Everything we've written about algo trading. Read, then practise it free on StockYatra.
A beginner Python backtest with pandas: load candles, add moving averages, avoid look-ahead bias, include fees, and compare with buy and hold. Code explained line by line.
Compare high-frequency, algorithmic and manual trading: who decides, speed, holding time, costs, skills needed, and which one a beginner should actually learn first.
High-frequency trading explained: latency, co-location, market making and arbitrage, who does HFT, the risks and rules, and what it means for retail traders.
A practical algo trading roadmap: market basics, writing rules, backtesting, paper trading, Pine Script then Python, and automating within the rules.
Algorithmic vs manual trading compared: discipline, speed, testing, adaptability, costs and risks, plus a hybrid approach that suits most beginners.
Trend following, mean reversion, breakout, momentum, pairs, VWAP/TWAP and market making: how each algo strategy works, when it struggles, how to test it.
Step-by-step backtesting: out-of-sample and walk-forward tests, overfitting, look-ahead and survivorship bias, fees, and a worked example with numbers.
Can you run trading bots on NEPSE? What SEBON directed in August 2026, why TMS has no trading API, the risks, and what Nepali traders can safely do.
SEBI's retail algo framework in plain English: algo IDs, static IP and 2FA, white vs black box, your own algo, key dates and what to ask your broker.
Algorithmic trading explained: how rules become orders, the main types of algos, the rules in India, Nepal and the US, the risks, and how to practise free.
SEBI split retail algos into white box and black box. What each means, the Research Analyst rule, algo IDs, static IPs, key dates and what to ask your broker.
Black box AI trading explained: how hidden-logic algorithms turn data into buy and sell signals, where they are used, their limits, and how to judge one.