RunRox - Entry Model🎯 RunRox Entry Model is an all-in-one reversal-pattern indicator engineered to help traders accurately identify key price-reversal points on their charts. It will be part of our premium indicator package and improve the effectiveness of your trading strategies.
The primary concept of this indicator is liquidity analysis, making it ideal for Smart Money traders and for trading within market structure. At the same time, the indicator is universal and can be integrated into any strategy. Below, I will outline the full concept of the indicator and its settings so you can better understand how it works.
🧬 CONCEPT
In the screenshot below, I’ll schematically illustrate the core idea of this indicator. It’s one of the patterns that the indicator automatically detects on the chart using a two-timeframe approach. We use the higher timeframe to identify liquidity zones, and the lower timeframe to capture liquidity removal and structure breaks. The schematic is shown in the screenshot below.
Our indicator includes three entry models in total , and I will discuss its functionality and features in more detail later in this post.
💡 FEATURES
Three entry models
PO3 HTF Bar
Entry Area
Optimization for each Entry Area
Filters
HTF FVG
Alert customization
Next, we will examine each entry model in detail.
🟠 ENTRY MODEL 1
The first model is the core one we’ll work with; all other models rely on its structure and construction. In the screenshot below, I’ll schematically show the complete model.
As shown in the screenshot above, we display higher-timeframe candles on the current chart to better visualize the entry model and keep the trader informed of what’s happening on the larger timeframe. The screenshot also highlights both the Long and Short models, as well as the Entry Area, which I will explain in more detail below.
The schematic model on the lower timeframe is shown in the screenshot above. It illustrates that after the Entry Model forms, we draw the Entry Area on the next candle and wait for a price pullback into this zone for the optimal trade entry. Statistically, before moving higher, the price typically revisits the Entry Area, covering the imbalances created by MSS; thus, the Entry Area represents the ideal entry point.
🟩 Entry Area
Once the Entry Model has formed, we focus on identifying the optimal pullback zone for taking a position. To determine which retracement area performs best, we conducted extensive historical backtesting on potential zones and selected those that consistently delivered the strongest results. This process yields Entry Areas with the highest probability of a successful reversal.
On the screenshot above, you can see an example of the Entry Area and which zones carry a higher versus lower probability of reversal. Zones rendered with greater transparency have historically delivered weaker results than the more opaque zones. The deeper-colored areas represent the optimal entry zones and can improve your risk-reward ratio by allowing you to enter at more favorable prices.
It’s important to remember that the entire Entry Area functions as a potential zone for scaling into a position. However, if your risk-to-reward ratio isn’t favorable, you can wait for the price to retrace to lower levels within the Entry Area and enter with a more attractive risk-to-reward.
🟢 Pattern Rating
Each entry model receives a rating in the form of green circles next to its name 🟢. The rating ranges from one to four circles, based on the historical performance of similar patterns. To calculate this rating, we backtest past data by analyzing candle behavior during the model’s formation and assign circles according to how similar patterns performed historically.
Example Ratings:
🟢 – One circle
🟢🟢 – Two circles
🟢🟢🟢 – Three circles
🟢🟢🟢🟢 – Four circles
The more green circles a model has, the more reliable it is—but it’s crucial to rely on your own analysis when identifying strong reversal points on the chart. This rating reflects the model’s historical performance and does not guarantee future results, so keep that in mind!
Below is a screenshot showing four model variations with different ratings on the chart.
⚠️ Unconfirmed Pattern
Entry Model 1 is designed so that, until the higher-timeframe candle closes, the pattern remains unconfirmed and is hidden on the chart. For traders who prefer to see setups as they form, there’s a dedicated feature that displays the unconfirmed pattern at the moment of its appearance - triggered by the Market Structure Shift - before the HTF candle closes. The screenshot below shows what the pattern looks like prior to confirmation.
‼️IMPORTANT: Until the pattern is confirmed and the higher-timeframe candle has closed, the model may disappear from the chart if price reverses and the HTF candle closes below the previous bar. Therefore, this mode is suitable only for experienced traders who want to see market moves in advance. Remember that the pattern can be removed from the chart, so we recommend waiting for the HTF candle to close before deciding to enter a trade.‼️
✂️ Filters
For the primary model, there are four filters designed to enhance entry points or exclude less-confirmed patterns. The filters available in the indicator are:
Bounce Filter
Market Shift Mode
Same Wave Filter
Only with Divergence
I will explain how each of these filters works below.
- Bounce Filter
The Bounce Filter identifies significant deviations of price from its mean and only displays the Entry Model once the asset’s price moves beyond the average level. The screenshot below illustrates how this appears on the chart.
The actual average-price calculation is more sophisticated than what’s shown in the screenshot, that image is just an illustrative example. When the price deviates significantly from the N-bar average, we start looking for the Entry Model. This approach works particularly well in range-bound markets without a clear trend, as it lets you trade strong deviations from the mean.
- Market Shift Mode
This filter works by detecting the initial impulse that triggered the liquidity sweep on the previous higher-timeframe candle, and then holding the Market Structure Shift level at that point after the sweep. If the filter is turned off, price may move higher following the liquidity removal, creating a new MSS level and potentially producing a false structure shift and entry signal on the formed model.
This filter helps you more accurately identify genuine shifts - but keep in mind that the model can still perform well without it, so choose the setting that best suits your trading style.
- Same Wave Filter
The Same Wave Filter removes entry models that form without a clear lower-timeframe structure when liquidity is swept from the previous higher-timeframe candle. In other words, if the prior HTF candle and the current one belong to the same impulse wave - without any retracements on the LTF - the model is filtered out.
Keep in mind that this filter may also exclude patterns that could have produced positive results, so whether to enable it depends on your trading system.
- Only with Divergence
The Only with Divergence filter detects divergence between the lows of successive candles and indicators like RSI. When the low that swept liquidity diverges from the previous candle’s low, the indicator displays a “DIV” label. Although RSI is cited as an example, our divergence calculation is more advanced. This filter highlights patterns where low divergence signals genuine liquidity manipulation and a likely aggressive price reversal.
🌀 Model Settings
Trade Direction: Choose whether to display models for Long or Short trades.
Fractal: Select between automatic fractal detection—which adapts the lower-timeframe (LTF) and higher-timeframe (HTF) candles—or Custom.
Custom Fractal: When Custom is selected, manually specify the LTF and HTF timeframes used to detect the patterns.
History Pattern Limit: Set the maximum number of patterns to display on the chart to keep it clean and uncluttered.
🎨 Model Style
You can flexibly customize the model’s appearance by choosing your preferred line thickness, color, and the other settings we discussed above.
🔵 ENTRY MODEL 2
This model appears under specific conditions when Model 1 cannot form. It’s a price-reversal model constructed according to different rules than the first model. The screenshot below shows how it looks on the chart.
This model forms less frequently than Model 1 but delivers equally strong performance and is displayed as a position-entry zone.
Like the Entry Area in Entry Model 1, this zone is calculated automatically and highlights the best entry levels: areas that showed the strongest historical results are rendered in a brighter shade.
🎨 Model Style
You can flexibly customize the style of Entry Model 2 - its color, opacity, visibility, and the average price of the previous candle.
🟢 ENTRY MODEL 3
Entry Model 3 is a continuation pattern that only forms after Entry Model 1 has completed and delivered the necessary price move to trigger Model 3.
Below is a schematic illustration of how Model 3 is intended to work.
🎨 Model Style
As with the previous models, you can flexibly customize the style of this zone.
⬆️ HTF CANDLES
One of the standout features of this indicator is the ability to plot higher-timeframe (HTF) candles directly on your lower-timeframe (LTF) chart, giving you clear visualization of the entry models and insight into what’s unfolding on the larger timeframe.
You can fully customize the HTF candles - select their style, the number of bars displayed, and tweak various settings to match your personal trading style.
HTF FVG
Fair Value Gaps (FVGs) can also be drawn on the HTF candles themselves, enabling you to spot key liquidity or interest zones at a glance, without switching between timeframes.
Additionally, you can view all significant historical HTF highs and lows, with demarcation lines showing where each HTF candle begins and ends.
All these options let you tailor the HTF candle display on your chart and monitor multiple timeframes’ trends in a single view.
📶 INFO PANEL
Instrument: the market symbol on which the model is detected
Fractal Timeframes: the LTF and HTF fractal periods used to locate the pattern
HTF Candle Countdown: the time remaining until the higher-timeframe candle closes
Trade Direction: the direction (Long or Short) in which the model is searched for entry
🔔 ALERT CUSTOMIZATION
And, of course, you can configure any alerts you need. There are seven alert types available:
Confirmed Entry Model 1
Unconfirmed Entry Model 1
Confirmed Entry Model 2
Confirmed Entry Model 3
Entry Area 1 Trigger
Entry Area 2 Trigger
Entry Area 3 Trigger
You also get a custom macro field where you can enter any placeholders to fully personalize your alerts. Below are example macros you can use in that field.
{{event}} - Event name ('New M1')
{{direction}} - Trade direction ('Long', 'Short')
{{area_beg}} - Entry Area Price
{{area_end}} - Entry Area Price
{{exchange}} - Exchange ('Binance')
{{ticker}} - Ticker ('BTCUSD')
{{interval}} - Timeframe ('1s', '1', 'D')
{{htf}} - High timeframe ('15', '60', 'D')
{{open}}-{{close}}-{{high}}-{{low}} - Candle price values
{{htf_open}}-{{htf_close}}-{{htf_high}}-{{htf_low}} - Last confirmed HTF candle's price
{{volume}} - Candle volume
{{time}} - Candle open time in UTC timezone
{{timenow}} - Signal time in UTC timezone
{{syminfo.currency}} - 'USD' for BTCUSD pair
{{syminfo.basecurrency}} - 'BTC' for BTCUSD pair
✅ USAGE EXAMPLES
Now I’ll demonstrate several ways to apply this indicator across different trading strategies.
Primarily, it’s most effective within the Smart Money framework - where liquidity and manipulation are the core focus - so it integrates seamlessly into your SMC-based approach.
However, it can also be employed in other strategies, such as classic technical analysis or Elliott Wave, to capitalize on reversal points on the chart.
Example 1
The first example illustrates forming a downtrend using a Smart Money strategy. After the market structure shifts and the first BOS is broken, we begin looking for a short entry.
Once Entry Model 1 is established, a Fair Value Gap appears, which we use as our position-entry zone. The nearest target becomes the newly formed BOS level.
In this trade, it was crucial to wait for a strong downtrend to develop before hunting for entries. Therefore, we waited for the first BOS to break and entered the trade to ride the continuation of the downtrend down to the next BOS level.
Example 2
The next example illustrates a downtrend developing with a Fair Value Gap on the 1-hour timeframe. The FVG is also displayed directly on the HTF candles in the chart.
The pattern forms within the HTF Fair Value Gap, indicating that we can balance this inefficiency and ride the continuation of the downtrend.
The target can simply be a 1:2 or 1:3 risk–reward ratio, as in our case.
📌 CONCLUSION
These two examples illustrate how this indicator can be used to identify reversals or trend continuations. In truth, there are countless ways to incorporate this tool, and each trader can adapt the model to fit their own strategy.
Always remember to rely on your own analysis and only enter trades when you feel confident in them.
Entrymodel
Quantify [Entry Model] | FractalystWhat’s the indicator’s purpose and functionality?
Quantify is a machine learning entry model designed to help traders identify high-probability setups to refine their strategies.
➙ Simply pick your bias, select your entry timeframes, and let Quantify handle the rest for you.
Can the indicator be applied to any market approach/trading strategy?
Absolutely, all trading strategies share one fundamental element: Directional Bias
Once you’ve determined the market bias using your own personal approach, whether it’s through technical analysis or fundamental analysis, select the trend direction in the Quantify user inputs.
The algorithm will then adjust its calculations to provide optimal entry levels aligned with your chosen bias. This involves analyzing historical patterns to identify setups with the highest potential expected values, ensuring your setups are aligned with the selected direction.
Can the indicator be used for different timeframes or trading styles?
Yes, regardless of the timeframe you’d like to take your entries, the indicator adapts to your trading style.
Whether you’re a swing trader, scalper, or even a position trader, the algorithm dynamically evaluates market conditions across your chosen timeframe.
How can this indicator help me to refine my trading strategy?
1. Focus on Positive Expected Value
• The indicator evaluates every setup to ensure it has a positive expected value, helping you focus only on trades that statistically favor long-term profitability.
2. Adapt to Market Conditions
• By analyzing real-time market behavior and historical patterns, the algorithm adjusts its calculations to match current conditions, keeping your strategy relevant and adaptable.
3. Eliminate Emotional Bias
• With clear probabilities, expected values, and data-driven insights, the indicator removes guesswork and helps you avoid emotional decisions that can damage your edge.
4. Optimize Entry Levels
• The indicator identifies optimal entry levels based on your selected bias and timeframes, improving robustness in your trades.
5. Enhance Risk Management
• Using tools like the Kelly Criterion, the indicator suggests optimal position sizes and risk levels, ensuring that your strategy maintains consistency and discipline.
6. Avoid Overtrading
• By highlighting only high-potential setups, the indicator keeps you focused on quality over quantity, helping you refine your strategy and avoid unnecessary losses.
How can I get started to use the indicator for my entries?
1. Set Your Market Bias
• Determine whether the market trend is Bullish or Bearish using your own approach.
• Select the corresponding bias in the indicator’s user inputs to align it with your analysis.
2. Choose Your Entry Timeframes
• Specify the timeframes you want to focus on for trade entries.
• The indicator will dynamically analyze these timeframes to provide optimal setups.
3. Let the Algorithm Analyze
• Quantify evaluates historical data and real-time price action to calculate probabilities and expected values.
• It highlights setups with the highest potential based on your selected bias and timeframes.
4. Refine Your Entries
• Use the insights provided—entry levels, probabilities, and risk calculations—to align your trades with a math-driven edge.
• Avoid overtrading by focusing only on setups with positive expected value.
5. Adapt to Market Conditions
• The indicator continuously adapts to real-time market behavior, ensuring its recommendations stay relevant and precise as conditions change.
How does the indicator calculate the current range?
The indicator calculates the current range by analyzing swing points from the very first bar on your charts to the latest available bar it identifies external liquidity levels, also known as BSLQ (buy-side liquidity levels) and SSLQ (sell-side liquidity levels).
What's the purpose of these levels? What are the underlying calculations?
1. Understanding Swing highs and Swing Lows
Swing High: A Swing High is formed when there is a high with 2 lower highs to the left and right.
Swing Low: A Swing Low is formed when there is a low with 2 higher lows to the left and right.
2. Understanding the purpose and the underlying calculations behind Buyside, Sellside and Pivot levels.
3. Identifying Discount and Premium Zones.
4. Importance of Risk-Reward in Premium and Discount Ranges
How does the script calculate probabilities?
The script calculates the probability of each liquidity level individually. Here's the breakdown:
1. Upon the formation of a new range, the script waits for the price to reach and tap into pivot level level. Status: "■" - Inactive
2. Once pivot level is tapped into, the pivot status becomes activated and it waits for either liquidity side to be hit. Status: "▶" - Active
3. If the buyside liquidity is hit, the script adds to the count of successful buyside liquidity occurrences. Similarly, if the sellside is tapped, it records successful sellside liquidity occurrences.
4. Finally, the number of successful occurrences for each side is divided by the overall count individually to calculate the range probabilities.
Note: The calculations are performed independently for each directional range. A range is considered bearish if the previous breakout was through a sellside liquidity. Conversely, a range is considered bullish if the most recent breakout was through a buyside liquidity.
What does the multi-timeframe functionality offer?
You can incorporate up to 4 higher timeframe probabilities directly into the table.
This feature allows you to analyze the probabilities of buyside and sellside liquidity across multiple timeframes, without the need to manually switch between them.
By viewing these higher timeframe probabilities in one place, traders can spot larger market trends and refine their entries and exits with a better understanding of the overall market context.
What are the multi-timeframe underlying calculations?
The script uses the same calculations (mentioned above) and uses security function to request the data such as price levels, bar time, probabilities and booleans from the user-input timeframe.
How does the Indicator Identifies Positive Expected Values?
Quantify instantly calculates whether a trade setup has the potential to generate positive expected value (EV).
To determine a positive EV setup, the indicator uses the formula:
EV = ( P(Win) × R(Win) ) − ( P(Loss) × R(Loss))
where:
- P(Win) is the probability of a winning trade.
- R(Win) is the reward or return for a winning trade, determined by the current risk-to-reward ratio (RR).
- P(Loss) is the probability of a losing trade.
- R(Loss) is the loss incurred per losing trade, typically assumed to be -1.
By calculating these values based on historical data and the current trading setup, the indicator helps you understand whether your trade has a positive expected value.
How can I know that the setup I'm going to trade with has a positive EV?
If the indicator detects that the adjusted pivot and buy/sell side probabilities have generated positive expected value (EV) in historical data, the risk-to-reward (RR) label within the range box will be colored blue and red .
If the setup does not produce positive EV, the RR label will appear gray.
This indicates that even the risk-to-reward ratio is greater than 1:1, the setup is not likely to yield a positive EV because, according to historical data, the number of losses outweighs the number of wins relative to the RR gain per winning trade.
What is the confidence level in the indicator, and how is it determined?
The confidence level in the indicator reflects the reliability of the probabilities calculated based on historical data. It is determined by the sample size of the probabilities used in the calculations. A larger sample size generally increases the confidence level, indicating that the probabilities are more reliable and consistent with past performance.
How does the confidence level affect the risk-to-reward (RR) label?
The confidence level (★) is visually represented alongside the probability label. A higher confidence level indicates that the probabilities used to determine the RR label are based on a larger and more reliable sample size.
How can traders use the confidence level to make better trading decisions?
Traders can use the confidence level to gauge the reliability of the probabilities and expected value (EV) calculations provided by the indicator. A confidence level above 95% is considered statistically significant and indicates that the historical data supporting the probabilities is robust. This high confidence level suggests that the probabilities are reliable and that the indicator’s recommendations are more likely to be accurate.
In data science and statistics, a confidence level above 95% generally means that there is less than a 5% chance that the observed results are due to random variation. This threshold is widely accepted in research and industry as a marker of statistical significance. Studies such as those published in the Journal of Statistical Software and the American Statistical Association support this threshold, emphasizing that a confidence level above 95% provides a strong assurance of data reliability and validity.
Conversely, a confidence level below 95% indicates that the sample size may be insufficient and that the data might be less reliable. In such cases, traders should approach the indicator’s recommendations with caution and consider additional factors or further analysis before making trading decisions.
How does the sample size affect the confidence level, and how does it relate to my TradingView plan?
The sample size for calculating the confidence level is directly influenced by the amount of historical data available on your charts. A larger sample size typically leads to more reliable probabilities and higher confidence levels.
Here’s how the TradingView plans affect your data access:
Essential Plan
The Essential Plan provides basic data access with a limited amount of historical data. This can lead to smaller sample sizes and lower confidence levels, which may weaken the robustness of your probability calculations. Suitable for casual traders who do not require extensive historical analysis.
Plus Plan
The Plus Plan offers more historical data than the Essential Plan, allowing for larger sample sizes and more accurate confidence levels. This enhancement improves the reliability of indicator calculations. This plan is ideal for more active traders looking to refine their strategies with better data.
Premium Plan
The Premium Plan grants access to extensive historical data, enabling the largest sample sizes and the highest confidence levels. This plan provides the most reliable data for accurate calculations, with up to 20,000 historical bars available for analysis. It is designed for serious traders who need comprehensive data for in-depth market analysis.
PRO+ Plans
The PRO+ Plans offer the most extensive historical data, allowing for the largest sample sizes and the highest confidence levels. These plans are tailored for professional traders who require advanced features and significant historical data to support their trading strategies effectively.
For many traders, the Premium Plan offers a good balance of affordability and sufficient sample size for accurate confidence levels.
What is the HTF probability table and how does it work?
The HTF (Higher Time Frame) probability table is a feature that allows you to view buy and sellside probabilities and their status from timeframes higher than your current chart timeframe.
Here’s how it works:
Data Request: The table requests and retrieves data from user-defined higher timeframes (HTFs) that you select.
Probability Display: It displays the buy and sellside probabilities for each of these HTFs, providing insights into the likelihood of price movements based on higher timeframe data.
Detailed Tooltips: The table includes detailed tooltips for each timeframe, offering additional context and explanations to help you understand the data better.
What do the different colors in the HTF probability table indicate?
The colors in the HTF probability table provide visual cues about the expected value (EV) of trading setups based on higher timeframe probabilities:
Blue: Suggests that entering a long position from the HTF user-defined pivot point, targeting buyside liquidity, is likely to result in a positive expected value (EV) based on historical data and sample size.
Red: Indicates that entering a short position from the HTF user-defined pivot point, targeting sellside liquidity, is likely to result in a positive expected value (EV) based on historical data and sample size.
Gray: Shows that neither long nor short trades from the HTF user-defined pivot point are expected to generate positive EV, suggesting that trading these setups may not be favorable.
What machine learning techniques are used in Quantify?
Quantify offers two main machine learning approaches:
1. Adaptive Learning (Fixed Sample Size): The algorithm learns from the entire dataset without resampling, maintaining a stable model that adapts to the latest market conditions.
2. Bootstrap Resampling: This method creates multiple subsets of the historical data, allowing the model to train on varying sample sizes. This technique enhances the robustness of predictions by ensuring that the model is not overfitting to a single dataset.
How does machine learning affect the expected value calculations in Quantify?
Machine learning plays a key role in improving the accuracy of expected value (EV) calculations. By analyzing historical price action, liquidity hits, and market bias patterns, the model continuously adjusts its understanding of risk and reward, allowing the expected value to reflect the most likely market movements. This results in more precise EV predictions, helping traders focus on setups that maximize profitability.
What is the Kelly Criterion, and how does it work in Quantify?
The Kelly Criterion is a mathematical formula used to determine the optimal position size for each trade, maximizing long-term growth while minimizing the risk of large drawdowns. It calculates the percentage of your portfolio to risk on a trade based on the probability of winning and the expected payoff.
Quantify integrates this with user-defined inputs to dynamically calculate the most effective position size in percentage, aligning with the trader’s risk tolerance and desired exposure.
How does Quantify use the Kelly Criterion in practice?
Quantify uses the Kelly Criterion to optimize position sizing based on the following factors:
1. Confidence Level: The model assesses the confidence level in the trade setup based on historical data and sample size. A higher confidence level increases the suggested position size because the trade has a higher probability of success.
2. Max Allowed Drawdown (User-Defined): Traders can set their preferred maximum allowed drawdown, which dictates how much loss is acceptable before reducing position size or stopping trading. Quantify uses this input to ensure that risk exposure aligns with the trader’s risk tolerance.
3. Probabilities: Quantify calculates the probabilities of success for each trade setup. The higher the probability of a successful trade (based on historical price action and liquidity levels), the larger the position size suggested by the Kelly Criterion.
What is a trailing stoploss, and how does it work in Quantify?
A trailing stoploss is a dynamic risk management tool that moves with the price as the market trend continues in the trader’s favor. Unlike a fixed take profit, which stays at a set level, the trailing stoploss automatically adjusts itself as the market moves, locking in profits as the price advances.
In Quantify, the trailing stoploss is enhanced by incorporating market structure liquidity levels (explain above). This ensures that the stoploss adjusts intelligently based on key price levels, allowing the trader to stay in the trade as long as the trend remains intact, while also protecting profits if the market reverses.
Why would a trader prefer a trailing stoploss based on liquidity levels instead of a fixed take-profit level?
Traders who use trailing stoplosses based on liquidity levels prefer this method because:
1. Market-Driven Flexibility: The stoploss follows the market structure rather than being static at a pre-defined level. This means the stoploss is less likely to be hit by small market fluctuations or false reversals. The stoploss remains adaptive, moving as the market moves.
2. Riding the Trend: Traders can capture more profit during a sustained trend because the trailing stop will adjust only when the trend starts to reverse significantly, based on key liquidity levels. This allows them to hold positions longer without prematurely locking in profits.
3. Avoiding Premature Exits: Fixed stoploss levels may exit a trade too early in volatile markets, while liquidity-based trailing stoploss levels respect the natural flow of price action, preventing the trader from exiting too soon during pullbacks or minor retracements.
🎲 Becoming the House: Gaining an Edge Over the Market
In American roulette, the casino has a 5.26% edge due to the presence of the 0 and 00 pockets. On even-money bets, players face a 47.37% chance of winning, while true 50/50 odds would require a 50% chance. This edge—the gap between the payout odds and the true probabilities—ensures that, statistically, the casino will always win over time, even if individual players win occasionally.
From a Trader’s Perspective
In trading, your edge comes from identifying and executing setups with a positive expected value (EV). For example:
• If you identify a setup with a 55.48% chance of winning and a 1:1 risk-to-reward (RR) ratio, your trade has a statistical advantage over a neutral (50/50) probability.
This edge works in your favor when applied consistently across a series of trades, just as the casino’s edge ensures profitability across thousands of spins.
🎰 Applying the Concept to Trading
Like casinos leverage their mathematical edge in games of chance, you can achieve long-term success in trading by focusing on setups with positive EV and managing your trades systematically. Here’s how:
1. Probability Advantage: Prioritize trades where the probability of success (win rate) exceeds the breakeven rate for your chosen risk-to-reward ratio.
• Example: With a 1:1 RR, you need a win rate above 50% to achieve positive EV.
2. Risk-to-Reward Ratio (RR): Even with a win rate below 50%, you can gain an edge by increasing your RR (e.g., a 40% win rate with a 2:1 RR still has positive EV).
3. Consistency and Discipline: Just as casinos profit by sticking to their mathematical advantage over thousands of spins, traders must rely on their edge across many trades, avoiding emotional decisions or overleveraging.
By targeting favorable probabilities and managing trades effectively, you “become the house” in your trading. This approach allows you to leverage statistical advantages to enhance your overall performance and achieve sustainable profitability.
What Makes the Quantify Indicator Original?
1. Data-Driven Edge
Unlike traditional indicators that rely on static formulas, Quantify leverages probability-based analysis and machine learning. It calculates expected value (EV) and confidence levels to help traders identify setups with a true statistical edge.
2. Integration of Market Structure
Quantify uses market structure liquidity levels to dynamically adapt. It identifies key zones like swing highs/lows and liquidity traps, enabling users to align entries and exits with where the market is most likely to react. This bridges the gap between price action analysis and quantitative trading.
3. Sophisticated Risk Management
The Kelly Criterion implementation is unique. Quantify allows traders to input their maximum allowed drawdown, dynamically adjusting risk exposure to maintain optimal position sizing. This ensures risk is scientifically controlled while maximizing potential growth.
4. Multi-Timeframe and Liquidity-Based Trailing Stops
The indicator doesn’t just suggest fixed profit-taking levels. It offers market structure-based trailing stop-loss functionality, letting traders ride trends as long as liquidity and probabilities favor the position, which is rare in most tools.
5. Customizable Bias and Adaptive Learning
• Directional Bias: Traders can set a bullish or bearish bias, and the indicator recalculates probabilities to align with the trader’s market outlook.
• Adaptive Learning: The machine learning model adapts to changes in data (via resampling or bootstrap methods), ensuring that predictions stay relevant in evolving markets.
6. Positive EV Focus
The focus on positive EV setups differentiates it from reactive indicators. It shifts trading from chasing signals to acting on setups that statistically favor profitability, akin to how professional quant funds operate.
7. User Empowerment
Through features like customizable timeframes, real-time probability updates, and visualization tools, Quantify empowers users to make data-informed decisions.
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Our charting tools are provided for informational and educational purposes only and should not be construed as financial, investment, or trading advice. They are not intended to forecast market movements or offer specific recommendations. Users should understand that past performance does not guarantee future results and should not base financial decisions solely on historical data.
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