic trading gives you discipline and speed, but it also exposes you to risks that don’t exist in manual trading. When a human makes a mistake, it happens once. When an algo makes a mistake, it keeps repeating it until you stop it. That’s why retail traders need

Options traders were some of the earliest retail participants to adopt automation in India. Options require speed, rules, and precision. They move fast, react sharply to volatility, and demand disciplined exits, all of which make them good candidates for algorithmic execution. But automating options trading is not the same as

For any algorithmic trading workflow, historical data is not an optional input; it's the base layer for every decision. Using incomplete data may make a backtest look convincing, but live results will almost certainly drift.