Introduction

Imagine you are a trader, eyeing a volatile stock. Prices are swinging every few seconds. You want to buy — but should you buy now, or wait for a better price? You hesitate. You watch. And in that moment of indecision, the stock dips exactly where you wanted it, then shoots back up. Opportunity gone.

Now imagine a computer doing that for you. No hesitation. No emotion. It sees the price hit your threshold and executes the trade in milliseconds — faster than you finished reading this sentence.

In fact, by the time you read this, a computer has already made 10,000 trades. Enter - algorithmic trading.

What is Algorithmic Trading?

Let's understand trading first.

At its simplest, a trade is just an agreement. You think a stock is worth buying, someone else thinks it is time to sell — you both agree on a price, and the deal is done. That is one complete trade. The stock market is essentially millions of these agreements happening every single day, with buyers and sellers constantly negotiating prices in real time.

So what makes prices move?

Simple supply and demand. If more people want to sell than buy, prices go down. If more people want to buy than sell, prices go up.

Take SpaceX, for example. When it hit the Nasdaq on June 12th, 2026 — “the largest IPO in history” — demand was through the roof. The stock was priced at $135, opened at $150 on its first day, and within days had surged to a peak of $225. Then, as the initial excitement cooled, it corrected back down toward the $150 range. Classic supply and demand — in real time, played out over two weeks.

Traders create strategies to make the best of these price movements and maximize their returns. So how does a strategy get built? It starts with data. You look at historical price patterns, identify conditions that consistently led to profitable trades in the past, and define clear entry and exit points. Then you take it to the real market — and hope history repeats itself.

Here are the key strategies traders use:

1. Trend Following — Go with the flow

In simple terms, this is following the flock. You trade in the direction the market is already moving.

When a stock starts rising, more investors notice. More of them buy in, which pushes the price higher, which attracts even more buyers. The momentum builds on itself. Trend following is simply betting that this crowd keeps moving in the same direction — for as long as it does. The rule is straightforward: when a stock has been consistently rising, buy it and ride the wave. When it starts falling, get out. Powerful when it works. But here is the problem — what happens when there is no clear wave to ride? When prices are moving sideways, neither up nor down, the strategy gets chopped to pieces. You keep buying and selling at the wrong moments, racking up small losses each time, waiting for a trend that never arrives.

2. Mean Reversion — The rubber band

Think of a rubber band. Stretch it as far as you like — it always snaps back. The harder you stretch it, the harder it snaps. Mean reversion works exactly the same way. Gold and Silver are a perfect example — both are precious metals, both react to the same economic forces, and historically they move in lockstep. But sometimes, a sudden news event causes Gold to spike while Silver barely moves. The gap between them stretches wider than normal — just like that rubber band being pulled apart. A mean reversion trader spots that gap and bets on the snap. They buy the one that fell behind, wait for the relationship to correct itself, and take the profit when it closes. The strategy is simple: spot the gap, bet on the snap, and take the profit when it closes.

Other notable strategies include:

Arbitrage — exploiting tiny price differences for the same asset across different markets
Sentiment Analysis — making trades based on news, social media, and market mood
Market Making — sitting in the middle of every trade, simultaneously offering to buy and sell, and profiting from the small gap between the two prices

So the system sounds perfectly logical — why bring algorithms into it?

Two words: speed and scale

In the 1980s, computers began reading market prices. Trades started happening faster. Twenty years later, prices were moving in microseconds — and here is the problem. A human trader following their own strategy still has to watch the market, recognize when conditions are met, and execute the trade — all while managing fear, fatigue, distraction, and self-doubt. A coffee can only help so much.

You are already at a disadvantage, just by being a human.

An algorithm does not hesitate. It does not feel fear when markets suddenly turn volatile, or fatigue after a long trading session. It simply watches the market, checks the rules it has been given, and executes the moment conditions are met — without a single flicker of emotion.

But speed is only half the story. Algorithms also operate at a scale no human can match. A trader might keep an eye on five or ten stocks at once. An algorithm can monitor thousands simultaneously, checking each one against its strategy every second and reacting the instant an opportunity appears.

Today, depending on how you count, algorithmic trading accounts for somewhere between 60% and 75% of all US equity trading volume. It is no longer a niche corner of finance. It is the market.

So we have a strategy — a clear set of rules. Now how does a computer actually follow them?

Part A - The Code

At its heart, an algorithm is just a program running a continuous loop. It does four things, over and over, every single second — or in some cases, every millisecond:

Fetch the latest market data
Run it through the strategy rules
If the conditions are met — place the order
Monitor and manage the open positions

The loop itself is surprisingly simple. The sophistication lies in the strategy - the set of rules that tells the algorithm exactly when to trade, how much to trade and when to stay out of the market.

A real algorithm may evaluate dozens of conditions before making a decision. It might only buy if the price reaches a certain level, trading volume is unusually high, the overall market is trending upward, and it has not already exceeded its daily risk limit. Every trade is constrained by rules governing price, timing, and position size.

Think of it like a security guard doing rounds. Every few minutes, they check the same doors, in the same order, following the same checklist. The algorithm does the same thing — except it completes its rounds thousands of times per second, never gets tired, and never skips a door.

Monitoring an open position is not just about watching profits. The algorithm is constantly managing risk as well. It may automatically sell if losses become too large, refuse to place new trades after reaching a daily loss limit, or reduce the size of future trades after a losing streak. In professional trading, managing risk is often just as important as finding profitable opportunities.

But here is the catch. The code is only as good as the rules it is given and the data feeding it. And unlike a human, it will never pause to second-guess a trade that feels wrong — it has no feelings to second-guess with. It just does exactly what it is told.

That is not a hypothetical risk. On August 1, 2012, Knight Capital — one of Wall Street's largest trading firms — accidentally reactivated a dormant piece of test code during a routine software deployment. Within 45 minutes, the firm had lost $440 million. The app did not crash. No error message popped up. The code simply executed, exactly as written, at scale, faster than any human could step in and stop it.

The lesson is not that algorithms are dangerous. It is that an algorithm has no built-in instinct for "this seems wrong”

Part B - the Data and the APIs

An algorithm is only as good as the data feeding it. So where does that data actually come from?

It starts at the exchange. Every time a trade happens on the Toronto Stock Exchange, the NYSE, or the NASDAQ — the price, the volume, and the timestamp are recorded instantly. The exchange then broadcasts this information outward, continuously, in real time. Every trade, every price movement, every second of the trading day.

That raw feed does not come directly to your algorithm, though. It travels first to data vendors — companies like Bloomberg and Refinitiv — who license the feed from the exchange, package it, and redistribute it to banks, brokerages, trading platforms, and apps. Wealthsimple gets its prices this way. Google Finance gets its prices this way. And so does your algorithm — but with one important difference, which we will get to in a moment.

So how does your algorithm actually talk to these data vendors and request information? Through an API — an Application Programming Interface. In plain terms, an API is simply a messenger. Your algorithm sends a request — "give me the current price of Apple" — and the API fetches it from the data source and returns the answer. Your algorithm never connects to the exchange or the data vendor directly. It talks to the API, the API handles everything in between, and the answer comes back in milliseconds.

Many beginner algorithms repeatedly request new market data in a continuous loop, just as described above. More advanced trading systems often take this a step further, maintaining a live connection, where price updates are streamed to the algorithm the moment they occur. The goal is the same - react as quickly as possible.

The same thing happens when your algorithm wants to place a trade. It does not log into a brokerage website and click buy. It sends an instruction to the brokerage's API — "buy 100 shares of Apple at market price" — and the API handles the execution. The entire process, from spotting the signal to placing the order, can happen in under a millisecond.

That speed matters because markets stand still. Between detecting an opportunity and placing an order, prices may already have changed. Every millisecond spent waiting for data or spending on an order increases the chance that the trade is executed at a different price, or missed entirely.

And here is that important difference we mentioned. When you search a stock price on Google, you are seeing data that is 15 to 20 minutes old. Google licenses delayed data because for someone casually checking a price, a 15-minute delay does not matter. For an algorithm making trading decisions, though, a 15-minute delay is completely useless. Algo traders pay for real-time data feeds — prices that reflect what is happening on the exchange right now, not what was happening before your last coffee break.

The APIs powering algo tradingMarket Data APIs — provides the information your algorithm uses to make decisions:

Polygon.io — real time and historical stock data, very popular with retail algo traders
Alpha Vantage — free tier available, good starting point for beginners
Bloomberg / Refinitiv — institutional grade, used by banks and hedge funds

Broker APIs — execute the decisions your algorithm has already made

Alpaca — commission free, developer friendly, great for beginners
Interactive Brokers (IBKR) — professional grade, access to global markets

Part C — Building and Testing the Strategy

Before a single dollar of real money is on the line, every serious algo trader runs a backtest. You feed your strategy years of historical market data and see how it would have performed under real-world conditions. Did it make money? How much did it lose at its worst point? How often did it get the trade right?

Even a successful backtest is not enough to trust a strategy with real money. Most traders next run it in a paper trading environment, where the algorithm receives live market data and places simulated trades without risking any capital. This helps uncover issues that historical testing might miss before a single real dollar is on the line.

Remember when we said the algorithm is only as good as the data feeding it? This is exactly where that comes back to bite people. Bad data silently destroys strategy performance — and the tricky part is, you often will not notice until you are trading live with real money. Here are the most common ways data quietly lies to you:

Survivorship bias — Imagine testing your strategy only on companies that still exist today. Of course it looks profitable — you have quietly erased every company that went bankrupt or got delisted along the way. The strategy was never actually that good; the dataset just hid its failures from you.

Look-ahead bias — This happens when data is timestamped incorrectly, letting your backtest "know" information before it would have actually been available. It is like grading yourself on a test after you have already seen the answer key — the result looks great, but it proves nothing.

Corporate action errors — Stock splits, dividends, and mergers all need to be adjusted in historical price data. Picture a stock that did a 2-for-1 split — without adjusting for that, your backtest sees the price "crash" by 50% overnight, even though nothing actually went wrong. That single unadjusted event can wreck an otherwise sound strategy.

Missing ticks or gaps — Brief outages or feed errors in the data, often happening during the most volatile moments in the market — exactly when accurate data matters most. It is like your security camera cutting out for thirty seconds, right when something important happened.

Vendor-specific anomalies — Different data providers sometimes record the same event slightly differently — a trade timestamped a few milliseconds apart, or a price rounded differently. Switch vendors mid-strategy, and these tiny inconsistencies can throw off your signals in ways that are hard to trace back.

So traders take this seriously. They validate incoming data against expected ranges, keep an audit trail of every data point used in a live trading decision, and run post-trade analysis to catch problems early — before bad data turns into bad trades.

Platforms for building and testing strategies:

QuantConnect — cloud based, write strategies in Python, backtest against years of historical data. The most popular platform for retail algo traders.
Backtrader — open source Python library, full control over your strategy and testing environment
TradingView — popular for writing and visualizing strategies, widely used by both beginners and experienced traders

That is the full engine room of algo trading — the code, the data, the APIs, and the testing environment that sits behind every automated trade.

From Institutions to your Basement

Two decades ago, algorithmic trading was the exclusive territory of major financial institutions. To even get started, you needed a Bloomberg terminal running at nearly $32,000 a year, a team of quantitative analysts, institutional access to the TSX data feed, and the backing of a major financial firm. A retail trader — no matter how talented — simply could not compete. The infrastructure alone was a wall too expensive to climb.

That wall has come down.

Today, a retail trader can implement the same strategies that institutions were running two decades ago with seven-figure budgets — from a laptop and a decent internet connection. Three things made that possible:

Cloud Computing — The processing power that once required a $50 million data center can now be rented from AWS or Google Cloud for a few dollars an hour. The hardware barrier is essentially gone.
Open APIs — Exchanges and brokerages began opening their systems to outside developers — the same data pipes and execution infrastructure that institutions had guarded for decades became accessible to anyone with a programming background.
Retail Platforms — Tools like QuantConnect and Alpaca were built specifically to put institutional-grade strategy building in the hands of individual traders — with documentation written for developers, not just quants with PhDs.

That is the fintech moment of algorithmic trading. Not the technology itself — that existed on Bay Street for decades. The moment was when the technology stopped being a privilege and became a tool anyone could pick up.

The Next Wave — AI and Machine Learning in Algo Trading

Traditional algorithms follow fixed rules set by a human — "buy when this condition is met, sell when that one is." The next generation of trading algorithms does not follow fixed rules. It learns them.

Machine learning models are increasingly replacing rule-based logic in algo trading. Instead of a human writing the strategy, the model is trained on years of market data and figures out the patterns itself — adapting and improving with every market cycle it processes.

The numbers reflect how fast this is moving. The algorithmic trading market grew from $21.89 billion in 2025 to $25.04 billion in 2026, with projections reaching $44.34 billion by 2030 — and AI is at the center of that growth.

Even at the institutional level, this shift is visible. Virtu Financial — one of the world's largest electronic market makers, executing over 5 million trades daily across 34 countries — partnered with AWS to deploy machine learning environments directly for its institutional clients, allowing them to apply AI to their own execution data for the first time.

And just like rule-based algo trading before it, this technology is beginning to trickle down. Cloud-based ML tools, open-source libraries like TensorFlow and PyTorch, and AI-native trading platforms are making machine learning strategies increasingly accessible to retail traders — not just hedge funds.

The direction is clear: the next retail algo trader will not just write rules. They will train models.

The tools are now freely accessible — but do retail traders have the knowledge and wisdom to use them well? That is a different question entirely.

Before you build one

The tools are accessible. The platforms are affordable. The strategies are documented. And if you have read this far, you probably have at least a passing urge to go try this yourself. Good. But before you do — here are three things that do not show up in the QuantConnect tutorial or the weekend workshop that promises to teach you algo trading in two days.

Overfitting - when the strategy is too good to be true :

Almost every algo trader, at some point, produces a backtest that looks spectacular — high returns, low drawdowns, almost no losing months. It feels like you have cracked the market.

You almost certainly have not.

What you have likely done is overfit your strategy — tuned it so precisely to historical data that it has memorized the past instead of learning from it. For anyone with a data science background, this is a familiar problem. It is the same as a model that scores 99% on training data and 60% on anything new.

In algo trading, the consequences are not a poor model score. They are real financial losses, live in the market, with your own money. The test is brutal in practice: does your strategy hold up on data it has never seen before? If it only works on the period you used to build it — it is not a strategy. It is a very expensive memory.

The Herding Problem

If you have watched Silicon Valley, you might remember the episode where Gilfoyle builds an AI to respond to Dinesh's messages, and Dinesh builds an AI Dinesh to respond back. The two AIs end up talking to each other in an endless loop — with no human in the conversation, no exit condition, and no one to pull the plug — until they cause a power outage. It is one of the funniest scenes in the show. In financial markets, the same dynamic is considerably less funny.

When thousands of traders build strategies using the same platforms, the same datasets, and the same widely documented approaches — they often end up with remarkably similar algorithms. And when similar algorithms react to the same signal at the same time, markets can move violently in ways that have nothing to do with underlying fundamentals.

This is called herding. It is partly what drove the 2010 Flash Crash — algorithms feeding on each other's activity, amplifying a move that no single trader intended. As AI-powered strategies become more widespread, regulators are increasingly paying attention to this risk.

Your algorithm does not operate in isolation. It is one voice in a very crowded room — and sometimes that room panics all at once.

Access is not Edge

The same democratization that put these tools in your hands has put them in the hands of hundreds of thousands of other retail traders. QuantConnect alone has over 200,000 registered users. And on the other side of your trades sit firms like Citadel, Virtu, and Renaissance Technologies — with teams of PhD mathematicians, proprietary data no public API can match, and servers co-located physically inside the exchange. Access to the tools is real. The playing field being level is not. Retail algo traders do find edges — in niche strategies, in asset classes the big firms ignore, in approaches that do not scale to institutional size but work at retail scale. But those edges require genuine rigour and intellectual honesty. The trader who understands this going in is dangerous in the best possible way. The trader who does not is just providing liquidity for someone else's algorithm.

Algorithmic trading is one of the most fascinating intersections of finance and technology today. The barriers that once made it exclusive to Bay Street have genuinely come down — and that is worth celebrating. But the market does not reward curiosity alone. It rewards preparation, discipline, and the humility to know what you do not know. So go build your strategy. Backtest it honestly. Start small. Read about the failures as much as the successes. The tools are in your hands. Now go learn how to use them well.

MANTHAN ED-TECH