How it works
Summary
The Degen Legion Indicator (DLI) is used on TradingView charts to alert you to potentially profitable trades. Once added to the chart, it provides you with stop loss and target values for a specific token and timeframe.
Next, we introduce you to different strategies - from simply buying and selling Ethereum and Bitcoin, to buying and shorting other crypto spot pairs, or buying and shorting futures; based on your risk tolerance and experience. Then, we'll walk you through how to create webhooks with your custom target and stoploss values from the indicator, and show you how to set alerts.
Sounds complicated, but with a little practice, you'll be adding new tokens to trade in less than 5 minutes. It is very easy, but we take the time to explain the mechanics in detail on this page, because some folks want to know everything about why things work! To see how the indicator can work for you, skip ahead to the different strategy sections.
- Bottom Line: The following details about the DLI are for your reference, but all you need to know is this: Use the 15, 20, or 30 minute charts, always set your calendar back 60 days in the settings before you use the indicator, and look for AA-rated stop loss values. No need to read further, but this information will always be here if you want to come back and read more.
Degen Legion Indicator (DLI) alerts at the beginning of a new trend, highlighted in green or red, and captures strong moves. White labels mark the end of a trend, while profit multipliers occur in-between.
Down the rabbit hole
Degen Philosophy
1. DLI Trading Theory #1: The most conservative stop loss (SL) is the value that provides the maximum safety or the least chance of being hit.
The mean (average) is often considered a conservative measure because it factors in all data points, including outliers, which can skew the overall average. With this in mind, DLI sets stop losses based on the mean distance from the entry point to the lowest lows for long positions and to the highest highs for short positions, within user-defined timeframes and date ranges. However, DLI doesn’t stop there—it enhances this approach to further optimize your stop loss levels. The goal is to help you maximize your profit potential before being stopped out, providing a more effective balance between risk and reward.
2. DLI Trading Theory #2: The most conservative take profit (TP) level is the value that is most frequently reached or exceeded.
This method increases the likelihood of reaching the TP level, as the mode—the most frequently occurring value—serves as a conservative target due to its higher probability of being hit. However, while this approach is logical, it may not always yield the best results. DLI introduces a more sophisticated strategy by calculating targets based on a combination of factors. This includes the moving average (MA) of the highest highs for long positions and the lowest lows for short positions, all while factoring in recent market volatility.
By applying this method within a user-defined date range and timeframe, DLI creates more adaptive and responsive TP targets that align with current market conditions. You can also capitalize on alternating buy and sell signals, enabling you to capture profits as the market fluctuates between these levels through a reversal strategy. This nuanced approach offers greater flexibility and the potential for higher gains since you are in synch with the flow of the market and not randomly fighting the trend.
3. DLI Trading Theory #3: Arbitrary targets lead to arbitrary results (embrace dynamic, data-driven strategies instead).
Most trading indicators and signal groups provide entry signals but leave traders guessing about where to set TP and SL levels. Many rely on arbitrary, round numbers—like targeting a 5% profit or setting a 2% SL—often without any real basis in market data. While you might sometimes be content with a modest 2.5% gain, you could be missing out on much larger moves—20%, 30%, even 40% in spot—by not aligning your strategy with the actual behavior of the market.
The DLI indicator calculates optimal TP and SL levels based on historical data, market trends, and volatility. It considers the specific timeframe you’re trading on, recognizing that a 1.23% SL might be suitable for a certain 5-minute chart, while a 30 minute chart might require a 3.057% SL to account for broader price swings.
Stop losses: the market's way of saying, 'I promise, I'll just take a little bit... Oh wait, never mind, I'll take it all!'
Some Degen in agony somewhere
Market Dominance
The trend is your friend until the end
By using DLI’s approach, you avoid the pitfalls of setting arbitrary levels that don’t reflect market reality. Instead, you benefit from a method that adapts to the market’s movements, ensuring you’re neither stopped out too soon nor left holding the bag because you didn’t know when to exit. It’s crucial to let go when the data tells you to—because every coin, no matter how hyped, can turn into a loss if you don’t have a solid exit strategy. Be smart, be data-driven, and be like Chad (next photo)—know when to hold and when to fold.
DLI checks the high-highs and low-lows of trend changes to compute recommended targets and SLs in the table from the first buy or sell signal entry until the next opposing signal. The white boxes, alerts and trend highlighter are each viewable or hidden under settings as desired.
Data Analysis Explained
Average Drawdown (DD)
- The Average Drawdown (DD) value in the table represents the average percent drop in prices from the entry point to the extreme valley (for longs) or peak (for shorts) for each trade over the period of time selected by the user in the settings.
- These values are measured from the first buy or sell alert to the next alternating and reflected by white labels (ex; long closed or short closed).
- Successive (not alternating) buy or sell alerts without white labels are not included in the count, DD nor target averages. Successive signals occur in an attempt to multiply profits, which happen when take profit targets are reached or stop loss.
Target
- The Target value in the table is calculated using an Exponential Moving Average (EMA) of the highest highs (for long trades) and the lowest lows (for short trades) since the entries. These are both listed “TP” for Total Profit on the white labels, which is the maximum profit possible for that trade.
- The EMA-based Target provides a smoothed and more stable value than the mode or mean, reflecting the past performance of the trade.
- The target is directly related to the selected chart timeframe and user-selected back date in settings.
Chart Timeframe
- The 15 minute chart is a widely used and recognized timeframe among intra-day traders, which can lead to more predictable behavior and clearer signals due to higher volume and more significant price action. This interval often captures sufficient market movements, making it a good balance between capturing short-term trends and avoiding excessive noise.
- Therefore, DLI is optimized for the 15 minute chart along with the user setting the past 60 days in the calendar, but there’s also no problem selecting the 20 or 30 minute chart.
Moving Average related to Lookback Period
Understanding the Moving Average (MA) Calculation:
The MA in this indicator is fixed to represent approximately 60 days of trading activity. This MA is dynamically adjusted based on the selected chart timeframe to ensure consistency in its application, whether you are analyzing short-term or long-term trends. The 60-day MA provides a smoothed view of the market's behavior over the last 60 trading days, helping you identify the medium-term trend across different timeframes.
Implications of Different Lookback Periods:
When you adjust the lookback period in your chart settings (e.g., to the past 3 months, past 1 year, past 3 years, etc), it affects the context in which the fixed 60-day MA is applied. Here’s how different lookback periods can impact the interpretation of trends:·
- Shorter Lookback Periods (e.g., 1-3 months): The 60-day MA will cover a significant portion of the data within this period, providing a trend analysis that is closely aligned with the selected timeframe. This setup is particularly effective for analyzing recent market movements and identifying medium-term trends.·
- Longer Lookback Periods (e.g., 1-3 years): In this scenario, the 60-day MA will only account for the most recent part of the selected period, offering a view of the market's behavior over the last few months within a broader historical context. While the MA will still provide valuable insights into recent trends, it may not capture longer-term market movements that span the entire selected period.
Recommendations for Use:
Given that the 60-day MA is fixed and cannot be adjusted within the settings of this script, it is important to understand how to best utilize it based on your analysis goals.
- For Medium-Term Analysis: The 60-day MA is ideal for understanding the medium-term trend within a 15-30 minute timeframe. If your focus is on market behavior over the past few months, this MA will offer a reliable trend signal.·
- For either shorter or more long-term analysis: The 60-day MA will still provide valuable insights, but keep in mind that it is optimized for a 60-day view. For short-term analysis, the MA might appear slightly less responsive, and for long-term analysis, it may focus more on recent trends rather than the entire period.
Trade Count and its importance
- The Trade Count value in the table shows the total number of trades (both long and short) that have been analyzed.
- It is calculated by adding the number of recorded long and short trades.
- The reason why we advise not using a higher timeframe than the 30 minute chart is simply because higher timeframes within a 60 day period lookback generally produce trade counts of less than 30 during this period.
Why Have a Trade Count of At Least 30?
- Statistical Significance: Having at least 30 trades ensures that the calculated SL and TP values are statistically significant. With more trades, the averages and patterns become more reliable and less susceptible to random variations.
- Confidence in Results: A higher trade count gives more confidence in the SL and TP values because they are based on a larger sample size. This reduces the impact of outliers or unusual market conditions on the overall results.
- Better Risk Management: With more data points, traders can better assess the risk and potential reward of their trading strategy, leading to more informed decision-making.
- Robust Strategy Testing: A minimum of 30 trades provides a solid foundation for backtesting and optimizing the strategy, ensuring that it performs well under various market conditions.
Stop Loss (SL) value explained
In case you wonder where DLI gets the SL value it posts on the table in the chart for longs and shorts, here is our logic. Look for an AA rating for SL values in the table for your best bet!
When considering stop loss (SL) levels in relation to average drawdown, it is generally more profitable to use a stop loss that is slightly greater than or equal to the average drawdown (DD).
SL Greater Than Average Drawdown (A):
- Pros: This approach allows more room for trades to recover from temporary market fluctuations, potentially increasing the win rate and capturing larger profits. It can reduce the frequency of stop-outs during volatile periods.
- Cons: It increases the risk on each trade, as losses per trade can be larger. This approach requires a higher risk tolerance and more robust risk management strategies to ensure overall profitability.
SL Equal to Average Drawdown (B):
- Pros: This aligns with historical performance, ensuring that the stop loss is set at a level that has been statistically validated. It balances the need to cut losses while allowing enough room for trades to recover and become profitable.
- Cons: While this approach is generally sound, it may still result in stop-outs during periods of increased volatility that exceed historical norms.
SL Less Than Average Drawdown (C):
- Pros: This approach minimizes losses on individual trades.
- Cons: It can lead to more frequent stop-outs, even during normal market fluctuations, resulting in potentially missing out on profitable trades. The overall strategy may suffer from lower win rates and higher transaction costs due to frequent trading.
Optimal Strategy:
Using a stop loss level that is slightly greater than the average drawdown tends to be the most effective. This approach strikes a balance between giving trades enough room to move without being prematurely stopped out and protecting against excessive losses. In other words, allowing trades the opportunity to recover while still maintaining a level of risk control. Therefore, the Stop Loss (SL) value is calculated in the following priority of desirability, and the script displays the corresponding letter code in the table (Example A is best, C is less preferable).
Base case: SL= (DD+Target/2)/2
A. If calculated base case < Target/2, then display base case
B. If calculated base case > Target/2, then display DD if DD < Target/2
C. Otherwise, display Target/2
Example A:
- DD = 1.0
- Target = 6.0
Since calculated base case < Target/2, then display base case
Result: SL=2.0
Example B:
- DD = 1.0
- Target = 7.0
Since Base Case > Target/2 and DD < Target/2, we display DD
Result: SL=1.0
Example C:
- DD = 2.5
- Target = 4.0
Since neither A or B are true, then target/2
Result: SL=2
Data Analysis Summary
The SL and target values provide crucial insights into the typical performance of your trades, helping you manage risks and set realistic profit targets. Selecting AA-grade SLs, ensuring a trade count of at least 30 and setting your calendar look-back to the past 60 days improves the reliability and accuracy of these metrics, making your trading strategy more robust and effective.
All factors being equal, the tokens with the largest ratio of target/stop loss values (comparing longs to longs, and shorts to shorts – or total average) have the highest likelihood of being the most profitable – in theory.
꧁ But wait, there's more! ꧂
Actually, a lot more!
We are not called Degen Legion just because it’s a trendy name! We also want to help each other find the best settings and share gem trades. Your pre-installed factory settings are great for any token on multiple timeframes, but with a little tweaking, there are surely some new discoveries to make!
The DLI defaults to the Exponential Moving Average (EMA), but there are many more to choose from, and practically infinite range of settings to test different configurations for the highest targets and lowest SLs. Your indicator includes the following, which all work seamlessly to provide you targets and stop losses. Feel free to experiment and make any changes you want in the settings – you can always restore the default at any time - just delete it from your chart, and add it again.
1. EMA – Exponential Moving Average
The Exponential Moving Average (EMA), introduced by traders in the early 20th century, emphasizes recent prices, providing a more timely reflection of current market conditions by applying a higher weight to today's closing price. This makes EMA ideal for identifying trends and reversals in dynamic markets due to its heightened sensitivity to recent price changes, allowing traders to react faster to market movements.
2. SMA – Simple Moving Average
The Simple Moving Average (SMA), a staple in technical analysis since the early 1900s, calculates the average price over a set number of periods, treating all data points equally. This straightforward approach makes the SMA perfect for identifying overall market trends and smoothing out short-term volatility, making it suitable for long-term analysis and steady investment strategies.
3. WMA – Weighted Moving Average
The Weighted Moving Average (WMA) assigns greater importance to recent data points, with weights decreasing linearly. Developed in the mid-20th century, this approach enhances the WMA's responsiveness to the latest market movements, making it effective for short-term trading where recent price actions are more significant, helping traders capture short-term trends and make timely decisions.
4. DEMA – Double Exponential Moving Average
Introduced by Patrick Mulloy in 1994, the Double Exponential Moving Average (DEMA) reduces the lag associated with traditional EMAs by incorporating an EMA of the EMA in its calculation. This feature is essential for fast-moving markets where reducing lag can result in quicker and more accurate decision-making, providing a competitive edge.
5. TMA – Triangular Moving Average
The Triangular Moving Average (TMA), developed in the mid-20th century, smooths data by averaging the SMA twice, giving more weight to the middle of the data set. This results in a smoother and more reliable trend line, ideal for filtering out market noise and following trends, making it a valuable tool for trend-following systems and reducing the impact of short-term fluctuations.
6. VAR – Variable Moving Average
The Variable Moving Average (VAR) adjusts its smoothing constant based on market volatility, a concept introduced in the late 20th century. This adaptive feature makes the VAR perfect for changing market conditions without the need for manual adjustments, ensuring the indicator remains relevant and effective across different market environments.
7. WWMA – Weighted Window Moving Average
The Weighted Window Moving Average (WWMA) assigns different weights to different periods within a moving window, often emphasizing recent price action. This approach, developed for modern algorithmic trading, is useful for algorithms that require a focus on recent data within a fixed analysis window, enhancing the relevance of the indicator for short-term trading strategies.
8. ZLEMA – Zero Lag Exponential Moving Average
Introduced by John Ehlers in the early 2000s, the Zero Lag Exponential Moving Average (ZLEMA) attempts to eliminate lag by subtracting the lag of the data, resulting in a more responsive moving average. This feature is excellent for traders who need highly responsive indicators to detect early trend changes, providing an edge in fast-paced trading environments.
9. TSF – Time Series Forecast
The Time Series Forecast (TSF) uses linear regression to predict future prices based on past trends, a method grounded in statistical analysis. This makes the TSF effective for predicting future price movements and confirming trends, making it a valuable tool for traders focused on trend prediction and validation.
10. HULL – Hull Moving Average
Developed by Alan Hull in 2005, the Hull Moving Average (HMA) reduces lag while maintaining a smooth curve by using the weighted moving average of weighted moving averages. This innovative approach makes the HMA optimal for traders looking to quickly identify trends without the noise of lag, providing a clear and timely indication of market direction.
11. TILL – Tillson Moving Average
The Tillson Moving Average (TILL), created by Tim Tillson, is a sophisticated smoothing technique that reduces lag and noise, similar to the HMA but often more complex. This makes TILL best for smoothing price data to reveal underlying trends, making it a powerful tool for identifying market direction without the lag associated with other moving averages.
12. WILDER – Wilder’s Moving Average
Developed by J. Welles Wilder in the 1970s, this moving average is a variation of the EMA with a smoothing factor of 1/n, making it smoother and less sensitive to short-term fluctuations. This characteristic makes Wilder’s Moving Average suitable for markets with consistent, moderate trends, and it is often used in conjunction with other Wilder indicators like the RSI for comprehensive analysis.
13. LSMA – Least Squares Moving Average
The Least Squares Moving Average (LSMA) fits a straight line to the last ‘n’ data points, projecting it forward to estimate the trend direction. Developed from regression analysis techniques, the LSMA is ideal for predicting trend direction and smoothing out volatility, providing clear trend signals for traders.
14. KAMA – Kaufman’s Adaptive Moving Average
Introduced by Perry Kaufman in 1995, Kaufman’s Adaptive Moving Average (KAMA) adjusts its length based on market volatility, reducing lag during trends and minimizing noise during sideways movements. This adaptability makes KAMA effective for traders needing a flexible moving average that responds to different market conditions, offering a dynamic approach to trend analysis.
15. VWMA – Volume Weighted Moving Average
The Volume Weighted Moving Average (VWMA) weights prices based on trading volume, giving more significance to periods with higher volume. Developed in the modern era to address the need for volume-based analysis, VWMA is best used in volume-heavy markets to assess the true market direction influenced by actual traded volume, providing a more accurate reflection of market sentiment.
