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Main types of trading strategies in algorithmic trading
Algorithmic trading does not start with the question "where should I buy?" or with choosing an indicator. It starts with a hypothesis about why the market sometimes behaves non-randomly: it continues a move, reverts to an average, temporarily diverges between related instruments, pays for liquidity provision, or rewards the allocation of risk across assets.
5/5/2026
Backtesting: how to test trading strategies properly
Backtesting often looks like the most convincing stage of trading strategy development: load historical data, run the entry and exit rules, get an equity curve, calculate return and drawdown. If the chart rises, it is tempting to think the strategy has been proven.
4/25/2026
Data in trading: what data is used and where to get it
In algorithmic trading, the question "where do we get the data?" usually comes up too late. First comes the idea, then the search for quotes, then the backtest, and only after that does it become clear that the strategy in reality depends on data that was not actually present in the research setup. The result is a system that can look convincing on a chart and still be unusable in live execution.
4/19/2026
Risk management in algorithmic trading: why stability survives, not maximum profit
Algorithmic trading has an unpleasant pattern: strategies with the most impressive expected return tend to die earlier than modest but resilient ones. Not because they were poorly designed, but because there is simply not enough of them left by the time the expectation actually materializes. The market has time to deliver a couple of long drawdowns, capital melts, parameters get re-fitted on the fly, and the strategy disappears before its "average" gets a chance to play out.
4/17/2026
What Is Algorithmic Trading and How Is It Different From Manual Trading
Algorithmic trading, or algorithmic trading, is trading based on predefined rules executed by a computer system. These rules can determine when to place an order, how to adjust price, how to split a large order, when to exit a position, and how to limit risk. Regulators and industry reports describe the modern market as an environment where algorithms are used very broadly: from executing large orders to market making and high-frequency strategies.
4/6/2026