Trend Deviation strategy - BTC [IkkeOmar]Intro:
This is an example if anyone needs a push to get started with making strategies in pine script. This is an example on BTC, obviously it isn't a good strategy, and I wouldn't share my own good strategies because of alpha decay.
This strategy integrates several technical indicators to determine market trends and potential trade setups. These indicators include:
Directional Movement Index (DMI)
Bollinger Bands (BB)
Schaff Trend Cycle (STC)
Moving Average Convergence Divergence (MACD)
Momentum Indicator
Aroon Indicator
Supertrend Indicator
Relative Strength Index (RSI)
Exponential Moving Average (EMA)
Volume Weighted Average Price (VWAP)
It's crucial for you guys to understand the strengths and weaknesses of each indicator and identify synergies between them to improve the strategy's effectiveness.
Indicator Settings:
DMI (Directional Movement Index):
Length: This parameter determines the number of bars used in calculating the DMI. A higher length may provide smoother results but might lag behind the actual price action.
Bollinger Bands:
Length: This parameter specifies the number of bars used to calculate the moving average for the Bollinger Bands. A longer length results in a smoother average but might lag behind the price action.
Multiplier: The multiplier determines the width of the Bollinger Bands. It scales the standard deviation of the price data. A higher multiplier leads to wider bands, indicating increased volatility, while a lower multiplier results in narrower bands, suggesting decreased volatility.
Schaff Trend Cycle (STC):
Length: This parameter defines the length of the STC calculation. A longer length may result in smoother but slower-moving signals.
Fast Length: Specifies the length of the fast moving average component in the STC calculation.
Slow Length: Specifies the length of the slow moving average component in the STC calculation.
MACD (Moving Average Convergence Divergence):
Fast Length: Determines the number of bars used to calculate the fast EMA (Exponential Moving Average) in the MACD.
Slow Length: Specifies the number of bars used to calculate the slow EMA in the MACD.
Signal Length: Defines the number of bars used to calculate the signal line, which is typically an EMA of the MACD line.
Momentum Indicator:
Length: This parameter sets the number of bars over which momentum is calculated. A longer length may provide smoother momentum readings but might lag behind significant price changes.
Aroon Indicator:
Length: Specifies the number of bars over which the Aroon indicator calculates its values. A longer length may result in smoother Aroon readings but might lag behind significant market movements.
Supertrend Indicator:
Trendline Length: Determines the length of the period used in the Supertrend calculation. A longer length results in a smoother trendline but might lag behind recent price changes.
Trendline Factor: Specifies the multiplier used in calculating the trendline. It affects the sensitivity of the indicator to price changes.
RSI (Relative Strength Index):
Length: This parameter sets the number of bars over which RSI calculates its values. A longer length may result in smoother RSI readings but might lag behind significant price changes.
EMA (Exponential Moving Average):
Fast EMA: Specifies the number of bars used to calculate the fast EMA. A shorter period results in a more responsive EMA to recent price changes.
Slow EMA: Determines the number of bars used to calculate the slow EMA. A longer period results in a smoother EMA but might lag behind recent price changes.
VWAP (Volume Weighted Average Price):
Default settings are typically used for VWAP calculations, which consider the volume traded at each price level over a specific period. This indicator provides insights into the average price weighted by trading volume.
backtest range and rules:
You can specify the start date for backtesting purposes.
You can can select the desired trade direction: Long, Short, or Both.
Entry and Exit Conditions:
LONG:
DMI Cross Up: The Directional Movement Index (DMI) indicates a bullish trend when the positive directional movement (+DI) crosses above the negative directional movement (-DI).
Bollinger Bands (BB): The price is below the upper Bollinger Band, indicating a potential reversal from the upper band.
Momentum Indicator: Momentum is positive, suggesting increasing buying pressure.
MACD (Moving Average Convergence Divergence): The MACD line is above the signal line, indicating bullish momentum.
Supertrend Indicator: The Supertrend indicator signals an uptrend.
Schaff Trend Cycle (STC): The STC indicates a bullish trend.
Aroon Indicator: The Aroon indicator signals a bullish trend or crossover.
When all these conditions are met simultaneously, the strategy considers it a favorable opportunity to enter a long trade.
SHORT:
DMI Cross Down: The Directional Movement Index (DMI) indicates a bearish trend when the negative directional movement (-DI) crosses above the positive directional movement (+DI).
Bollinger Bands (BB): The price is above the lower Bollinger Band, suggesting a potential reversal from the lower band.
Momentum Indicator: Momentum is negative, indicating increasing selling pressure.
MACD (Moving Average Convergence Divergence): The MACD line is below the signal line, signaling bearish momentum.
Supertrend Indicator: The Supertrend indicator signals a downtrend.
Schaff Trend Cycle (STC): The STC indicates a bearish trend.
Aroon Indicator: The Aroon indicator signals a bearish trend or crossover.
When all these conditions align, the strategy considers it an opportune moment to enter a short trade.
Disclaimer:
THIS ISN'T AN OPTIMAL STRATEGY AT ALL! It was just an old project from when I started learning pine script!
The backtest doesn't promise the same results in the future, always do both in-sample and out-of-sample testing when backtesting a strategy. And make sure you forward test it as well before implementing it!
Furthermore this strategy uses both trend and mean-reversion systems, that is usually a no-go if you want to build robust trend systems .
Don't hesitate to comment if you have any questions or if you have some good notes for a beginner.
Đường Trung bình trượt Hàm mũ (EMA)
The OG Outback [TTF]The Outback indicator
After a major overhaul of our Outback strategy, we decided that we would make our original version available for anyone to use.
The fundamental element of this indicator is based on price action relative to a slow moving average. That said, given that price will always tend towards a moving average, we have also implemented a method for helping filter out false signals leveraging a "consolidation cloud" and fast moving average. This, coupled with references to a customized version of the Relative Strength Index (RSI), has enabled us to provide significantly higher quality signals relating to price crossing a moving average.
Note: For this version, we have only prepared a single set of conditions and alerts (as noted by the 🦘 symbols). However it's worth noting there are several variations that can be done with some fundamental technical analysis and referencing additional indicators that can take this foundation and build upon it for a substantial increase in risk/reward and profit targets.
Triple Moving Averages (Gradient, Alarm & Multi TF)Triple Moving Averages
Features:
- 7 Different MA's (RMA, SMA, EMA, 'WMA', HMA, DEMA, EMA)
- Gradient coloring
- Multi timeframe
- Crossover alarm's and alarm delay function
- Forecasting (By removing the last bar in the MA period)
Moving Average to easely identify the trend and trend strength.
Gradient coloring and personal color preferences can be made.
Alert Delay System
When timing is essentially, this helps you get the alarm just in time.
Use it with the triggers ONLY ONCE PER BAR or ONLY ONCE. Then the alarm comes before the close, but you don't have to worry about it triggering just seconds after bar open :)
Default = 15m Recomended for 1h chart
Alarm's
Get the alarms before it's actually crossing or when it crosses
*This is not a selfmade indicator but simply merging from several indicators and added alert delay function and multi timeframe support
// Credits
- BigBitsIO Script : Scripting Tutorial 6 Triple Many Moving Averages Forecasting
- PineCoders Script : Color Gradient Framework PineCoders
Price and Volume Stochastic Divergence [MW]Introduction
This indicator creates signals of interest for entering and exiting long and short positions on equities. It primarily uses up and down trends defined by the change in cumulative volume with some filtering provided by a short period exponential moving average (9 EMA by default).
Settings
Moving Average Period : The moving average over which the cumulative volume delta is calculated. Default: 14
Short Period EMA : The EMA used to represent price action, and is used to generate the EMA Delta line. Default: 27 (3*3*3)
Long Period EMA : The second EMA used to calculate the EMA Delta line. Default: 108 (2*2*3*3*3)
Stochastic K Value : The value used for stochastic curve smoothing. Default: 3
Dot Size : The diameter of the larger indicator. Default: 10
Dot Transparency : The transparency level of the outer ring of the primary BUY/SELL signal. Default: 50 (0 is opaque, 100 is transparent)
Band Distance from 0 to 100 : The upper and lower band distance. Default: 20
Calculations
The cumulative volume delta (CVD) is calculated using candle bodies and wicks. For a red candle, buying volume is calculated by multiplying the volume by the spread percentage of the average of the top and bottom wicks, while Selling Volume is calculated multiplying the volume by the spread percentage of the average of the top and bottom wicks - in addition to the spread percentage of the candle body.
For a green candle, buying volume is calculated by multiplying the volume by the spread percentage of the average of the top and bottom wicks - plus the spread percentage of the candle body - while Selling Volume is calculated using only the spread percentage average of the top and bottom wicks.
Once we have the CVD, we can then perform a stochastic calculation of the CVD value.
stochastic calculation = (current value - lowest value in period) / (highest value in period - lowest value in period)
We’ll do the same stochastic calculation for the short term EMA (27 EMA default) as well as for the difference between the short term and long term EMA.
When the stochastic CVD value is rising from zero and the short term EMA stochastic value equals 100, then it’s a major bullish signal. When the stochastic CVD value is falling from 100 and the short term EMA stochastic value equals 0, then it’s a major bearish signal.
Sometimes, after a bullish or bearish signal, the stochastic CVD will reverse direction triggering a new opposing signal.
How to Interpret
The CVD indicates when there is either more buying than selling or vice versa. A value over 50 for the stochastic CVD curve represents more buying taking place. A value below 50 represents more selling. One might intuitively believe that when there is more buying volume than selling volume that the price would follow suit. This is not always the case.
Most of the time buying volume will precede consistent price movement upwards, and selling volume will precede consistent price movement downwards. When this divergence occurs, the indicator generates a signal. When this divergence begins to fail, and buying or selling volume reverses, then another signal is generated indicating that the buying/selling impulse is headed back into the direction of price action.
These interactions are visually represented on the chart with the coral line that represents CVD, and the yellow line that represents the EMA, or the average price. When the coral line goes up and the yellow line stays down, that’s the BUY signal. When the coral line goes down and the yellow line stays up, that’s the sell signal. When the coral line switches direction, the chart generates another signal showing that volume is moving in a direction that supports the price.
The orange line represents the stochastic representation of the difference between the short EMA (27 by default) and the long EMA (108 by default). EMA differences is a method that can be used to define a trend. When a short term EMA is above a longer term EMA, that may represent a bullish trend. When it is below, that may represent a bearish trend. When all 3 lines are rising or falling in the same direction at the same time, it tends to indicate a movement that has the potential to continue.
Other Usage Notes and Limitations
It's important for traders to be aware of the limitations of any indicator and to use them as part of a broader, well-rounded trading strategy that includes risk management, fundamental analysis, and other tools that can help with reducing false signals, determining trend direction, and providing additional confirmation for a trade decision. Diversifying strategies and not relying solely on one type of indicator or analysis can help mitigate some of these risks.
This indicator can be paired with the MW Volume Impulse indicator if it is desired to see the actual buying and selling cumulative volume deltas. Also, in many cases, the BUY and SELL signals tend to correspond with Keltner Bands (ATR Bands) becoming extended. Lastly, volume weighted average price (VWAP) along with other macro events can impact price and negate signals. To view VWAP lines, you may choose to use the Multi VWAP or Multi VWAP for Gaps indicator to help ensure that the signals you see in this indicator are not being affected by VWAP lines.
Long EMA Strategy with Advanced Exit OptionsThis strategy is designed for traders seeking a trend-following system with a focus on precision and adaptability.
**Core Strategy Concept**
The essence of this strategy lies in use of Exponential Moving Averages (EMAs) to identify potential long (buy) positions based on the relative positions of short-term, medium-term, and long-term EMAs. The use of EMAs is a classic yet powerful approach to trend detection, as these indicators smooth out price data over time, emphasizing the direction of recent price movements and potentially signaling the beginning of new trends.
**Customizable Parameters**
- **EMA Periods**: Users can define the periods for three EMAs - long-term, medium-term, and short-term - allowing for a tailored approach to capture trends based on individual trading styles and market conditions.
- **Volatility Filter**: An optional Average True Range (ATR)-based volatility filter can be toggled on or off. When activated, it ensures that trades are only entered when market volatility exceeds a user-defined threshold, aiming to filter out entries during low-volatility periods which are often characterized by indecisive market movements.
- **Trailing Stop Loss**: A trailing stop loss mechanism, expressed as a percentage of the highest price achieved since entry, provides a dynamic way to manage risk by allowing profits to run while cutting losses.
- **EMA Exit Condition**: This advanced exit option enables closing positions when the short-term EMA crosses below the medium-term EMA, serving as a signal that the immediate trend may be reversing.
- **Close Below EMA Exit**: An additional exit condition, which is disabled by default, allows positions to be closed if the price closes below a user-selected EMA. This provides an extra layer of flexibility and risk management, catering to traders who prefer to exit positions based on specific EMA thresholds.
**Operational Mechanics**
Upon activation, the strategy evaluates the current price in relation to the set EMAs. A long position is considered when the current price is above the long-term EMA, and the short-term EMA is above the medium-term EMA. This setup aims to identify moments where the price momentum is strong and likely to continue.
The strategy's versatility is further enhanced by its optional settings:
- The **Volatility Filter** adjusts the sensitivity of the strategy to market movements, potentially improving the quality of the entries during volatile market conditions.
The Average True Range (ATR) is a key component of this filter, providing a measure of market volatility by calculating the average range between the high and low prices over a specified number of periods. Here's how you can adjust the volatility filter settings for various market conditions, focusing on filtering out low-volatility markets:
Setting Examples for Volatility Filter
1. High Volatility Markets (e.g., Cryptocurrencies, Certain Forex Pairs):
ATR Periods: 14 (default)
ATR Multiplier: Setting the multiplier to a lower value, such as 1.0 or 1.2, can be beneficial in high-volatility markets. This sensitivity allows the strategy to react to volatility changes more quickly, ensuring that you're entering trades during periods of significant movement.
2. Medium Volatility Markets (e.g., Major Equity Indices, Medium-Volatility Forex Pairs):
ATR Periods: 14 (default)
ATR Multiplier: A multiplier of 1.5 (default) is often suitable for medium volatility markets. It provides a balanced approach, ensuring that the strategy filters out low-volatility conditions without being overly restrictive.
3. Low Volatility Markets (e.g., Some Commodities, Low-Volatility Forex Pairs):
ATR Periods: Increasing the ATR period to 20 or 25 can smooth out the volatility measure, making it less sensitive to short-term fluctuations. This adjustment helps in focusing on more significant trends in inherently stable markets.
ATR Multiplier: Raising the multiplier to 2.0 or even 2.5 increases the threshold for volatility, effectively filtering out low-volatility conditions. This setting ensures that the strategy only triggers trades during periods of relatively higher volatility, which are more likely to result in significant price movements.
How to Use the Volatility Filter for Low-Volatility Markets
For traders specifically interested in filtering out low-volatility markets, the key is to adjust the ATR Multiplier to a higher level. This adjustment increases the threshold required for the market to be considered sufficiently volatile for trade entries. Here's a step-by-step guide:
Adjust the ATR Multiplier: Increase the ATR Multiplier to create a higher volatility threshold. A multiplier of 2.0 to 2.5 is a good starting point for very low-volatility markets.
Fine-Tune the ATR Periods: Consider lengthening the ATR calculation period if you find that the strategy is still entering trades in undesirable low-volatility conditions. A longer period provides a more averaged-out measure of volatility, which might better suit your needs.
Monitor and Adjust: Volatility is not static, and market conditions can change. Regularly review the performance of your strategy in the context of current market volatility and adjust the settings as necessary.
Backtest in Different Conditions: Before applying the strategy live, backtest it across different market conditions with your adjusted settings. This process helps ensure that your approach to filtering low-volatility conditions aligns with your trading objectives and risk tolerance.
By fine-tuning the volatility filter settings according to the specific characteristics of the market you're trading in, you can enhance the performance of this strategy
- The **Trailing Stop Loss** and **EMA Exit Conditions** provide two layers of exit strategies, focusing on capital preservation and profit maximization.
**Visualizations**
For clarity and ease of use, the strategy plots the three EMAs and, if enabled, the ATR threshold on the chart. These visual cues not only aid in decision-making but also help in understanding the market's current trend and volatility state.
**How to Use**
Traders can customize the EMA periods to fit their trading horizon, be it short, medium, or long-term trading. The volatility filter and exit options allow for further customization, making the strategy adaptable to different market conditions and personal risk tolerance levels.
By offering a blend of trend-following principles with advanced risk management features, this strategy aims to cater to a wide range of trading styles, from cautious to aggressive. Its strength lies in its flexibility, allowing traders to fine-tune settings to their specific needs, making it a potentially valuable tool in the arsenal of any trader looking for a disciplined approach to navigating the markets.
Ripster Trend LabelsRipster Trend Labels: This script provides labels indicating the trend and chop conditions in the market. It helps traders identify potential trading opportunities based on short-term trends. Its support my EMA Cloud System by providing labels on when Clouds turn bullish or bearish and also uses my concepts of Chop vs Trend based on premarket levels.
What Does Script Do-> It Identifies Bullish & Bearish Trend and if Market is in Chop Range or Trending
How Does it Identify Trend & Chop->
Description: The script calculates the 10-minute 12 and 50 Exponential Moving Averages (EMA) to determine the short-term trend direction. It's based on the Ripster 10-minute trading system, and it's recommended to use it in conjunction with the 5-12 and 34-50 Ripster Cloud scripts for more effective analysis.
For Sake of simplicity only using 12 & 50 EMA to create the labels, this should be used with the Clouds itself for better analysis.
This Script also calculates when Price is moving over premarket pivot or moving under premarket pivots. These Pivots are High or Lows in premarket in this version. The move over Pivot Signals the Trend , if Stock or ETF remains in those pivots, it is considered as Chop.
For now I am only using Premarket Data and my principles of Chop Vs Trend based on that to identify this direction. In future versions, I might implement Daily levels or Yesterday High Lows, to add more pivots for more accurately identifying chop ranges or if we are in Trend.
Table Display: The script displays the trend and chop labels in a table format on the chart, making it easier for traders to interpret the information.
How to Trade and Analyze Using Ripster Clouds Labels:
I recommend using my Ripster Clouds Indicator with these labels for best use
For High Probability Bullish Trend Trades
-> When All 3 Columns Price Action, Ripster 34/50 Clouds & Ripster 5/12 are bullish means trend is strong and its High Probability Long.
For High Probability Bearish Trend Trades
-> When All 3 Columns Price Action, Ripster 34/50 Clouds & Ripster 5/12 are bearish means trend is strong bearish and High Probability Long
Identifying Chop
-> If price action label says Chop its most likely sideways action even though other two columns are saying Bearish or Bullish. We can still trade because combination of other two is still strong Trend Signal, but its better to risk less when labels are showing Chop
By using this script, traders can make more informed decisions about entering and exiting trades based on the current market conditions identified by the trend and chop labels.
VWAP 8EMA Crossover Scalping IndicatorWhy?
Everybody, especially in Indian context, from 9:15 AM to 3:30 PM, wants to trade in BankNifty.
And even 15m is Too Big timeframe for The Great Indian Options buyers. Everyone knows how potentially BankNifty (& FinNifty on Tuesday and Sensex on Friday) can show dance within 15m.
So there always been an overarching longing among traders to have something in shorter timeframes. And this 5m timeframe, looks like a universally (sic) accepted Standard Timeframe for Indian Options traders.
So here is this.
What?
The time we are publishing this public indicator Indian market (Nifty) is in ATH at ~22200.
In any such super trending market it's always good to wait for a dip and then in suitable time, enter the trade in the direction of the larger trend. The reversal trading systems, in such a situation, proves to be ineffective.
Of course there are time when market is sideways and keeps on oscillating between +/2 standard deviation of the 20 SMA. In such a situation the reversal play works perfectly. But not so in such a trending market.
So the question comes up - after a dip what's the right point to enter.
Hence comes the importance of such a crossover based trading system.
In this indicator, it's a well-known technique (nothing originally from ours, it's taken from social media, exact one we forgot) to find out the 8EMA and VWAP crossover.
So we learned from social media, practice in our daily trading a bit, actuate it and now publishing it.
A few salient points
It does not make sense to jump into the trade just on the crossover (or crossunder).
So we added some more sugar to it, e.g. we check the color the candle. Also the next candle if crosses and closes above (or below) the breakout candle's high/low.
The polarity (color) of both the alert (breakout/breakdown) and confirmation candle to be same (green for crossover, red from crossunder).
Of course, it does provider BUY and SELL alerts separately.
These all we have found out doing backtesting and forward testing with 1/2 lots and saw this sort of approaches works.
Hence all of these are added to this script.
Nomenclature
Here green line is the 8EMA and the red line is the VWAP.
Also there is a black dotted line. That's 50 EMA. It's to show you the trend.
The recent trade is shown in the top right of the chart as green (for buy) or red (for sell) with SL and 1:1 target.
How to trade using this system?
This is roughly we have found the best possible use of this indicator.
Lets explain with a bullish BUY positive crossover (means 8EMA is crossing over the daily VWAP)
Keep timeframe as 5m
Check the direction/slope of the black dotted line (50 EMA). If it's upwards, only take bullish positions.
Open the chart which has the VWAP. (e.g. FinNifty spot or MidcapNifty spot does not have vwap). So in those cases Future is the way to go.
Wait for a breakout crossover and let the indicator gives a green, triangular UP arrow.
Draw a horizontal line to the close of that candle for next few (say 6 candles i.e. 30m) candles.
Wait for the price first to retest the 8EMA or even better the VWAP (or near to the 8EMA, VWAP)
Let the price moves and closes above the horizontal line drawn in the 4th step.
Take a bullish trade, keeping VWAP as the SL and 1:1 as the target.
Additionally, Options buyer can consult ADX also to see if the ADX is more than 25 and moving up for the bullish trade. (This has to be added seperately in the chart, it's not a part of the indicator).
Mention
The concept we have taken from some social media. Forget exactly where we heard this first time. We just coded it with some additional steps.
Statutory Disclaimer
There is no silver bullet / holy grail in trading. Nothing works 100% time. One has to be careful about the loss (s)he can bear in case of the trade goes against.
We, as the author of this script, is not responsible for any trading or position decision one is taken based on the outcome of this.
It is our sole discretion to change, add, delete the portion or withdraw the whole script without any prior notice or intimation.
In Indian Context: We are not SEBI registered.
FluxFilter Trend Strategy [BITsPIP]Hello fellow traders, I'm excited to share with you the FluxFilter Trend Strategy, a trading approach I've developed for those interested in exploring trend-following strategies. My goal was to create something straightforward and accessible, so traders looking to refine their portfolios can easily integrate its features. By the end of this guide, I hope you'll have a solid grasp of how the FluxFilter Trend Strategy functions, appreciate its benefits, understand its potential drawbacks, and see how it might fit into various trading contexts.
I) Overview
The FluxFilter Trend Strategy is tailored to align with the market's long-term trend. It examines the price data from the previous year to gauge the market's overall trajectory by employing moving averages. Subsequently, within shorter timeframes, the strategy utilizes a combination of modified Supertrend, Hull Suite, and various trend-following and filtering techniques to generate buy or sell signals. Although its advanced take profit and stop loss mechanisms might initially present a learning curve, they are integral to the strategy's effectiveness. They are designed to secure gains by capturing prevailing trends and mitigating the impact of false reversal signals.
II) Deep Backtesting
Deep backtesting stands as a cornerstone in the development of trading strategies, offering a robust method for traders to assess the performance of their strategy against historical data. This process yields a retrospective view, illustrating how the strategy might have navigated through past market fluctuations, thereby shedding light on its potential robustness and areas for refinement. However, it's crucial to acknowledge that a strategy's performance can be influenced by a myriad of factors including market dynamics, the chosen timeframe, and the inherent attributes of the traded asset. Consequently, it's advisable to conduct thorough backtesting under various conditions to ascertain the strategy's reliability before applying it to actual trading scenarios.
III) Benefits
A primary advantage of the FluxFilter Trend Strategy is its proficiency in discerning genuine market trends from mere price fluctuations, thereby avoiding premature or uncertain trades. Unlike approaches that take high risks on speculative trades, this strategy prioritizes a high degree of confidence in the direction of the trade. It meticulously waits for a clear confirmation of the market trend. Once this certainty is established, the strategy promptly generates trade signals, ensuring that traders are positioned to capitalize on optimal market entry points without delay. This approach not only enhances the potential for profit but also aligns with a disciplined and methodical trading ethos.
IV) Applications
FluxFilter Trend Strategy can be applied across various timeframes, with a particular efficacy in those under 15 minutes. Its adaptable framework means it can be customized to cater to a variety of asset classes, encompassing stocks, commodities, forex, and cryptocurrencies. Initially, the strategy was specifically calibrated for low-volatile cryptocurrencies, as reflected in the default settings for stop loss and take profit values. It's important to recognize that the unique volatility and trend patterns of your selected market necessitate careful adjustments to these parameters. This fine-tuning of profit targets and stop loss thresholds is crucial for aligning the strategy with the specific dynamics of your chosen market, which I will discuss shortly.
V) Strategy's Logic
1. Trend Identification: My conviction lies in the power of trend trading to yield long-term gains. Central to the FluxFilter Trend Strategy is the Hull Suite indicator, a tool developed by InSilico, serving as one of the confirmation indicators. This indicator acts as a compass for trend direction; a price residing above the Hull Suite line signals an uptrend, potentially marking an entry point for a buy position or confirming it. In contrast, a price positioned below this line suggests a downtrend, potentially indicating a strategic moment to sell or confirming the sell.
2. Noise Reduction: The financial markets are known for their 'noise'—short-lived price movements that can obscure the true market direction. The FluxFilter Trend Strategy is designed to sift through this noise, thereby facilitating more lucid and informed trading decisions. It employs a set of straightforward yet innovative techniques to single out significant misleading fluctuations. This is achieved by analyzing recent bars to spot bars with unusually large bodies, which often represent misleading market noise.
3. Risk Management: A key facet of the strategy is its emphasis on pragmatic risk management. Traders are empowered to establish practical stop-loss and take-profit levels, tailoring these crucial parameters to the specific market they are engaging in. This customization is instrumental in optimizing long-term profitability, ensuring that the strategy adapts fluidly to the unique characteristics and volatility patterns of different trading environments.
VI) Strategy's Input Settings and Default Values
1. Modified Supertrend
i. Factor: Serving as a multiplier in the Average True Range (ATR) calculation, this parameter adjusts the distance of the Supertrend line relative to the price chart. Elevating the factor value widens the gap between the Supertrend line and price, offering a more conservative stance. On the flip side, diminishing the factor value pulls the Supertrend line closer to the price action, heightening its sensitivity. While the preset value is 1, you have the flexibility to modify this to suit your trading approach.
ii. ATR Length: This defines the count of bars that are incorporated into the ATR computation, directly influencing the Supertrend's adaptability to market changes. With a default setting of 30 bars, it strikes a balance, smoothing over short-term fluctuations while maintaining a meaningful sensitivity to market trends. Adjusting this parameter allows you to tailor the indicator's responsiveness to suit your trading strategy, considering the volatility and behavioral patterns of the asset you are trading.
2. Hull Suite
i. Hull Suite Length: Designed for capturing long-term trends, the Hull Suite Length is configured at 1000. Functioning comparably to moving averages, the Hull Suite features upper and lower bands, though these are not employed in our current strategy.
ii. Length Multiplier: It's advisable to maintain a minimal value for the Length Multiplier, prioritizing the optimization of the Hull Suite Length. Presently, it is set to 1.
3. Filtering Indicators
i. Fluctuation Filtering Percentage: It's advisable to set this parameter to ten times the size of the average bar in your specific market, as this helps effectively mitigate the impact of market fluctuations. While the initial default is 0.4(%), based on the BTCUSDT market, it's crucial to adjust this figure to align with the characteristics of different assets or markets you're trading in.
ii. Fluctuation Filtering Bars: This parameter designates the count of preceding bars to consider when assessing market fluctuations. It's fully customizable, allowing you to tailor it based on your market insights. The preset default is 3, a balance chosen to minimize susceptibility to potentially misleading signals.
iii. Trend Confirmation Percentage: This metric is pivotal for verifying the viability of a trend post-entry. If the trade doesn't achieve this percentage in profit, it indicates a deviation from the expected trend. Under such circumstances, it may be prudent to exit the trade prematurely rather than awaiting the stop-loss trigger. It's recommended to set this parameter at half the size of the average candle body for the market you're analyzing. The initial default is set at 0.2(%).
4. StopLoss and TakeProfit
i. StopLoss and TakeProfit Settings: Two distinct approaches are available. Semi-Automatic StopLoss/TakeProfit Setting and Manual StopLoss/TakeProfit Setting. The Semi-Automatic mode streamlines the process by allowing you to input values for a 5-minute timeframe, subsequently auto-adjusting these values across various timeframes, both lower and higher. Conversely, the Manual mode offers full control, enabling you to meticulously define TakeProfit values for each individual timeframe.
ii. TakeProfit Threshold # and TakeProfit Value #: Imagine this mechanism as an ascending staircase. Each step represents a range, with the lower boundary (TakeProfit Value) designed to close the trade upon being reached, and the upper boundary (TakeProfit Threshold) upon being hit, propelling the trade to the next level, and forming a new range. This stair-stepping approach enhances risk management and has the potential to increase profitability. The pre-set configurations are tailored for volatile markets, such as BTCUSDT. It's advisable to devote time to tailoring these settings to your specific market, aiming to achieve optimal results based on backtesting.
iii. StopLoss Value: In line with its name, this value marks the limit of loss you're prepared to accept should the market trend go against your expectations. It's crucial to note that once your asset reaches the first TakeProfit range, the initial StopLoss value becomes obsolete, supplanted by the first TakeProfit Value. The default StopLoss value is pegged at 1.8(%), a figure worth considering in your trading strategy.
VII) Entry Conditions
The principal element that triggers the signal is the Modified Supertrend. Additional indicators serve as confirmatory tools. Nonetheless, to refine your strategy effectively, it's crucial to fine-tune the parameters. This involves adjusting input variables such as take profit levels, threshold parameters, and the filtering values discussed previously.
VIII) Exit Conditions
The strategy stipulates exit conditions primarily governed by stop loss and take profit parameters. On infrequent occasions, if the trend lacks confirmation post-entry, the strategy mandates an exit upon the issuance of a reverse signal (whether confirmed or unconfirmed) by the strategy itself.
Good Luck!!
Smart DCA StrategyINSPIRATION
While Dollar Cost Averaging (DCA) is a popular and stress-free investment approach, I noticed an opportunity for enhancement. Standard DCA involves buying consistently, regardless of market conditions, which can sometimes mean missing out on optimal investment opportunities. This led me to develop the Smart DCA Strategy – a 'set and forget' method like traditional DCA, but with an intelligent twist to boost its effectiveness.
The goal was to build something more profitable than a standard DCA strategy so it was equally important that this indicator could backtest its own results in an A/B test manner against the regular DCA strategy.
WHY IS IT SMART?
The key to this strategy is its dynamic approach: buying aggressively when the market shows signs of being oversold, and sitting on the sidelines when it's not. This approach aims to optimize entry points, enhancing the potential for better returns while maintaining the simplicity and low stress of DCA.
WHAT THIS STRATEGY IS, AND IS NOT
This is an investment style strategy. It is designed to improve upon the common standard DCA investment strategy. It is therefore NOT a day trading strategy. Feel free to experiment with various timeframes, but it was designed to be used on a daily timeframe and that's how I recommend it to be used.
You may also go months without any buy signals during bull markets, but remember that is exactly the point of the strategy - to keep your buying power on the sidelines until the markets have significantly pulled back. You need to be patient and trust in the historical backtesting you have performed.
HOW IT WORKS
The Smart DCA Strategy leverages a creative approach to using Moving Averages to identify the most opportune moments to buy. A trigger occurs when a daily candle, in its entirety including the high wick, closes below the threshold line or box plotted on the chart. The indicator is designed to facilitate both backtesting and live trading.
HOW TO USE
Settings:
The input parameters for tuning have been intentionally simplified in an effort to prevent users falling into the overfitting trap.
The main control is the Buying strictness scale setting. Setting this to a lower value will provide more buying days (less strict) while higher values mean less buying days (more strict). In my testing I've found level 9 to provide good all round results.
Validation days is a setting to prevent triggering entries until the asset has spent a given number of days (candles) in the overbought state. Increasing this makes entries stricter. I've found 0 to give the best results across most assets.
In the backtest settings you can also configure how much to buy for each day an entry triggers. Blind buy size is the amount you would buy every day in a standard DCA strategy. Smart buy size is the amount you would buy each day a Smart DCA entry is triggered.
You can also experiment with backtesting your strategy over different historical datasets by using the Start date and End date settings. The results table will not calculate for any trades outside what you've set in the date range settings.
Backtesting:
When backtesting you should use the results table on the top right to tune and optimise the results of your strategy. As with all backtests, be careful to avoid overfitting the parameters. It's better to have a setup which works well across many currencies and historical periods than a setup which is excellent on one dataset but bad on most others. This gives a much higher probability that it will be effective when you move to live trading.
The results table provides a clear visual representation as to which strategy, standard or smart, is more profitable for the given dataset. You will notice the columns are dynamically coloured red and green. Their colour changes based on which strategy is more profitable in the A/B style backtest - green wins, red loses. The key metrics to focus on are GOA (Gain on Account) and Avg Cost .
Live Trading:
After you've finished backtesting you can proceed with configuring your alerts for live trading.
But first, you need to estimate the amount you should buy on each Smart DCA entry. We can use the Total invested row in the results table to calculate this. Assuming we're looking to trade on BITSTAMP:BTCUSD
Decide how much USD you would spend each day to buy BTC if you were using a standard DCA strategy. Lets say that is $5 per day
Enter that USD amount in the Blind buy size settings box
Check the Blind Buy column in the results table. If we set the backtest date range to the last 10 years, we would expect the amount spent on blind buys over 10 years to be $18,250 given $5 each day
Next we need to tweak the value of the Smart buy size parameter in setting to get it as close as we can to the Total Invested amount for Blind Buy
By following this approach it means we will invest roughly the same amount into our Smart DCA strategy as we would have into a standard DCA strategy over any given time period.
After you have calculated the Smart buy size , you can go ahead and set up alerts on Smart DCA buy triggers.
BOT AUTOMATION
In an effort to maintain the 'set and forget' stress-free benefits of a standard DCA strategy, I have set my personal Smart DCA Strategy up to be automated. The bot runs on AWS and I have a fully functional project for the bot on my GitHub account. Just reach out if you would like me to point you towards it. You can also hook this into any other 3rd party trade automation system of your choice using the pre-configured alerts within the indicator.
PLANNED FUTURE DEVELOPMENTS
Currently this is purely an accumulation strategy. It does not have any sell signals right now but I have ideas on how I will build upon it to incorporate an algorithm for selling. The strategy should gradually offload profits in bull markets which generates more USD which gives more buying power to rinse and repeat the same process in the next cycle only with a bigger starting capital. Watch this space!
MARKETS
Crypto:
This strategy has been specifically built to work on the crypto markets. It has been developed, backtested and tuned against crypto markets and I personally only run it on crypto markets to accumulate more of the coins I believe in for the long term. In the section below I will provide some backtest results from some of the top crypto assets.
Stocks:
I've found it is generally more profitable than a standard DCA strategy on the majority of stocks, however the results proved to be a lot more impressive on crypto. This is mainly due to the volatility and cycles found in crypto markets. The strategy makes its profits from capitalising on pullbacks in price. Good stocks on the other hand tend to move up and to the right with less significant pullbacks, therefore giving this strategy less opportunity to flourish.
Forex:
As this is an accumulation style investment strategy, I do not recommend that you use it to trade Forex.
STRATEGY IN ACTION
Here you see the indicator running on the BITSTAMP:BTCUSD pair. You can read the indicator as follows:
Vertical green bands on historical candles represents where buy signals triggered in the past
Table on the top right represents the results of the A/B backtest against a standard DCA strategy
Green Smart Buy column shows that Smart DCA was more profitable than standard DCA on this backtest. That is shown by the percentage GOA (Gain on Account) and the Avg Cost
Smart Buy Zone label marks the threshold which the entire candle must be below to trigger a buy signal (line can be changed to a box under plotting settings)
Green color of Smart Buy Zone label represents that the open candle is still valid for a buy signal. A signal will only be generated if the candle closes while this label is still green
Below is the same BITSTAMP:BTCUSD chart a couple of days later. Notice how the threshold has been broken and the Smart Buy Zone label has turned from green to red. No buy signal can be triggered for this day - even if the candle retraced and closed below the threshold before daily candle close.
Notice how the green vertical bands tend to be present after significant pullbacks in price. This is the reason the strategy works! Below is the same BITSTAMP:BTCUSD chart, but this time zoomed out to present a clearer picture of the times it would invest vs times it would sit out of the market. You will notice it invests heavily in bear markets and significant pullbacks, and does not buy anything during bull markets.
Finally, to visually demonstrate the indicator on an asset other than BTC, here is an example on CRYPTO:ETHUSD . In this case the current daily high has not touched the threshold so it is still possible for this to be a valid buy trigger on daily candle close. The vertical green band will not print until the buy trigger is confirmed.
BACKTEST RESULTS
Now for some backtest results to demonstrate the improved performance over a standard DCA strategy using all non-stablecoin assets in the top 30 cryptos by marketcap.
I've used the TradingView ticker (exchange name denoted as CRYPTO in the symbol search) for every symbol tested with the exception of BTCUSD because there was some dodgy data at the beginning of the TradingView BTCUSD chart which overinflated the effectiveness of the Smart DCA strategy on that ticker. For BTCUSD I've used the BITSTAMP exchange data. The symbol links below will take you to the correct chart and exchange used for the test.
I'm using the GOA (Gain on Account) values to present how each strategy performed.
The value on the left side is the standard DCA result and the right is the Smart DCA result.
✅ means Smart DCA strategy outperformed the standard DCA strategy
❌ means standard DCA strategy outperformed the Smart DCA strategy
To avoid overfitting, and to prove that this strategy does not suffer from overfitting, I've used the exact same input parameters for every symbol tested below. The settings used in these backtests are:
Buying strictness scale: 9
Validation days: 0
You can absolutely tweak the values per symbol to further improve the results of each, however I think using identical settings on every pair tested demonstrates a higher likelihood that the results will be similar in the live markets.
I'm presenting results for two time periods:
First price data available for trading pair -> closing candle on Friday 26th Jan 2024 (ALL TIME)
Opening candle on Sunday 1st Jan 2023 -> closing candle on Friday 26th Jan 2024 (JAN 2023 -> JAN 2024)
ALL TIME:
BITSTAMP:BTCUSD 80,884% / 133,582% ✅
CRYPTO:ETHUSD 17,231% / 36,146% ✅
CRYPTO:BNBUSD 5,314% / 2,702% ❌
CRYPTO:SOLUSD 1,745% / 1,171% ❌
CRYPTO:XRPUSD 2,585% / 4,544% ✅
CRYPTO:ADAUSD 338% / 353% ✅
CRYPTO:AVAXUSD 130% / 160% ✅
CRYPTO:DOGEUSD 13,690% / 16,432% ✅
CRYPTO:TRXUSD 414% / 466% ✅
CRYPTO:DOTUSD -16% / -7% ✅
CRYPTO:LINKUSD 1,161% / 2,164% ✅
CRYPTO:TONUSD 25% / 47% ✅
CRYPTO:MATICUSD 1,769% / 1,587% ❌
CRYPTO:ICPUSD 70% / 50% ❌
CRYPTO:SHIBUSD -20% / -19% ✅
CRYPTO:LTCUSD 486% / 718% ✅
CRYPTO:BCHUSD -4% / 3% ✅
CRYPTO:LEOUSD 102% / 151% ✅
CRYPTO:ATOMUSD 46% / 91% ✅
CRYPTO:UNIUSD -16% / 1% ✅
CRYPTO:ETCUSD 283% / 414% ✅
CRYPTO:OKBUSD 1,286% / 1,935% ✅
CRYPTO:XLMUSD 1,471% / 1,592% ✅
CRYPTO:INJUSD 830% / 1,035% ✅
CRYPTO:OPUSD 138% / 195% ✅
CRYPTO:NEARUSD 23% / 44% ✅
Backtest result analysis:
Assuming we have an initial investment amount of $10,000 spread evenly across each asset since the creation of each asset, it would have provided the following results.
Standard DCA Strategy results:
Average percent return: 4,998.65%
Profit: $499,865
Closing balance: $509,865
Smart DCA Strategy results:
Average percent return: 7,906.03%
Profit: $790,603
Closing balance: $800,603
JAN 2023 -> JAN 2024:
BITSTAMP:BTCUSD 47% / 66% ✅
CRYPTO:ETHUSD 26% / 33% ✅
CRYPTO:BNBUSD 15% / 17% ✅
CRYPTO:SOLUSD 272% / 394% ✅
CRYPTO:XRPUSD 7% / 12% ✅
CRYPTO:ADAUSD 43% / 59% ✅
CRYPTO:AVAXUSD 116% / 151% ✅
CRYPTO:DOGEUSD 8% / 14% ✅
CRYPTO:TRXUSD 48% / 65% ✅
CRYPTO:DOTUSD 24% / 35% ✅
CRYPTO:LINKUSD 83% / 124% ✅
CRYPTO:TONUSD 7% / 21% ✅
CRYPTO:MATICUSD -3% / 7% ✅
CRYPTO:ICPUSD 161% / 196% ✅
CRYPTO:SHIBUSD 1% / 8% ✅
CRYPTO:LTCUSD -15% / -7% ✅
CRYPTO:BCHUSD 47% / 68% ✅
CRYPTO:LEOUSD 9% / 11% ✅
CRYPTO:ATOMUSD 1% / 15% ✅
CRYPTO:UNIUSD 9% / 23% ✅
CRYPTO:ETCUSD 27% / 40% ✅
CRYPTO:OKBUSD 21% / 30% ✅
CRYPTO:XLMUSD 11% / 19% ✅
CRYPTO:INJUSD 477% / 446% ❌
CRYPTO:OPUSD 77% / 91% ✅
CRYPTO:NEARUSD 78% / 95% ✅
Backtest result analysis:
Assuming we have an initial investment amount of $10,000 spread evenly across each asset for the duration of 2023, it would have provided the following results.
Standard DCA Strategy results:
Average percent return: 61.42%
Profit: $6,142
Closing balance: $16,142
Smart DCA Strategy results:
Average percent return: 78.19%
Profit: $7,819
Closing balance: $17,819
DSKOLI TableThis helps to determine bullish or bearish trend of any chart on any generally available time-frame and good to have for Intraday watch.
Details -
a. Points shown in table shows the difference of last shown price from specified EMAs, this helps to know the price movement of candles are above or below the EMA and its coloured with red and green which even further helps to determine its existing trend.
Note or Disclaimer:
1. This may be considered only for Watching as Learning and informational purpose.
2. Take advice from financial advisor before entering, holding, converting or exiting from any order or trade.
3. Always keep your acceptable stop-loss in all your transactions while trading or investing.
DSKOLI or TradingView reserves all right and don't hold any responsibilities for any loss/losses as well as accuracy of levels or price movement.
Multi MAs mit LabelA MA (Moving Average) is useful to identify a trend of an assets. The TradingView builtin indicator "Exponential Moving Average" is useful, but limited in some aspects:
Bound to the active timeframe (e.g. h1)
One MA per indicator instance. Makes it confusing when using multiple
In reality to want to have multiple MAs with different types (EMA, SMA), length and timeframes on your chart to identify trading opportunities. As an example you can use the daily EMA12 and EMA21 to identify the trend and EMA200 on the h4 to enter a trade. That's what this script is used for.
The provided script is an extension to the indicator powered by chipmonk (link to profile below). The original script let you add up to 8 EMAs that can be bound to any timeframe and length. The timeframe and length is displayed on the chart next to EMA.
Unfortunately you can only add EMAs (Exponential Moving Averages) and no SMAs (Simple Moving Averages). That's why the script was extended. You can now choose the type (EMA or SMA) for up to 8 MAs.
Links
Profile of chipmonk
Indicator by chipmonk
Trend Change IndicatorThe Trend Change Indicator is an all-in-one, user-friendly trend-following tool designed to identify bullish and bearish trends in asset prices. It features adjustable input values and a built-in alert system that promptly notifies investors of potential shifts in both short-term and long-term price trends. This alert system is crucial for helping less active investors correctly position themselves ahead of major trend shifts and assists in risk management after a trend is established. It's important to note that this indicator is most effective with assets that historically exhibit strong trends.
At the heart of this tool is the interaction between the 30-day and 60-day Exponential Moving Averages (EMA). A bullish trend is indicated in green when the 30-day EMA is above the 60-day EMA, while a bearish trend is signaled in red when the 30-day EMA is below the 60-day EMA. The appearance of gray alerts users to potential shifts in the current trend as the EMAs converge, falling below the Average True Range (ATR) safety margin. This analysis is conducted across both hourly and daily timeframes, with the 4-hour timeframe providing early signals for daily trend changes. The band visually represents the interaction between the daily EMAs and is also displayed in the second row of the table, with the first row showing the same EMA interaction on the 4-hour timeframe.
This indicator also includes a 140-day (20-week) Simple Moving Average (SMA), visually represented by a line with predictive dots. This feature significantly enhances the investor's ability to understand long-term trends in asset prices, offering forward-looking insights by projecting the SMA value 10 days into the future. The value of this forecast lies in interpreting the slope of the dots; upward trending dots suggest a bullish underlying trend, while downward trending dots indicate a bearish trend. Generally, prices above the SMA signal bullishness, and prices below indicate bearishness.
In summary, the Trend Change Indicator is a comprehensive solution for identifying price trends and managing risk. Its intuitive, color-coded design makes it an indispensable tool for traders and investors who aim to be well-positioned ahead of trend shifts and manage risk once a trend has been established. While it has proven historically valuable in trending markets such as cryptocurrencies, tech stocks, and commodities, it is advisable to use this indicator in conjunction with other technical analysis tools for a more comprehensive and well-rounded decision-making process.
Bitcoin ETFs Clustered EMA [UOI]The 'Bitcoin ETFs Clustered EMA ' is designed to track and analyze the combined movement of various Bitcoin-related ETFs. This indicator incorporates a range of prominent ETFs, including iShares Bitcoin (IBIT), Bitwise Bitcoin (BITB), Tidal Bitcoin (DEFI), ARK Bitcoin (ARKB), Grayscale Bitcoin (GBTC), Fidelity Bitcoin (FBTC), WisdomTree Bitcoin (BTCW), Invesco Bitcoin (BTCO), Valkyrie Bitcoin (BRRR), VanEck Bitcoin (HODL), and Franklin Bitcoin (EZBC). By normalizing their prices to a unified scale and applying Exponential Moving Averages (EMAs) of different lengths (Short, Long, and Extra Long), it provides a comprehensive view of the aggregated trend strength and direction in the Bitcoin ETF market. Its color-coded plotting system offers quick visual cues for market sentiment, making it an invaluable tool for traders focusing on Bitcoin-related securities.
Apply this indicator to the charts of NASDAQ:MARA or AMEX:SPY to see how you can effectively trade these ETFs.
Remember, these do not trade 24/7, so when applied to a Bitcoin chart, the indicator only properly shows during regular trading hours. Also, since these ETFs were recently launched, don't expect them to work properly on longer timeframes like the daily chart. You need to use it on lower timeframes; otherwise, the EMAs may not display correctly. As time passes, you will be able to use it on higher timeframes.
EMA + Lower Timeframe EMA (correct display in Replay Mode)This indicator shows
one EMA for the current timeframe
one EMA for a lower timeframe
Unlike the built-in Tradingview EMA indicator, this indicator shows the correct values for the lower timeframe EMA during Replay Mode.
{Gunzo} Trend Sniper (Multiple MAs with coefficient)Updated GUNZO's Trend Sniper script by adding in different MA types to choose from. This can help reduce false signals and sharpen the trend reversal points.
Here's a summary of the key changes:
1. Multiple Moving Average Types: The original script was focused solely on the Weighted Moving Average (WMA) with a coefficient. The updated script introduces flexibility by allowing users to choose from a variety of Moving Average types, including WMA, VWMA (Volume Weighted Moving Average), EMA (Exponential Moving Average), SMA (Simple Moving Average), HullMA (Hull Moving Average), TEMA (Triple Exponential Moving Average), DEMA (Double Exponential Moving Average), T3, and RMA (Running Moving Average).
2. Coefficient Integration: In the original script, the coefficient was specifically designed for the WMA calculation. The updated script extends this concept to all the selected Moving Average types. This coefficient is applied differently depending on the type of MA, often affecting the length of the MA calculation.
3. Dynamic Length Calculation: For MAs that traditionally use an integer length (like SMA, EMA, etc.), the updated script calculates this length dynamically by multiplying the user-defined length by the coefficient and then rounding it to the nearest integer. This ensures compatibility with Pine Script's requirements for these functions.
All credits to GUNZO
original script:
Zero-lag Volatility-Breakout EMA Trend StrategyThis is a simple volatility-breakout strategy which uses the difference in two different zero-lag* EMAs (explained below on what exactly I mean by this) to track the upwards or downwards strength of an instrument. When the difference breaks above a Bollinger Band of a configurable standard deviation multiple, the strategy enters based off the direction of the base EMA used (i.e. if the difference breaks above and the current EMA is rising, a long entry is produced. If the difference breaks above and the current EMA is falling, a short entry is produced).
The two EMA-type metrics used to calculate the volatility difference are calculated by the following formula:
top_ema = math.max(src, ta.ema(src, length))
bottom_ema = math.min(src, ta.ema(src, length))
ema_difference = (top_ema - bottom_ema) - 1
This produces a difference which responds immediately to large price movements, instead of lagging if it used strictly the EMA itself.
SETTINGS
Source : The source of the strategy - close, hlc3, another indicator plot, etc.
EMA Difference Length : The length of both the EMA difference statistics and the base EMA used to calculate the entry side.
Standard Deviation Multiple : The Bollinger Bands multiple used when the difference is breaking out.
Use Binary Strategy : The strategy has two configurations: Binary and Rapid-Exit. 'Binary' means that it will not close a long position until a short position is generated, and vice-versa. 'Rapid-Exit' will close a long or short position once the difference reaches the middle Bollinger Band MA. This means that turning on 'Binary' will expose you to more market risk, but potentially greater market return. Turning off 'Binary' will exit quickly and reduce drawdown.
The strategy results below use 10% equity and 0.1% fees per trade.
CARNAC Magic DCAThe "CARNAC Magic DCA" indicator is designed for investors looking for the best opportunities for Dollar-Cost Averaging (DCA).
How it works:
The Carnac Dynamic DCA Threshold calculates a dynamic threshold for DCA entries using Exponential Moving Average (EMA), Average True Range (ATR), and the maximum distance from the EMA over a full lookback period, aiding in identifying optimal buy opportunities. It also only signals a DCA buying opportunity after a bearish candle, which helps lower the average DCA price.
Configurable Inputs:
EMA Start Length: Sets the initial length for the series of EMAs, affecting their sensitivity to price changes.
ATR Length: Determines the period for the ATR calculation, influencing the dynamic DCA threshold's responsiveness to market volatility.
ATR Multiplier: Modifies the impact of the ATR on the DCA threshold, allowing for finer control over the threshold's sensitivity to volatility.
Start Calculation From: Enables setting a specific start date for calculations, tailoring the analysis to a particular trading period.
DCA Buy Signal Alert: Generates an alert when the price is below both the dynamic DCA threshold and the opening price, indicating a potential buy signal based on DCA strategy.
Ten EMAs: Carnac Magic DCA includes a ten EMA plot, which decrease in length from the user-defined starting length, offering a multi-layered trend analysis.
EMA Color Coding: The sequential arrangement of EMAs is visually represented through color coding, facilitating quick trend recognition.
Average Buy Price Analysis: Calculates and displays the average buy price and its percentage difference from the average closing price since the user-defined start date, helping assess the strategy’s effectiveness compared to traditional DCA methods (purchasing at the close of every candle).
Visual Indicators and Labels: Includes visual alerts for buy signals and informative labels showing average buy prices and related statistics.
PlayBit EMAPlayBit EMA Indicator
Introducing the PlayBit EMA, a highly esteemed technical analysis tool within the PlayBit Community and a personal favorite of Bitcoin Playboy. This indicator has cemented its place as a staple among traders for its simplicity and effectiveness.
Key Features:
PB EMA: Utilizes two Exponential Moving Averages (EMAs) to identify support and resistance zones and help identify potential reversal points.
Dynamic Fill Color:
The fill color will change based on if the closing price is above, below, or in between.
This indicator is not only a reflection of market dynamics but also an essential tool for traders looking to make informed decisions based on the relationship between price action and moving averages. Whether you're a seasoned trader or just starting out, the PlayBit EMA is an invaluable addition to your trading arsenal.
CARNAC Elasticity IndicatorThe CARNAC Elasticity Indicator (EI) is a technical analysis tool designed for traders and investors using TradingView. It calculates the percentage deviation of the current price from an Exponential Moving Average (EMA) and helps traders identify potential overbought and oversold conditions in a financial instrument.
Key Features:
EMA Length: Users can customize the length of the Exponential Moving Average (EMA) used in the calculations by adjusting the "EMA Length" parameter in the indicator settings.
Percentage Deviation: The indicator calculates the percentage deviation of the current price from the EMA. Positive values indicate prices above the EMA, while negative values indicate prices below the EMA.
Maximum Deviations: The indicator tracks the maximum positive (above EMA) and negative (below EMA) percentage deviations over time, allowing traders to monitor extreme price movements.
Bands: Upper and lower bands are displayed on the indicator chart at 100 and -100, respectively. Additionally, dashed middle bands at 50 and -50 provide reference points for moderate deviations.
Dynamic Color Coding: The indicator uses dynamic color coding to highlight the current percentage deviation. It turns red for values above 50 (indicating potential overbought conditions), green for values below -50 (indicating potential oversold conditions), and purple for values in between.
How to Use:
Overbought Conditions: Watch for the percentage deviation to cross above 50, indicating potential overbought conditions. This might be a signal to consider selling or taking profits.
Oversold Conditions: Look for the percentage deviation to cross below -50, signaling potential oversold conditions. This could be an opportunity to consider buying or entering a long position.
Historical Extremes: Keep an eye on the upper and lower bands (100 and -100) to identify historical extremes in percentage deviation.
The CARNAC Elasticity Indicator can be a valuable tool for traders seeking to identify potential trend reversals and assess the strength of price movements. However, it should be used in conjunction with other technical analysis tools and risk management strategies for comprehensive trading decisions.
The Flash-Strategy with Minervini Stage Analysis QualifierThe Flash-Strategy (Momentum-RSI, EMA-crossover, ATR) with Minervini Stage Analysis Qualifier
Introduction
Welcome to a comprehensive guide on a cutting-edge trading strategy I've developed, designed for the modern trader seeking an edge in today's dynamic markets. This strategy, which I've honed through my years of experience in the trading arena, stands out for its unique blend of technical analysis and market intuition, tailored specifically for use on the TradingView platform.
As a trader with a deep passion for the financial markets, my journey began several years ago, driven by a relentless pursuit of a trading methodology that is both effective and adaptable. My background in trading spans various market conditions and asset classes, providing me with a rich tapestry of experiences from which to draw. This strategy is the culmination of that journey, embodying the lessons learned and insights gained along the way.
The cornerstone of this strategy lies in its ability to generate precise long signals in a Stage 2 uptrend and equally accurate short signals in a Stage 4 downtrend. This approach is rooted in the principles of trend following and momentum trading, harnessing the power of key indicators such as the Momentum-RSI, EMA Crossover, and Average True Range (ATR). What sets this strategy apart is its meticulous design, which allows it to adapt to the ever-changing market conditions, providing traders with a robust tool for navigating both bullish and bearish scenarios.
This strategy was born out of a desire to create a trading system that is not only highly effective in identifying potential trade setups but also straightforward enough to be implemented by traders of varying skill levels. It's a reflection of my belief that successful trading hinges on clarity, precision, and disciplined execution. Whether you are a seasoned trader or just beginning your journey, this guide aims to provide you with a comprehensive understanding of how to harness the full potential of this strategy in your trading endeavors.
In the following sections, we will delve deeper into the mechanics of the strategy, its implementation, and how to make the most out of its features. Join me as we explore the nuances of a strategy that is designed to elevate your trading to the next level.
Stage-Specific Signal Generation
A distinctive feature of this trading strategy is its focus on generating long signals exclusively during Stage 2 uptrends and short signals during Stage 4 downtrends. This approach is based on the widely recognized market cycle theory, which divides the market into four stages: Stage 1 (accumulation), Stage 2 (uptrend), Stage 3 (distribution), and Stage 4 (downtrend). By aligning the signal generation with these specific stages, the strategy aims to capitalize on the most dynamic and clear-cut market movements, thereby enhancing the potential for profitable trades.
1. Long Signals in Stage 2 Uptrends
• Characteristics of Stage 2: Stage 2 is characterized by a strong uptrend, where prices are consistently rising. This stage typically follows a period of accumulation (Stage 1) and is marked by increased investor interest and bullish sentiment in the market.
• Criteria for Long Signal Generation: Long signals are generated during this stage when the technical indicators align with the characteristics of a Stage 2 uptrend.
• Rationale for Stage-Specific Signals: By focusing on Stage 2 for long trades, the strategy seeks to enter positions during the phase of strong upward momentum, thus riding the wave of rising prices and investor optimism. This stage-specific approach minimizes exposure to less predictable market phases, like the consolidation in Stage 1 or the indecision in Stage 3.
2. Short Signals in Stage 4 Downtrends
• Characteristics of Stage 4: Stage 4 is identified by a pronounced downtrend, with declining prices indicating prevailing bearish sentiment. This stage typically follows the distribution phase (Stage 3) and is characterized by increasing selling pressure.
• Criteria for Short Signal Generation: Short signals are generated in this stage when the indicators reflect a strong bearish trend.
• Rationale for Stage-Specific Signals: Targeting Stage 4 for shorting capitalizes on the market's downward momentum. This tactic aligns with the natural market cycle, allowing traders to exploit the downward price movements effectively. By doing so, the strategy avoids the potential pitfalls of shorting during the early or late stages of the market cycle, where trends are less defined and more susceptible to reversals.
In conclusion, the strategy’s emphasis on stage-specific signal generation is a testament to its sophisticated understanding of market dynamics. By tailoring the long and short signals to Stages 2 and 4, respectively, it leverages the most compelling phases of the market cycle, offering traders a clear and structured approach to aligning their trades with dominant market trends.
Strategy Overview
At the heart of this trading strategy is a philosophy centered around capturing market momentum and trend efficiency. The core objective is to identify and capitalize on clear uptrends and downtrends, thereby allowing traders to position themselves in sync with the market's prevailing direction. This approach is grounded in the belief that aligning trades with these dominant market forces can lead to more consistent and profitable outcomes.
The strategy is built on three foundational components, each playing a critical role in the decision-making process:
1. Momentum-RSI (Relative Strength Index): The Momentum-RSI is a pivotal element of this strategy. It's an enhanced version of the traditional RSI, fine-tuned to better capture the strength and velocity of market trends. By measuring the speed and change of price movements, the Momentum-RSI provides invaluable insights into whether a market is potentially overbought or oversold, suggesting possible entry and exit points. This indicator is especially effective in filtering out noise and focusing on substantial market moves.
2. EMA (Exponential Moving Average) Crossover: The EMA Crossover is a crucial component for trend identification. This strategy employs two EMAs with different timeframes to determine the market trend. When the shorter-term EMA crosses above the longer-term EMA, it signals an emerging uptrend, suggesting a potential long entry. Conversely, a crossover below indicates a possible downtrend, hinting at a short entry opportunity. This simple yet powerful tool is key in confirming trend directions and timing market entries.
3. ATR (Average True Range): The ATR is instrumental in assessing market volatility. This indicator helps in understanding the average range of price movements over a given period, thus providing a sense of how much a market might move on a typical day. In this strategy, the ATR is used to adjust stop-loss levels and to gauge the potential risk and reward of trades. It allows for more informed decisions by aligning trade management techniques with the current volatility conditions.
The synergy of these three components – the Momentum-RSI, EMA Crossover, and ATR – creates a robust framework for this trading strategy. By combining momentum analysis, trend identification, and volatility assessment, the strategy offers a comprehensive approach to navigating the markets. Whether it's capturing a strong trend in its early stages or identifying a potential reversal, this strategy aims to provide traders with the tools and insights needed to make well-informed, strategically sound trading decisions.
Detailed Component Analysis
The efficacy of this trading strategy hinges on the synergistic functioning of its three key components: the Momentum-RSI, EMA Crossover, and Average True Range (ATR). Each component brings a unique perspective to the strategy, contributing to a well-rounded approach to market analysis.
1. Momentum-RSI (Relative Strength Index)
• Definition and Function: The Momentum-RSI is a modified version of the classic Relative Strength Index. While the traditional RSI measures the velocity and magnitude of directional price movements, the Momentum-RSI amplifies aspects that reflect trend strength and momentum.
• Significance in Identifying Trend Strength: This indicator excels in identifying the strength behind a market's move. A high Momentum-RSI value typically indicates strong bullish momentum, suggesting the potential continuation of an uptrend. Conversely, a low Momentum-RSI value signals strong bearish momentum, possibly indicative of an ongoing downtrend.
• Application in Strategy: In this strategy, the Momentum-RSI is used to gauge the underlying strength of market trends. It helps in filtering out minor fluctuations and focusing on significant movements, providing a clearer picture of the market's true momentum.
2. EMA (Exponential Moving Average) Crossover
• Definition and Function: The EMA Crossover component utilizes two exponential moving averages of different timeframes. Unlike simple moving averages, EMAs give more weight to recent prices, making them more responsive to new information.
• Contribution to Market Direction: The interaction between the short-term and long-term EMAs is key to determining market direction. A crossover of the shorter EMA above the longer EMA is an indicator of an emerging uptrend, while a crossover below signals a developing downtrend.
• Application in Strategy: The EMA Crossover serves as a trend confirmation tool. It provides a clear, visual representation of the market's direction, aiding in the decision-making process for entering long or short positions. This component ensures that trades are aligned with the prevailing market trend, a crucial factor for the success of the strategy.
3. ATR (Average True Range)
• Definition and Function: The ATR is an indicator that measures market volatility by calculating the average range between the high and low prices over a specified period.
• Role in Assessing Market Volatility: The ATR provides insights into the typical market movement within a given timeframe, offering a measure of the market's volatility. Higher ATR values indicate increased volatility, while lower values suggest a calmer market environment.
• Application in Strategy: Within this strategy, the ATR is instrumental in tailoring risk management techniques, particularly in setting stop-loss levels. By accounting for the market's volatility, the ATR ensures that stop-loss orders are placed at levels that are neither too tight (risking premature exits) nor too loose (exposing to excessive risk).
In summary, the combination of Momentum-RSI, EMA Crossover, and ATR in this trading strategy provides a comprehensive toolkit for market analysis. The Momentum-RSI identifies the strength of market trends, the EMA Crossover confirms the market direction, and the ATR guides in risk management by assessing volatility. Together, these components form the backbone of a strategy designed to navigate the complexities of the financial markets effectively.
1. Signal Generation Process
• Combining Indicators: The strategy operates by synthesizing signals from the Momentum-RSI, EMA Crossover, and ATR indicators. Each indicator serves a specific purpose: the Momentum-RSI gauges trend momentum, the EMA Crossover identifies the trend direction, and the ATR assesses the market’s volatility.
• Criteria for Signal Validation: For a signal to be considered valid, it must meet specific criteria set by each of the three indicators. This multi-layered approach ensures that signals are not only based on one aspect of market behavior but are a result of a comprehensive analysis.
2. Conditions for Long Positions
• Uptrend Confirmation: A long position signal is generated when the shorter-term EMA crosses above the longer-term EMA, indicating an uptrend.
• Momentum-RSI Alignment: Alongside the EMA crossover, the Momentum-RSI should indicate strong bullish momentum. This is typically represented by the Momentum-RSI being at a high level, confirming the strength of the uptrend.
• ATR Consideration: The ATR is used to fine-tune the entry point and set an appropriate stop-loss level. In a low volatility scenario, as indicated by the ATR, the stop-loss can be set tighter, closer to the entry point.
3. Conditions for Short Positions
• Downtrend Confirmation: Conversely, a short position signal is indicated when the shorter-term EMA crosses below the longer-term EMA, signaling a downtrend.
• Momentum-RSI Confirmation: The Momentum-RSI should reflect strong bearish momentum, usually seen when the Momentum-RSI is at a low level. This confirms the bearish strength of the market.
• ATR Application: The ATR again plays a role in determining the stop-loss level for the short position. Higher volatility, as indicated by a higher ATR, would warrant a wider stop-loss to accommodate larger market swings.
By adhering to these mechanics, the strategy aims to ensure that each trade is entered with a high probability of success, aligning with the market’s current momentum and trend. The integration of these indicators allows for a holistic market analysis, providing traders with clear and actionable signals for both entering and exiting trades.
Customizable Parameters in the Strategy
Flexibility and adaptability are key features of this trading strategy, achieved through a range of customizable parameters. These parameters allow traders to tailor the strategy to their individual trading style, risk tolerance, and specific market conditions. By adjusting these parameters, users can fine-tune the strategy to optimize its performance and align it with their unique trading objectives. Below are the primary parameters that can be customized within the strategy:
1. Momentum-RSI Settings
• Period: The lookback period for the Momentum-RSI can be adjusted. A shorter period makes the indicator more sensitive to recent price changes, while a longer period smoothens the RSI line, offering a broader view of the momentum.
• Overbought/Oversold Thresholds: Users can set their own overbought and oversold levels, which can help in identifying extreme market conditions more precisely according to their trading approach.
2. EMA Crossover Settings
• Timeframes for EMAs: The strategy uses two EMAs with different timeframes. Traders can modify these timeframes, choosing shorter periods for a more responsive approach or longer periods for a more conservative one.
• Source Data: The choice of price data (close, open, high, low) used in calculating the EMAs can be varied depending on the trader’s preference.
3. ATR Settings
• Lookback Period: Adjusting the lookback period for the ATR impacts how the indicator measures volatility. A longer period may provide a more stable but less responsive measure, while a shorter period offers quicker but potentially more erratic readings.
• Multiplier for Stop-Loss Calculation: This parameter allows traders to set how aggressively or conservatively they want their stop-loss to be in relation to the ATR value.
Here are the standard settings:
Trend Finding by EMAsINTRO
This indicator is a price action based tool used to visualize trends using Exponential Moving Averages (EMAs).
CONCEPTS
It's created with two EMAs with different lengths (9 and 15) based on user-defined parameters. The script calculates the EMAs for the given lengths using the closing prices of the asset.
The EMAs are plotted on the chart, and their colors are dynamically determined by a conditional statement. If slower EMA is crossing above the faster EMA than the color will be change, And vise-versa for the opposite.
USES:-
The visualization of EMAs in different colors assists in identifying potential trends:
a bullish trend when EMAs color is Blue
and a bearish trend when EMAs color are Red.
Purpose
This script provides a quick visual representation of potential trend changes based on the relationship between these two EMAs.
ASFX SignalsDescription:
The ASFX Signals Indicator, created by OmegaTools, is an open-source Pine Script™ code designed to provide traders with valuable signals for potential entry and exit points in the market. This script incorporates a combination of Exponential Moving Average (EMA) signals and Volume Weighted Average Price (VWAP) confluence, enhancing the precision of trading decisions.
Key Features:
Threshold Configuration: Users can customize the threshold parameter (thres) to fine-tune signal sensitivity, adapting the indicator to different market conditions.
EMA Length Customization: The script allows traders to adjust the length of the Exponential Moving Average (EMA) with the "EMA Length" input, providing flexibility in capturing various trends.
Show/Hide Options: Users have the flexibility to choose whether to display the EMA line, VWAP confluence, and VWAP upper and lower bands, tailoring the visual representation based on individual preferences.
VWAP Confluence: The indicator integrates VWAP confluence, offering additional confirmation for trading signals. Traders can choose the VWAP resolution and set the deviation parameter for enhanced accuracy.
Signal Filtering: The script intelligently filters signals based on the percentage of the candle that crosses the EMA. Long signals are filtered out if the closing price is above the VWAP or the specified threshold, and short signals are filtered out if the closing price is below the VWAP or the threshold.
Visual Signals: The indicator provides clear visual signals for long and short entries, making it easy for traders to identify potential opportunities. The signals are accompanied by arrows and labels for quick interpretation.
How to Use:
Adjust the threshold, EMA length, and VWAP parameters based on your trading preferences.
Choose whether to display the EMA line, VWAP confluence, and upper/lower bands.
Interpret long and short signals for potential entry and exit points, considering the percentage of the candle that crosses the EMA.
Consider additional confirmation provided by VWAP confluence.
Concepts and Methodology:
The ASFX Signals Indicator combines EMA signals and VWAP confluence to generate actionable trading signals. The script intelligently considers the percentage of the candle that crosses the EMA, providing a nuanced approach to signal confirmation. The EMA offers trend insights, while VWAP confluence enhances signal reliability.
FlexiMA Variance Tracker [presentTrading]🔶 Introduction and How it is Different
The FlexiMA Variance Tracker (FlexiMA-VT) represents a novel approach in technical analysis, distinctively standing out in the realm of financial market indicators. It leverages the concept of a variable Length Moving Average (MA) to create a versatile and dynamic oscillator. Unlike traditional oscillators that rely on a fixed-length MA, the FlexiMA-VT adapts to market conditions by varying the length of the MA, offering a more responsive and nuanced view of market trends. (*The achieved method took reference from SuperTrend Polyfactor Oscillator)
This innovative design allows the FlexiMA-VT to capture a broader spectrum of market movements, making it highly effective in diverse trading environments. Whether in stable or volatile markets, its adaptability ensures consistent relevance, providing traders with deeper insights into potential market swings.
The proposed oscillator accentuates several key aspects through a distinctive mesh of bars, which are derived from the differences between the price and a set of 20 Moving Averages, each altered by varying factors. The intensity of the mesh's colors serves as an indicator, with brighter hues signifying a greater convergence of Moving Average signals.
Starting Length = 5
Starting Length = 40
🔶 Strategy, How it Works: Detailed Explanation
1. Core Concept:
The FlexiMA-VT operates by comparing the price or an average value (indicator source) against a set of moving averages with varying lengths.
These lengths are dynamically adjusted through a starting factor and multiple increment factors, ensuring a comprehensive analysis over different time scales.
2. Normalization and Standard Deviation Calculation:
Once deviations are calculated, they undergo a normalization process, which can be set to 'None', 'Max-Min', or 'Absolute Sum'.
This step is crucial as it standardizes the deviations, allowing for a consistent scale of comparison.
The standard deviation of these normalized deviations is then calculated, offering insights into the market’s volatility and potential trend strength.
🔹Normalization
3. Median Value and Oscillator Creation:
The median of the normalized deviations forms the core of the FlexiMA-VT oscillator.
This median value provides a balanced central point, reflecting the consensus of various MA lengths.
The standard deviation bands plotted around the median enhance the interpretative power of the oscillator, indicating potential overbought or oversold conditions.
4. Multi-Factor Analysis:
The FlexiMA-VT uses multiple increment factors to generate a range of MAs, each factor representing a different scale of trend analysis.
By averaging the results from these different scales, the FlexiMA-VT forms a more comprehensive and reliable oscillator.
🔹Consensus
5. Practical Application:
Traders can use the FlexiMA-VT for various purposes, including identifying trend reversals, gauging market momentum, and determining overbought or oversold conditions.
Its dynamic nature makes it adaptable to different trading strategies, from short-term scalping to long-term position trading.
🔶 Settings
1. Indicator Source (indicatorSource): Determines the base data for calculations, typically a price average (HLC3).
2. Indicator Length (indicatorLength): Sets the base length for Moving Averages, influencing initial calculations.
3. Starting Factor (startingFactor): Initial multiplier for MA length, impacting the starting point of analysis.
4. Increment Factors (incrementFactor_1, incrementFactor_2, incrementFactor_3): Modulate the rate of change in MA lengths, adding variability.
5. Normalization Method (normalizeMethod): Standardizes deviations, with methods like 'Max-Min' and 'Absolute Sum' for comparability.
Stochastic Trend Evaluator (STE)Stochastic Trend Evaluator (STE): Detailed Description
Overview :
The Stochastic Trend Evaluator (STE) is a sophisticated trading tool designed for TradingView that combines stochastic oscillation analysis with Exponential Moving Average (EMA) trends. It is tailored to assist traders in identifying potential buy and sell opportunities in various market conditions, particularly focusing on trend reversals and momentum shifts.
Functionality & Concept :
The STE is built on two core components – the Stochastic Oscillator and the 200-period EMA.
Stochastic Oscillator :
This oscillator is a momentum indicator comparing a particular closing price of a security to a range of its prices over a certain period.
Settings:
- %K Length: 14
- %K Smoothing: 3
- %D Smoothing: 3
The %K line is the main line indicating momentum, while the %D line is a moving average of %K, providing signal triggers.
200 EMA :
The 200-period EMA serves as a dynamic trend indicator.
It helps in distinguishing between bullish and bearish market phases.
A closing price above the 200 EMA suggests a bullish trend, while below it indicates a bearish trend.
Signal Generation :
STE generates signals based on the interaction between the Stochastic Oscillator and the 200 EMA.
Buy Signal :
Occurs when the stochastic %K crosses above 20 (indicative of oversold conditions), and the closing price is above the 200 EMA.
Represented visually by green label-up arrows.
Sell Signal :
Triggered when the stochastic %K crosses below 80 (suggestive of overbought conditions), and the closing price is below the 200 EMA.
Indicated by red label-down arrows.
Background Color Indicator :
The background color of the chart changes to enhance visual interpretation of the market condition.
Green background for a bullish market scenario (when a buy signal is active).
Red background for a bearish market scenario (when a sell signal is active).
Usage Guidelines :
The STE is best used in markets that exhibit clear trends.
Ideal for traders focusing on medium to long-term trade setups.
Can be used in conjunction with other indicators for confirmation and risk management.
Note : The STE, being a proprietary tool, is based on a unique blend of standard technical analysis concepts and custom logic to provide these trading signals. It is designed to give traders a comprehensive view of the market momentum and trend strength without revealing the intricate details of its algorithm.
