How to test a trading bot

In the era of automation and algorithmic cryptocurrency trading, checking a strategy’s effectiveness before a real launch is not just a recommendation but a mandatory step. Let’s figure out how to test a bot, which methods exist, their advantages, and how to avoid typical mistakes.

Why test a trading bot before launch

When real money is at stake, a blind launch of a trading algorithm is not the most far‑sighted idea. Testing a trading bot on history helps you understand how it will behave in different market conditions and phases: rise, fall, sideways movement. Without tests the algorithm may look promising only on paper yet fail completely in practice.
The main goal of testing is to identify weak points in a strategy and reduce algorithmic‑trading risks before letting the bot loose in real market conditions. Newcomers, who still have to figure out how to choose bot parameters and adapt it to their trading style, should definitely turn to testing.

Types of bot testing

There are two main approaches that allow an adequate evaluation of a trading strategy.

The first is a backtest, where the strategy is run on historical data. This method provides extensive statistics on the algorithm’s past performance, showing periods of maximum drawdown, frequency of entries and exits, and profit dynamics.

The second is a forward test, which involves using the strategy in real time but with limited capital or on a demo account. This approach lets you check the algorithm’s behaviour in current market conditions, eliminating the “curve‑fitting” effect.

Ideally, both methods should be used sequentially to obtain the most objective picture.

How to run a backtest

On the Veles Finance platform backtests are performed automatically, so users do not need programming skills. After registration, the user gets full access to backtests as well as a $5 welcome bonus for crypto‑bot trading.

To run a historical test, choose a strategy and set parameters—trading instrument, timeframe, trade size, stop‑loss and take‑profit levels. Testing these parameters helps the trader understand how to choose bot settings, adapting the algorithm to current market conditions. The platform interface is intuitive, so setting up a crypto bot requires no technical background.

Next, the Veles backtest starts, and within seconds the trader receives a detailed report:

• Total profit/loss
• Maximum drawdown
• Number of trades
• Average profit/loss per trade
• Percentage of successful trades
• Equity curve visualization

The data let you objectively check the strategy’s true effectiveness and resilience in different market phases. The visual format simplifies analysis even if the user has no prior trading or automation experience.
For a step‑by‑step guide on how to use backtests, read our blog — How to use backtests.

How to assess strategy quality

Assessing a trading strategy is not only about analysing profit. Stability, risks, and trading logic matter as well. Even high historical returns can be an illusion if the strategy cannot withstand current market conditions—a common occurrence.

First thing to watch is stability.
A good strategy should show smooth growth without sharp equity drawdowns. If profit jumps and the capital curve looks like a rollercoaster, that likely signals an unstable strategy or overlooked details. Drawdown size is crucial too. Even a profitable system may be unacceptably risky if it drops 40 % of capital or more, because a losing streak could wipe you out.

Next is the ratio of winning to losing trades.
Important is not only the number but also the size: if losses outweigh gains, the strategy will be unprofitable over distance. You must also account for commissions and slippage, especially in high‑frequency strategies where these costs can eat the entire result.

An equally dangerous issue is over‑optimisation.
That is when a strategy works perfectly on history but collapses in live trading—often because parameters were fitted to a specific data slice. To avoid this, test the strategy on different market segments, split data into training and test periods, and then run a forward test in real time.

A quality strategy is clear and explainable. If the trader cannot describe what the entry is based on and why it works, then even with good stats it remains a “black box.” In a volatile environment like crypto, that is simply a losing approach.

Common beginner mistakes

Newcomers to algorithmic trading often make the same mistakes that later cost them dearly—not only in money but also in motivation. Understanding these typical errors is already half the way to success. If a trader realises what to avoid, he can build a more stable and profitable strategy. Below are the main beginner mistakes in crypto when working with trading bots and testing strategies.

One of the most common mistakes is launching a bot without preliminary testing.
Many rely on a nice strategy presentation or someone’s recommendation and start using an algorithm live without checking how it works on history. A gross strategic error. Without a backtest and subsequent forward test, you cannot know how tradable a strategy is. The trader then hits a losing streak without knowing whether the culprit is the market, the settings, or the algorithm logic.

Another typical mistake is blind faith in historical profit.
When a newcomer sees good backtest results, he often does not understand what produced them.
The strategy may be overly optimised for specific historical data and completely ineffective in the future. Beginners strive to pick “perfect” parameters, curve‑fitting the strategy to history, but in real time that system loses money—especially if testing used a small data sample or only one market cycle.

A separate problem is incorrect crypto‑bot setup.
Many novices do not understand how to choose bot parameters: they set risk too high, place stop‑losses too close, forget commissions, or ignore an asset’s volatility. These errors directly affect results. Sometimes even a sound strategy loses simply due to technically poor settings.

A classic mistake driven by the desire for quick money is running the bot on the entire deposit.
As in manual trading, risk management, capital allocation, and gradual scaling are vital in algo‑trading. Starting athletes don’t lift maximum weight immediately; otherwise, injury is likely.
The same with trade size—raise it as the deposit grows organically. Beginners want fast profit and risk it all. One losing streak leads to total loss. Algorithmic trading is not a 100 % guarantee and still requires trader control, it is the same systematic work.

A serious error is ignoring platform technical limits.
Beginners rarely check how fast their bot‑testing platform works, how orders are processed, what fees on the chosen exchange are, and whether the required API is supported. They face “surprises” and waste time fixing issues instead of focusing on trading.

Finally, another frequent mistake is lack of analysis and feedback.
After launching a strategy or bot, beginners seldom analyse why they had losses or gains. They keep no journal, ignore trade reports, and don’t refine parameters. They just “play” instead of building a system. Remember: trading is strict discipline. Only constant testing, tuning, evaluation, and adjustment make a strategy viable and profitable.

Launching a trading bot

When the testing stages are complete, the algorithm is verified via Veles backtest, and confirmed, it’s time to run it live. Even here act step by step. Start with a small capital amount, closely watching the strategy’s behaviour. Monitor market‑volatility changes and be ready to adjust the crypto bot quickly if the market phase shifts. Only after successful backtests and positive results should you scale the strategy and use it as a full‑fledged earning tool.

FAQ

1. What is a backtest and why is it needed?
   A backtest in trading is a simulation of a trading algorithm on historical data to assess its effectiveness before live deployment.

2. How to run a strategy backtest in TradingView?
   With TradingView backtesting you can use Pine Script to create and analyse a strategy on history, setting parameters and visualising trades on the chart.

3. Which is better: backtest or forward test?
   Both stages are important. A strategy backtest shows how it worked in the past, and a forward test shows how viable it is in the present.

4. How to check a strategy for over‑optimisation?
   Test it on different markets, periods, and assets, and be sure to run a forward test to confirm stable behaviour outside historical data.

5. How to choose bot parameters and avoid mistakes?
   Only through thorough historical testing, risk‑aware setup, and constant analysis of the algorithm’s live behaviour can you pick optimal parameters for your trading bot.