Learn to conduct in-depth backtests of your strategies without coding
Backtesting can often feel like a Herculean task. From sourcing data to building and testing strategies, the entire process is challenging — even for seasoned traders. For beginners diving into technical analysis (backtesting, specifically), it can seem downright intimidating.
That’s why I’m launching this no-code backtesting series. My goal? To backtest various technical strategies without writing a single line of code — all while not compromising on the depth of the analysis.
In this first article of the series, we’ll explore a simple yet powerful RSI trading strategy across different stocks. Together, we’ll analyze the strategy’s performance using detailed metrics, charts, and stats — all done with just a few clicks.
Ready to dive in?!
Platform for No-Code Backtesting
While there are quite a few options available in the market, for this series, we’re going with BacktestZone, a platform tailored for traders who want powerful results without the complexity of coding.
Background
BacktestZone is basically a no-code backtesting platform that allows traders to backtest on 80K+ securities (stocks, forex) spanning 70+ exchanges. The platform currently has around 30 technical indicators, covering all the basic ones and a variety of complex indicators.
Features
1. Easy-to-use interface: One thing that truly sets apart BacktestZone from its competitors is its clean, easy-to-use interface that enables traders to backtest any trading strategies with just a few clicks.
2. Detailed Evaluation Report: The platform provides a comprehensive analysis of the strategy that includes ratios, metrics, charts, stats, etc. This can come in handy when one wants to do a full-fledged investigation of the strategy’s performance.
All these factors make BacktestZone the perfect fit for this series.
Now that we have a good understanding of the platform we’re going to use in this series, let’s do some backtesting!
Backtesting RSI Strategy using BacktestZone
1. Creating a BacktestZone Account
Make sure to have a BacktestZone account to access the platform. If you don't have one, head over to the platform’s web app: https://backtestzoneweb.accozen.co.in/ and create an account. Once you’re logged in, you’ll be directed to the app’s main interface.
2. Setting-Up Basic Parameters
In this step, we’ll insert all the preliminary inputs that are:
Selecting the exchange and stock
Specifying the backtesting period
Selecting the technical indicator
Let’s start with selecting the exchange and stock. We’re going to backtest our strategy on Apple’s stock.
The next step is to specify the backtesting period. The default period is YTD but let’s test our strategy on 5-year historical data of AAPL. Let’s set our starting date to Feb 12, 2020, and the ending date to Feb 11, 2025:
The last step of our basic setup is to choose the technical indicator. The platform currently has 30+ technical indicators from the basic ones like SMA, and MACD to advanced indicators like Vortex indicator, TRIX, Chande Forecast Oscillator, etc. Let’s stick to RSI for this article:
3. Creating the Trading Strategy
The best thing about BacktestZone is that it gives a template to start from once the technical indicator is selected saving a ton of time. For example, if you choose SMA, the app will automatically fill in the specifications of the classic SMA 12,26 crossover strategy to give users a solid starting point.
Since it’s RSI in our case, the app will fill in the entry and exit conditions as follows:
The template we have here is nothing but another classic strategy which is the RSI 14 crossover strategy with 30 and 70 as the oversold and overbought levels respectively. For the sake of brevity, we’re gonna stick to this basic strategy.
If you want to be creative, I encourage you to play around with the different parameters and all the fields are entirely customizable.
Now that we’ve created our strategy, hit the “Save & Backtest” button to run the backtest and obtain the results.
4. Strategy Evaluation
BacktestZone offers an extensive evaluation report filled with tons of metrics, graphs, and tables. The metrics can be categorized into seven groups:
Key metrics
Statistical metrics
Performance metrics
Risk metrics
Consistency metrics
Risk-adjusted metrics
Tail risk metrics
In total, there are around 60+ evaluation metrics allowing traders to dive deep into the strategy’s performance.
The platform doesn’t have many graphs but covers the necessary ones which are:
Cumulative returns (Strategy vs Buy/Hold)
Strategy drawdown
Cumulative returns (Strategy vs Index)
Now that we have a good background on the evaluation report given by the platform, let’s try to actually evaluate our RSI trading strategy.
For the sake of brevity, I’m not going to touch upon all the metrics and charts; I will focus only on the Key metrics and the Cumulative returns (Strategy vs Buy/Hold) chart.
Here’s a snapshot of our strategy’s key metrics:
So our RSI trading strategy definitely played it safer than just holding AAPL, but at a cost. The cumulative return is way lower (39.38% vs. 87.74%), and the CAGR tells the same story — half the growth of buy-and-hold. The Sharpe ratio is slightly weaker too, meaning the risk-adjusted return isn’t great.
On the bright side, the max drawdown is much better (-19.58% vs. -31.31%), and volatility is significantly lower (14.51% vs. 23.61%), so the strategy is far less bumpy. It’s a classic case of trading off high returns for smoother, less gut-wrenching performance.
Now, let’s take a look at the Cumulative returns (Strategy vs Buy/Hold) chart:
The cumulative returns chart makes it clear — buy-and-hold (purple) absolutely outperformed my RSI strategy (light blue) over time. While buy-and-hold rode AAPL’s long-term uptrend, the strategy moved in a more step-like manner, with long periods of stagnation. This suggests the RSI strategy avoided major drawdowns but also missed big upward moves. It’s a classic trade-off: lower risk, lower reward. If the goal was smoother returns with less volatility, the strategy delivered, but if maximizing gains was the priority, holding AAPL was the clear winner.
Along with the metrics and charts, we can also get the list of trades executed by the program allowing us to get different perspectives of the strategy.
The trade log tells an interesting story — it made only a handful of trades over four years, favoring a low-frequency approach. Each trade followed a clear buy-sell cycle, locking in modest gains each time. The last trade closed at 227.65, much higher than the first buy at 118.62, but since the strategy wasn’t always in the market, it missed out on AAPL’s long-term rally. It played it safe but at the cost of those big buy-and-hold gains.
Final Thoughts
This entire backtesting process — setting up the RSI strategy, running it on AAPL, and evaluating the results — was done without writing a single line of code.
BacktestZone’s intuitive interface makes it ridiculously simple to define entry/exit rules, perform backtests, and analyze performance with detailed metrics and charts. Instead of spending hours coding, I was able to test and evaluate a technical trading strategy in just a few clicks.
If you’re a trader looking for a no-code way to validate your ideas and optimize your strategies, BacktestZone makes it effortless. And this is just the beginning — stay tuned as I test more strategies in the upcoming articles!
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