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Practical, jargon-free guides on paper trading, charting, risk management and the markets of Asia. Read, then practise it free on StockYatra.
Master StockYatra Pro: Trade NEPSE with up to 5X leverage, protect gains with trailing stops, backtest 113 Pro Chart strategies, and consult Lumi AI. Plans from Rs. 199/month.
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Learn how to use StockYatra Pro Chart Lab to backtest 113 trend, momentum, and mean-reversion strategies. Auto-tune parameters and consult Lumi AI 50 times daily.
The Jane Street–SEBI case explained simply: what SEBI's July 2025 interim order alleged about Bank Nifty, the escrow deposit, the appeal, and what retail traders can learn.
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.
How trading automation works: alerts, webhooks, broker APIs, no-code bots, risk limits and kill switches, the levels of automation, rules in India, and scam red flags.
What quantitative trading is, how quants turn ideas into tested strategies, common factors like momentum and value, the risks of overfitting, and how beginners can start.
What a market maker does, how the bid-ask spread and order book work, how market makers earn and lose, and how spreads affect your own trading costs.
The May 6, 2010 Flash Crash explained: the sell algorithm, how high-frequency traders and liquidity behaved, why prices hit pennies, and the rules that changed afterwards.
The NSE co-location case in plain words: the tick-by-tick feed, the allegations about early access, SEBI's 2019 orders, the 2023 SAT ruling and what traders can learn.
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.
What "black box" alert platforms flag in US markets: unusual volume, options flow, dark pool prints and AI scores, how to read them, and common beginner mistakes.
What AI and machine learning can and cannot do in markets: why short-term prices are mostly noise, where AI helps, and how to use AI tools sensibly.
Why beginners should learn white-box (transparent) strategies first, and how to rebuild a typical "AI signal" as plain rules and backtest it, step by step.
"Secret AI" bots promising daily returns are a fast-growing scam. Learn how they work, 10 red flags, what regulators warn, and what to do if you have paid.
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.