Our approach

Our Approach to Algorithmic Trading

Quantitative methods and automation, supported by research and clear expectations about what they can and cannot do.

How we think about it

Technology in service of a process

QuantFarming’s development communications have discussed trading-system optimization, historical-data testing, and regression analysis. The common thread is turning ideas into something that can be measured, tested, and reviewed.

Algorithmic trading

Computer-based instructions apply defined rules to market information. An algorithm may identify opportunities, generate signals, or place orders, but the presence of an algorithm does not establish that a strategy will be profitable. Source: QuantConnect

Quantitative research

Quantitative trading uses data, mathematical methods, and statistical analysis to develop and evaluate ideas, then checks whether findings hold up beyond the data used to develop them. Source: QuantConnect

Backtesting and overfitting

Backtesting applies rules to historical data to see how they would have behaved. A strategy can fit that history closely without working on new data, which is called overfitting, so simulated results are never proof of future success. Source: QuantConnect research guide

AI as a research tool

QuantFarming has explored artificial intelligence and machine learning for evaluating market conditions and refining inputs, including volatility and relationships in market data. AI helps analysis; it does not predict the future or remove risk. Source: CFTC advisory on AI trading bots

Rules and judgment

Rules-based versus discretionary trading

Rules-based trading sets the conditions for decisions in advance. Discretionary trading leaves more room for judgment in the moment. A rules-based approach can be followed manually or implemented through software.

In either case, a trading plan should address the strategy, risk management, and how decisions will be reviewed. Software does not replace the need for that plan, and having a structured process does not guarantee a profit.

Dimly lit desk with monitors showing market charts

Evaluating any tool

What to look for before trusting software

These questions apply to any algorithmic product, including ours.

What does it do automatically?

Some tools only provide signals for a person to review; others can place trades. Know what runs on its own and what remains your responsibility.

What evidence supports the claims?

Look beyond promotional examples. Ask what the software is designed to do and which risks apply to the markets involved.

What will it cost?

Review subscription charges and trading costs, including fees and spreads, before you commit.

Further reading from independent sources: CFTC customer advisory on AI trading bots, CME Group trade plan workbook, FINRA overview of algorithmic trading, and the QuantConnect research guide.

Want to talk it through?

Book a free introductory call to ask about the technology, the support, and how the program is structured.

Educational notice. This content is for educational and informational purposes and is not personalized investment advice. Trading involves risk, including the possibility of financial loss. Past performance and simulated results do not guarantee future outcomes. Read the full risk disclosure.