Dutch Algotrading

The Pokemon SlowBro trading algorithm

Added 2024-09-13 10:00:01 +0000 UTC

Introduction

Welcome my Patrons to yet another blogpost only strategy.

You already must know the message here, blogpost only algorithms do not have the highest scores in the Strategy League. However some do have some remarkable settings or something else special, that wil make it a “Patreon exclusive” 😊

So let’s dive into this algo.

Author and origins

There is no author known for this strategy. But looking at the name and the ASCII art in the code itself. He/She must me a Pokemon fan.

Let’s go high-level over the other code in this file:

Timeframe: The primary timeframe for this strategy is set to '1h' (one hour), which means the strategy will analyze data and make decisions based on one-hour candles.

Informative Timeframe: The strategy also utilizes an informative (secondary) timeframe set to '1d' (one day). This allows the strategy to consider daily data for more comprehensive decision-making.

This strategy has a dynamic ROI setting where it aims for different profit targets depending on how long the trade has been open:

100% ROI after 10080 minutes (one week), targeting a 100% profit.
30% ROI after 2880 minutes (two days).
20% ROI after 1440 minutes (one day).
10% ROI from the start.

Stop Loss: A very aggressive stop loss is set at -99%, which practically means the strategy will hold onto a position unless it is near total loss.

Sell Profit Only: This setting ensures that the strategy will only send a sell signal if the trade is in profit, aligning with the defined ROI targets.

Ignore ROI If Buy Signal: This is set to false, meaning the strategy will respect the ROI settings even if there is a concurrent buy signal, which could help prevent the strategy from holding onto positions too long in the hopes of additional gains.

Indicators

The strategy calculates the 30-day low and high prices from the daily data:

30-day Low: The lowest closing price in the last 30 days.
30-day High: The highest closing price in the last 30 days.

Trading Logic

The buy and sell signals are generated by the code as follows:

Buy Trigger: A buy signal is generated when the hourly close price crosses above the 30-day low from the daily data. This suggests that the asset is bouncing off a significant support level, indicating a potential upward movement.

Sell Trigger: Conversely, a sell signal is triggered when the hourly close price crosses above the 30-day high from the daily data. This setup aims to capture profit at a recent peak, assuming that touching this level might indicate a potential retracement or peak formation.

The Slowbro strategy uses a very straightforward logic based on price action relative to recent highs and lows on a larger timeframe. This method could be particularly effective in markets where historical support and resistance levels are respected. However, the extreme stop loss setting is quite risky as it exposes the trader to potentially significant losses. This aspect, combined with a reliance solely on price crossing historical extremes without additional filters or confirmation indicators, may result in a strategy that could benefit from further refinement and risk management enhancements to avoid large drawdowns during volatile or trending conditions.

Backtest results

The Slowbro strategy's performance on a 5-minute timeframe, as revealed by the backtest, offers us some interesting insights. Here's a critical analysis based on the table you've provided:

A 51.32% profit is not very high and there are certainly many other algo’s that perform way way better. Nonetheless, the strategy boasts an impressive win rate of 91.67%. High win rates are desirable; however, they need to be scrutinized against profit margins to ensure that the winning trades are not just recouping the losses from the fewer losing trades.

The superlow amount of 120 trades on the 5 minute timeframe here tells me that this strategy only makes trades on very rare occasions. If you would load your bot with this algo, then it will be dormant most of the time. Here opportunity costs come into play if you know that there are many other well performing algo’s that are much more active.

In this case the name of the algorithm certainly is applicable. It is very slow on a high frequency timeframe…

Now on the other hand, the maximum consecutive wins and losses has a difference of 100 trades. So if there is a trade on these rare occasions, then it is certainly time to step in. The probabilities are high that the trade is a winner. But beware of the negative aspects aI already told you here.

Lik for example the drawdown. It is high at 69.33%. This is concerning as it implies there is potential for significant losses, which could be distressing for traders, especially when leveraged.

The modest CAGR in conjunction with the Sharpe, Sortino, and Calmar ratios points out that the risk-adjusted returns are not as high as one might expect given the high win rate and profitability percentage. This could be a red flag for potential overfitting or lack of adaptability to different market conditions.

Finally, while the backtest does provide a snapshot of past performance, it's crucial to remember that it does not guarantee future results, especially in the fast-paced environment that a 5-minute timeframe represents. The high win rate may not sustain in live conditions, particularly with slippage and commission considerations.

Conclusion

The visual data corroborates our earlier analysis that while Slowbro is not highly profitable and has a low trading frequency. It may carry higher risk than some other strategies. Its win rate is impressive, but when the performance is adjusted for risk, Slowbro doesn't fare as well. This suggests the strategy may benefit from risk management improvements. Caution should be exercised if considering this strategy for live trading, and continuous monitoring would be essential due to its aggressive approach.