Dutch Algotrading
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RACING WITH TRENDS - True Lambo V2
🕑 Added 2024-12-05 19:00:02 +0000 UTC
Hello my dear Patrons,
In this post another analysis of a trading strategy I found on the Internet. It is called the True Lambo 2 and is probably (hopefully) improved version of the true_lambo 1 trading algorithm that I tested a while ago.
Let’s keep things short and simple and stick to the plan of making not such dreadfull long posts at the end of this year. We have more to do at the moment (like watching Bitcoin reach 100.000 US Dollar for example 😊).
The algorithm is created by jilv220 and has the following remarks added by the author:
The Strategy
The strategy has loads of buy and sell parameters that can be optimized by Hyperopt. It looks to be aimed to high-frequency trading on short timeframes, which makes it suitable for active cryptocurrency markets.
Timeframe and Stoploss
Timeframe: The strategy operates on a 5-minute timeframe, allowing it to capture rapid price movements.
Stoploss: A deep stoploss of -0.99 is set, though a custom trailing stoploss function is used for more precise risk management during trades.
ROI (Return on Investment) Targets
The strategy features a step-based ROI configuration:
0 minutes: 9.2% profit
29 minutes: 4.2% profit
85 minutes: 2.8% profit
128 minutes: 0.5% profit
This allows the strategy to lock in profits depending on how long a trade is active.
Indicators Used
The strategy utilizes a broad set of technical indicators to analyze market trends and identify trading opportunities. Some key indicators that are configured are:
Exponential Moving Averages (EMA): Short-term (5, 8, 14) and long-term (50, 200) EMAs track momentum and trends.
Bollinger Bands (BB): These provide volatility insights, with various standard deviation widths for nuanced analysis.
VWAP Bands: Used to identify support and resistance levels, calculated with rolling standard deviations.
RSI and Stochastic Indicators: Offer insights into overbought and oversold market conditions, critical for timing trades.
Elliott Wave Oscillator (EWO): Detects wave-like price patterns.
T3 Average: A smoothed trend-following indicator for advanced market movement detection.
It also uses a custom stop loss function too that is activated. It uses predefined profit thresholds to transition between different trailing stoploss levels. For example, at lower profit levels, a moderate stoploss is applied, but as profits increase, the stoploss tightens to secure gains. The transition between these levels is smooth, using linear interpolation to ensure that the stoploss adjusts proportionally as the trade moves into deeper profitability.
Buy Conditions
The algorithm includes multiple buying rules optimized for diverse market conditions:
Crash Recovery: Detects sharp declines by analyzing percentage drops over short timeframes (3, 9 candles) to enter at potential recovery points.
ClucHA and VWAP Strategies: Buy signals are generated based on price interactions with Bollinger Bands and VWAP levels, signaling potential reversals.
Momentum Metrics: Uses RSI and EWO to confirm a strong bullish or bearish momentum before placing trades.
Lambo-specific Entries: Combines fast and standard RSI levels with EMA crossovers to enter trades in moderately bullish conditions.
Sell Conditions
The selling logic focuses on maximizing profits while minimizing risks:
Exits occur when price crosses above key moving averages or deviates significantly from predefined thresholds.
RSI-based conditions ensure momentum fades before closing positions.
Again, the custom trailing stoploss is employed to let profitable trades run while securing gains against sudden reversals.
Backtest results
As always I tested this algorithm on its merits and got the following optimistic results back from the tests on the 5 and 15 minute timeframe (because of the maximum 1 hour timeframe as the informative timeframe).
The best-performing timeframe in this table is the 5-minute timeframe, achieving an impressive 843.80% profit with a 77.97% win rate over 5,278 trades. The Calmar Ratio (3.38) and Sharpe Ratio (5.33) indicate strong risk-adjusted returns, despite a maximum drawdown of 32.91%. The relatively high win streaks (45 max) and profit factor of 1.22 suggest consistent profitability, making this timeframe optimal for this strategy's high-frequency design.
This chart shows the True Lambo strategy's performance on the 5-minute timeframe, analyzing cumulative wins, losses, and profit over time.
The cumulative profit demonstrates a sharp rise during the 2021 bull market, peaking around the bull market's top (highlighted by the yellow dashed line). This reflects the strategy's ability to capitalize on favorable market conditions with high volatility and trending behavior.
The wins line exhibit a steady and consistent increase throughout the backtest, indicating a high win rate. However, post-bull market, the profit line begins to decline, suggesting that the strategy struggles in less favorable or choppy market conditions.
The losses line remain relatively stable, showing that the strategy manages risk well by maintaining fewer losing trades, which aligns with its high win rate.
This chart highlights the strategy's strong performance during trending conditions and its vulnerability to drawdowns in stagnant or bearish periods, likely due to its trend-reliant buy and sell signals. This calls for further optimization to adapt to varying market environments.
The drawdowns are relatively minimal and stable during the early period up until late 2021, coinciding with the bull market's favorable conditions. The drawdowns during this period mostly remain below the average, reflecting the strategy's ability to handle upward-trending markets efficiently.
However, post-2021, as the market transitions into more volatile or bearish phases, the drawdowns increase significantly, peaking above 25% in some instances. This rise indicates that the strategy struggles in non-trending or declining market conditions, likely due to its reliance on trend-following mechanisms, which lose effectiveness during such periods.
The weekly winrate distribution on the left shows a relatively high median winrate, centered around 0.8 (or 80%). The boxplot is fairly narrow, indicating consistency in the strategy's winrate across trades. However, the presence of a few outliers at the lower end (around 0.4) reflects occasional instances of underperformance, potentially due to market conditions that are unfavorable for the strategy's trend-following logic.
The profit distribution on the right reveals that most weekly profits are clustered near a relatively modest range, with a median slightly above zero. There are a few significant outliers in the positive direction, representing highly profitable periods likely linked to strong market trends. But there are also outliers in the negative range, which could correspond to drawdown-heavy weeks during choppy or bearish market phases.
This information aligns with its strong reliance on trending markets and the potential for large drawdowns during adverse conditions.
This strategy can compare its high win percentage to the other best performing strategies (that I had at that time – at the moment I have other better algos in the league – I was running behind schedule with some of these algo’s)
This high win rate is counterbalanced by a significant drawdown, which reflects the strategy's vulnerability to adverse market conditions or large losses that are not sufficiently mitigated by its risk management rules.
The algorithm overall has modest performance metrics—such as lower Calmar and Sortino ratios—compared to other strategies in the chart. While it generates strong results in trending markets due to its frequent wins, the high drawdown indicates that its ability to handle downturns or periods of increased volatility is limited. This suggests a need for additional safeguards, such as tighter stoploss settings or enhanced market condition filters, to reduce the impact of losing trades and improve the overall risk-adjusted performance.
It’s no wonder that this performance leads to a place within the top 50 (I guess). It’s not the worst strategy because the rules and indicators do feel like the have potential, but tweaking the other variables and parameters might seem to make the performance better. Still this True Lambo strategy achieves a high win rate and consistent performance in trending markets but suffers from significant drawdowns, which limit its overall effectiveness and highlight the need for better risk management in volatile or bearish conditions.
All the output of the test, including the strategy Python file are in the zip file included in this post. It also includes the plots of this strategies performance over time. If you do want to investigate the potential of this algo further. Please do so with caution. Backtest and forward test this on your own setup and after confirmation of true profits carefully start testing with real money.
Thanks for reading this post and I’ll see you in the next post.
Goodbye!
Comments
Dutch Algotrading
Thank you only4java for your comment. This is correct. As long as I am testing these algos out I will use those configs. If I will change this, then I will certainly make this public.
Dutch Algotrading
Yes I still use these same configs. No need to change these since then all future comparisons cannot be made.
methun trade
Thanks
only4java
Hey fella, you can find them In the shop https://www.patreon.com/dutchalgotrading/shop/my-config-files-and-data-for-backtesting-7861?source=storefront
methun trade
I was also wondering the same
only4java
Do you still using the config of this post? https://www.patreon.com/posts/my-config-for-is-77678512 Can u provide us with a newer one? Thx
