Algorithmic trading, from scratch.
Every strategy starts the same way — someone notices what looks like a pattern. The real question is never whether the story sounds good; it's whether it survives real data, real costs, and the cold test of chance. This guide takes you from that first hunch to thinking like a quant, in plain language.
It starts with a hunch
Picture this. You watch the market for a few weeks and spot something: stocks that jump 5% at the opening bell often keep drifting higher through the day. Feels like an edge. So you start trading it by gut — some days you buy, some days you hesitate — and after a month you're up a little. Was that skill, or just a good month? Trading by gut can never answer that. It's tangled up with emotion, memory plays tricks, and you can't re-run the experiment. That's the wall discretionary trading hits.
What makes it algorithmic
Now write the hunch as an exact rule: every day, buy the five biggest gap-ups at 9:15 and sell them all by 3:30. No judgment, no exceptions. A computer can follow it — which means it can be tested on years of history in seconds and run the same way every single day. That's algorithmic (or systematic) trading: strategies precise enough to be code. The magic isn't the automation — it's that a rule you can write down is a rule you can test honestly, without fooling yourself. That's the only kind of strategy abtrade works with.
The simplest trade: owning a slice of a company
Start with the basics. A share is a small piece of a company — buy it low, sell it higher, keep the difference. Betting a stock will rise is going long. You can also profit when a stock falls: borrow shares, sell them now, buy them back cheaper later, return them, and pocket the gap. That's going short. Long and short are the two primary colours; everything more exotic mixes from here.
Derivatives: contracts built on top of the market
A derivative is a contract whose value comes from — derives from — something else, usually a stock or an index. Two reasons they exist: leverage (control a large position with little cash) and hedging (insurance against a move you fear). The simplest is a future: an agreement to buy or sell the underlying at a set price on a set date. Long a Nifty future and Nifty rises, you gain; it falls, you lose — both ways, amplified. Powerful and unforgiving: leverage multiplies losses exactly as fast as gains.
Options: the right, not the obligation
Options are where it gets interesting — and where most beginners get hurt. An option is the right, but not the obligation, to buy or sell the underlying at a fixed price (the strike) before a deadline (expiry). For that right the buyer pays the seller a fee — the premium. Two kinds (call / put) × two sides (buy / sell) give four basic positions. Here's each, with the same ₹1400 stock so you can feel the difference:
Where beginners fool themselves
Knowing the instruments is the easy half. The hard half is not lying to yourself about whether your rule actually works. These are the traps that separate a real edge from an expensive illusion:
The one rule that matters most
A backtest tells you what would have happened. It never tells you what will. Treat every gorgeous backtest as a suspect, not a promise — and ask first: is this edge real, or did I get lucky on a short stretch of history?
That's why abtrade leads with sample size, significance, and out-of-sample tracking instead of a big green number. The number is easy. The honesty is the hard, valuable part.
How you'll work here
- 1Describe an idea in plain English — e.g. 'buy the 5 biggest gap-ups at the open, exit by close.'
- 2Run it on real NSE + BSE data. abtrade checks it for lookahead bias automatically.
- 3Read the honest scorecard — skill vs. market, t-stat, drawdown, and realistic costs — with an AI risk read.
- 4Improve it, version it, then deploy it to be forward-tracked on live data from that day on.