MACD Volume Strategy for XAUUSD (15m) [PineIndicators]The MACD Volume Strategy is a momentum-based trading system designed for XAUUSD on the 15-minute timeframe. It integrates two key market indicators: the Moving Average Convergence Divergence (MACD) and a volume-based oscillator to identify strong trend shifts and confirm trade opportunities. This strategy uses dynamic position sizing, incorporates leverage customization, and applies structured entry and exit conditions to improve risk management.
⚙️ Core Strategy Components
1️⃣ Volume-Based Momentum Calculation
The strategy includes a custom volume oscillator to filter trade signals based on market activity. The oscillator is derived from the difference between short-term and long-term volume trends using Exponential Moving Averages (EMAs)
Short EMA (default = 5) represents recent volume activity.
Long EMA (default = 8) captures broader volume trends.
Positive values indicate rising volume, supporting momentum-based trades.
Negative values suggest weak market activity, reducing signal reliability.
By requiring positive oscillator values, the strategy ensures momentum confirmation before entering trades.
2️⃣ MACD Trend Confirmation
The strategy uses the MACD indicator as a trend filter. The MACD is calculated as:
Fast EMA (16-period) detects short-term price trends.
Slow EMA (26-period) smooths out price fluctuations to define the overall trend.
Signal Line (9-period EMA) helps identify crossovers, signaling potential trend shifts.
Histogram (MACD – Signal) visualizes trend strength.
The system generates trade signals based on MACD crossovers around the zero line, confirming bullish or bearish trend shifts.
📌 Trade Logic & Conditions
🔹 Long Entry Conditions
A buy signal is triggered when all the following conditions are met:
✅ MACD crosses above 0, signaling bullish momentum.
✅ Volume oscillator is positive, confirming increased trading activity.
✅ Current volume is at least 50% of the previous candle’s volume, ensuring market participation.
🔻 Short Entry Conditions
A sell signal is generated when:
✅ MACD crosses below 0, indicating bearish momentum.
✅ Volume oscillator is positive, ensuring market activity is sufficient.
✅ Current volume is less than 50% of the previous candle’s volume, showing decreasing participation.
This multi-factor approach filters out weak or false signals, ensuring that trades align with both momentum and volume dynamics.
📏 Position Sizing & Leverage
Dynamic Position Calculation:
Qty = strategy.equity × leverage / close price
Leverage: Customizable (default = 1x), allowing traders to adjust risk exposure.
Adaptive Sizing: The strategy scales position sizes based on account equity and market price.
Slippage & Commission: Built-in slippage (2 points) and commission (0.01%) settings provide realistic backtesting results.
This ensures efficient capital allocation, preventing overexposure in volatile conditions.
🎯 Trade Management & Exits
Take Profit & Stop Loss Mechanism
Each position includes predefined profit and loss targets:
Take Profit: +10% of risk amount.
Stop Loss: Fixed at 10,100 points.
The risk-reward ratio remains balanced, aiming for controlled drawdowns while maximizing trade potential.
Visual Trade Tracking
To improve trade analysis, the strategy includes:
📌 Trade Markers:
"Buy" label when a long position opens.
"Close" label when a position exits.
📌 Trade History Boxes:
Green for profitable trades.
Red for losing trades.
📌 Horizontal Trade Lines:
Shows entry and exit prices.
Helps identify trend movements over multiple trades.
This structured visualization allows traders to analyze past performance directly on the chart.
⚡ How to Use This Strategy
1️⃣ Apply the script to a XAUUSD (Gold) 15m chart in TradingView.
2️⃣ Adjust leverage settings as needed.
3️⃣ Enable backtesting to assess past performance.
4️⃣ Monitor volume and MACD conditions to understand trade triggers.
5️⃣ Use the visual trade markers to review historical performance.
The MACD Volume Strategy is designed for short-term trading, aiming to capture momentum-driven opportunities while filtering out weak signals using volume confirmation.
Cerca negli script per "profitable"
Balance of Power for US30 4H [PineIndicators]The Balance of Power (BoP) Strategy is a momentum-based trading system for the US30 index on a 4-hour timeframe. It measures the strength of buyers versus sellers in each candle using the Balance of Power (BoP) indicator and executes trades based on predefined threshold crossovers. The strategy includes dynamic position sizing, adjustable leverage, and visual trade tracking.
⚙️ Core Strategy Mechanics
Positive values indicate buying strength.
Negative values indicate selling strength.
Values close to 1 suggest strong bullish momentum.
Values close to -1 indicate strong bearish pressure.
The strategy uses fixed threshold crossovers to determine trade entries and exits.
📌 Trade Logic
Entry Conditions
Long Entry: When BoP crosses above 0.8, signaling strong buying pressure.
Exit Conditions
Position Close: When BoP crosses below -0.8, indicating a shift to selling pressure.
This threshold-based system filters out low-confidence signals and focuses on high-momentum shifts.
📏 Position Sizing & Leverage
Leverage: Adjustable by the user (default = 5x).
Risk Management: Position size adapts dynamically based on equity fluctuations.
📊 Trade Visualization & History Tracking
Trade Markers:
"Buy" labels appear when a long position is opened.
"Close" labels appear when a position is exited.
Trade History Boxes:
Green for profitable trades.
Red for losing trades.
These elements provide clear visual tracking of past trade execution.
⚡ Usage & Customization
1️⃣ Apply the script to a US30 4H chart in TradingView.
2️⃣ Adjust leverage settings as needed.
3️⃣ Review trade signals and historical performance with visual markers.
4️⃣ Enable backtesting to evaluate past performance.
This strategy is designed for momentum-based trading and is best suited for volatile market conditions.
AO/AC Trading Zones Strategy [Skyrexio] Overview
AO/AC Trading Zones Strategy leverages the combination of Awesome Oscillator (AO), Acceleration/Deceleration Indicator (AC), Williams Fractals, Williams Alligator and Exponential Moving Average (EMA) to obtain the high probability long setups. Moreover, strategy uses multi trades system, adding funds to long position if it considered that current trend has likely became stronger. Combination of AO and AC is used for creating so-called trading zones to create the signals, while Alligator and Fractal are used in conjunction as an approximation of short-term trend to filter them. At the same time EMA (default EMA's period = 100) is used as high probability long-term trend filter to open long trades only if it considers current price action as an uptrend. More information in "Methodology" and "Justification of Methodology" paragraphs. The strategy opens only long trades.
Unique Features
No fixed stop-loss and take profit: Instead of fixed stop-loss level strategy utilizes technical condition obtained by Fractals and Alligator to identify when current uptrend is likely to be over. In some special cases strategy uses AO and AC combination to trail profit (more information in "Methodology" and "Justification of Methodology" paragraphs)
Configurable Trading Periods: Users can tailor the strategy to specific market windows, adapting to different market conditions.
Multilayer trades opening system: strategy uses only 10% of capital in every trade and open up to 5 trades at the same time if script consider current trend as strong one.
Short and long term trend trade filters: strategy uses EMA as high probability long-term trend filter and Alligator and Fractal combination as a short-term one.
Methodology
The strategy opens long trade when the following price met the conditions:
1. Price closed above EMA (by default, period = 100). Crossover is not obligatory.
2. Combination of Alligator and Williams Fractals shall consider current trend as an upward (all details in "Justification of Methodology" paragraph)
3. Both AC and AO shall print two consecutive increasing values. At the price candle close which corresponds to this condition algorithm opens the first long trade with 10% of capital.
4. If combination of Alligator and Williams Fractals shall consider current trend has been changed from up to downtrend, all long trades will be closed, no matter how many trades has been opened.
5. If AO and AC both continue printing the rising values strategy opens the long trade on each candle close with 10% of capital while number of opened trades reaches 5.
6. If AO and AC both has printed 5 rising values in a row algorithm close all trades if candle's low below the low of the 5-th candle with rising AO and AC values in a row.
Script also has additional visuals. If second long trade has been opened simultaneously the Alligator's teeth line is plotted with the green color. Also for every trade in a row from 2 to 5 the label "Buy More" is also plotted just below the teeth line. With every next simultaneously opened trade the green color of the space between teeth and price became less transparent.
Strategy settings
In the inputs window user can setup strategy setting:
EMA Length (by default = 100, period of EMA, used for long-term trend filtering EMA calculation).
User can choose the optimal parameters during backtesting on certain price chart.
Justification of Methodology
Let's explore the key concepts of this strategy and understand how they work together. We'll begin with the simplest: the EMA.
The Exponential Moving Average (EMA) is a type of moving average that assigns greater weight to recent price data, making it more responsive to current market changes compared to the Simple Moving Average (SMA). This tool is widely used in technical analysis to identify trends and generate buy or sell signals. The EMA is calculated as follows:
1.Calculate the Smoothing Multiplier:
Multiplier = 2 / (n + 1), Where n is the number of periods.
2. EMA Calculation
EMA = (Current Price) × Multiplier + (Previous EMA) × (1 − Multiplier)
In this strategy, the EMA acts as a long-term trend filter. For instance, long trades are considered only when the price closes above the EMA (default: 100-period). This increases the likelihood of entering trades aligned with the prevailing trend.
Next, let’s discuss the short-term trend filter, which combines the Williams Alligator and Williams Fractals. Williams Alligator
Developed by Bill Williams, the Alligator is a technical indicator that identifies trends and potential market reversals. It consists of three smoothed moving averages:
Jaw (Blue Line): The slowest of the three, based on a 13-period smoothed moving average shifted 8 bars ahead.
Teeth (Red Line): The medium-speed line, derived from an 8-period smoothed moving average shifted 5 bars forward.
Lips (Green Line): The fastest line, calculated using a 5-period smoothed moving average shifted 3 bars forward.
When the lines diverge and align in order, the "Alligator" is "awake," signaling a strong trend. When the lines overlap or intertwine, the "Alligator" is "asleep," indicating a range-bound or sideways market. This indicator helps traders determine when to enter or avoid trades.
Fractals, another tool by Bill Williams, help identify potential reversal points on a price chart. A fractal forms over at least five consecutive bars, with the middle bar showing either:
Up Fractal: Occurs when the middle bar has a higher high than the two preceding and two following bars, suggesting a potential downward reversal.
Down Fractal: Happens when the middle bar shows a lower low than the surrounding two bars, hinting at a possible upward reversal.
Traders often use fractals alongside other indicators to confirm trends or reversals, enhancing decision-making accuracy.
How do these tools work together in this strategy? Let’s consider an example of an uptrend.
When the price breaks above an up fractal, it signals a potential bullish trend. This occurs because the up fractal represents a shift in market behavior, where a temporary high was formed due to selling pressure. If the price revisits this level and breaks through, it suggests the market sentiment has turned bullish.
The breakout must occur above the Alligator’s teeth line to confirm the trend. A breakout below the teeth is considered invalid, and the downtrend might still persist. Conversely, in a downtrend, the same logic applies with down fractals.
In this strategy if the most recent up fractal breakout occurs above the Alligator's teeth and follows the last down fractal breakout below the teeth, the algorithm identifies an uptrend. Long trades can be opened during this phase if a signal aligns. If the price breaks a down fractal below the teeth line during an uptrend, the strategy assumes the uptrend has ended and closes all open long trades.
By combining the EMA as a long-term trend filter with the Alligator and fractals as short-term filters, this approach increases the likelihood of opening profitable trades while staying aligned with market dynamics.
Now let's talk about the trading zones concept and its signals. To understand this we need to briefly introduce what is AO and AC. The Awesome Oscillator (AO), developed by Bill Williams, is a momentum indicator designed to measure market momentum by contrasting recent price movements with a longer-term historical perspective. It helps traders detect potential trend reversals and assess the strength of ongoing trends.
The formula for AO is as follows:
AO = SMA5(Median Price) − SMA34(Median Price)
where:
Median Price = (High + Low) / 2
SMA5 = 5-period Simple Moving Average of the Median Price
SMA 34 = 34-period Simple Moving Average of the Median Price
The Acceleration/Deceleration (AC) Indicator, introduced by Bill Williams, measures the rate of change in market momentum. It highlights shifts in the driving force of price movements and helps traders spot early signs of trend changes. The AC Indicator is particularly useful for identifying whether the current momentum is accelerating or decelerating, which can indicate potential reversals or continuations. For AC calculation we shall use the AO calculated above is the following formula:
AC = AO − SMA5(AO) , where SMA5(AO)is the 5-period Simple Moving Average of the Awesome Oscillator
When the AC is above the zero line and rising, it suggests accelerating upward momentum.
When the AC is below the zero line and falling, it indicates accelerating downward momentum.
When the AC is below zero line and rising it suggests the decelerating the downtrend momentum. When AC is above the zero line and falling, it suggests the decelerating the uptrend momentum.
Now let's discuss the trading zones concept and how it can create the signal. Zones are created by the combination of AO and AC. We can divide three zone types:
Greed zone: when the AO and AC both are rising
Red zone: when the AO and AC both are decreasing
Gray zone: when one of AO or AC is rising, the other is falling
Gray zone is considered as uncertainty. AC and AO are moving in the opposite direction. Strategy skip such price action to decrease the chance to stuck in the losing trade during potential sideways. Red zone is also not interesting for the algorithm because both indicators consider the trend as bearish, but strategy opens only long trades. It is waiting for the green zone to increase the chance to open trade in the direction of the potential uptrend. When we have 2 candles in a row in the green zone script executes a long trade with 10% of capital.
Two green zone candles in a row is considered by algorithm as a bullish trend, but now so strong, that's the reason why trade is going to be closed when the combination of Alligator and Fractals will consider the the trend change from bullish to bearish. If id did not happens, algorithm starts to count the green zone candles in a row. When we have 5 in a row script change the trade closing condition. Such situation is considered is a high probability strong bull market and all trades will be closed if candle's low will be lower than fifth green zone candle's low. This is used to increase probability to secure the profit. If long trades are initiated, the strategy continues utilizing subsequent signals until the total number of trades reaches a maximum of 5. Each trade uses 10% of capital.
Why we use trading zones signals? If currently strategy algorithm considers the high probability of the short-term uptrend with the Alligator and Fractals combination pointed out above and the long-term trend is also suggested by the EMA filter as bullish. Rising AC and AO values in the direction of the most likely main trend signaling that we have the high probability of the fastest bullish phase on the market. The main idea is to take part in such rapid moves and add trades if this move continues its acceleration according to indicators.
Backtest Results
Operating window: Date range of backtests is 2023.01.01 - 2024.12.31. It is chosen to let the strategy to close all opened positions.
Commission and Slippage: Includes a standard Binance commission of 0.1% and accounts for possible slippage over 5 ticks.
Initial capital: 10000 USDT
Percent of capital used in every trade: 10%
Maximum Single Position Loss: -9.49%
Maximum Single Profit: +24.33%
Net Profit: +4374.70 USDT (+43.75%)
Total Trades: 278 (39.57% win rate)
Profit Factor: 2.203
Maximum Accumulated Loss: 668.16 USDT (-5.43%)
Average Profit per Trade: 15.74 USDT (+1.37%)
Average Trade Duration: 60 hours
How to Use
Add the script to favorites for easy access.
Apply to the desired timeframe and chart (optimal performance observed on 4h BTC/USDT).
Configure settings using the dropdown choice list in the built-in menu.
Set up alerts to automate strategy positions through web hook with the text: {{strategy.order.alert_message}}
Disclaimer:
Educational and informational tool reflecting Skyrex commitment to informed trading. Past performance does not guarantee future results. Test strategies in a simulated environment before live implementation
These results are obtained with realistic parameters representing trading conditions observed at major exchanges such as Binance and with realistic trading portfolio usage parameters.
Pure Price Action Breakout with 1:5 RR
Description of the Price Action Trading Script (Pine Script v6)
Overview
This script is a pure price action-based breakout strategy designed for TradingView. It identifies key breakout levels and executes long and short trades based on market structure. The strategy ensures a minimum risk-to-reward ratio (RR) of 1:5, aiming for high profitability with well-defined stop-loss and take-profit levels.
How the Script Works
1️⃣ Breakout Identification
The script uses a lookback period to find the highest high and lowest low over the last n bars.
A bullish breakout occurs when the price closes above the previous highest high.
A bearish breakout happens when the price closes below the previous lowest low.
2️⃣ Entry & Exit Strategy
Long Entry: If a bullish breakout is detected, the script enters a long position.
Short Entry: If a bearish breakout is detected, the script enters a short position.
The stop-loss is placed at the recent swing low (for long trades) or recent swing high (for short trades).
The target price is calculated based on a risk-to-reward ratio of 1:5, ensuring profitable trades.
3️⃣ Risk Management
The stop-loss prevents excessive losses by exiting trades when the market moves unfavorably.
The strategy ensures that each trade has a reward potential at least 5 times the risk.
Positions are executed based on price action only, without indicators like moving averages or RSI.
4️⃣ Visual Representation
The script plots breakout levels to help traders visualize potential trade setups.
Entry points, stop-loss, and take-profit levels are labeled on the chart for easy tracking.
Key Features & Benefits
✔ Pure Price Action – No lagging indicators, only real-time price movements.
✔ High Risk-to-Reward Ratio (1:5) – Ensures high-profit potential trades.
✔ Real-time Entry & Exit Signals – Provides accurate trade setups.
✔ Dynamic Stop-loss Calculation – Adjusts based on recent market structure.
✔ Customizable Parameters – Lookback periods and risk ratios can be modified.
Naive Bayes Candlestick Pattern Classifier v1.1 BETAAn intermezzo on why i made this script publication..
A : Candlestick Pattern took hours to backtest, why not using Machine Learning techniques?
B : Machine Learning, no that's gonna be really heavy bro!
A : Not really, because we use Naive Bayes.
B : The simplest, yet powerful machine learning algorithm to separate (a.k.a classify) multivariate data.
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Hello, everyone!
After deep research in extracting meaningful information from the market, I ended up building this powerful machine learning indicator based on the evolution of Bayesian Statistics. This indicator not only leverages the simplicity of Naive Bayes but also extends its application to candlestick pattern analysis, making it an invaluable tool for traders who are looking to enhance their technical analysis without spending countless hours manually backtesting each pattern on each market!.
What most interesting part is actually after learning all of likely useless methods like fibonacci, supply and demand, volume profile, etc. We always ended up back to basic like support and resistance and candlestick patterns, but with a slight twist on strategy algorithm design and statistical approach. Thus, the only reason why i made this, because i exactly know that you guys will ended up in this position as time goes by.
The essence of this indicator lies in its ability to automate the recognition and statistical evaluation of various candlestick patterns. Traditionally, traders have relied on visual inspection and manual backtesting to determine the effectiveness of patterns like Bullish Engulfing, Bearish Engulfing, Harami variations, Hammer formations, and even more complex multi-candle patterns such as Three White Soldiers, Three Black Crows, Dark Cloud Cover, and Piercing Pattern. However, these conventional methods are both time-consuming and prone to subjective bias.
To address these challenges, I employed Naive Bayes—a probabilistic classifier that, despite its simplicity, offers robust performance in various domains. Naive Bayes assumes that each feature is independent of the others given the class label, which, although a strong assumption, works remarkably well in practice, especially when the dataset is large like market data and the feature space is high-dimensional. In our case, each candlestick pattern acts as a feature that can be statistically evaluated based on its historical performance. The indicator calculates a probability that a given pattern will lead to a price reversal, by comparing the pattern’s close price to the highest or lowest price achieved in a lookahead window.
One of the standout features of this script is its flexibility. Each candlestick pattern is not only coded into the system but also comes with individual toggles to enable or disable them based on your trading strategy. This means you can choose to focus on single-candle patterns like Bullish Engulfing or more complex multi-candle formations such as Three White Soldiers, without modifying the core code. The built-in customization options allow you to adjust colors and labels for each pattern, giving you the freedom to tailor the visual output to your preference. This level of customization ensures that the indicator integrates seamlessly into your existing TradingView setup.
Moreover, the indicator isn’t just about pattern recognition—it also incorporates outcome-based learning. Every time a pattern is detected, it looks ahead a predefined number of bars to evaluate if the expected reversal actually materialized. This outcome is then stored in arrays, and over time, the script dynamically calculates the probability of success for each pattern. These probabilities are presented in a real-time updating table on your chart, which shows not only the percentage probability but also the count of historical occurrences. With this information at your fingertips, you can quickly gauge the reliability of each pattern in your chosen market and timeframe.
Another significant advantage of this approach is its speed and efficiency. While more complex machine learning models like neural networks might require heavy computational resources and longer training times, the Naive Bayes classifier in this script is lightweight, instantaneous and can be updated on the fly with each new bar. This real-time capability is essential for modern traders who need to make quick decisions in fast-paced markets.
Furthermore, by automating the process of backtesting, the indicator frees up your time to focus on other aspects of trading strategy development. Instead of manually analyzing hundreds or even thousands of candles, you can rely on the statistical power of Naive Bayes to provide you with insights on which patterns are most likely to result in profitable moves. This not only enhances your efficiency but also helps to eliminate the cognitive biases that often plague manual analysis.
In summary, this indicator represents a fusion of traditional candlestick analysis with modern machine learning techniques. It harnesses the simplicity and effectiveness of Naive Bayes to deliver a dynamic, real-time evaluation of various candlestick patterns. Whether you are a seasoned trader looking to refine your technical analysis or a beginner eager to understand market dynamics, this tool offers a powerful, customizable, and efficient solution. Welcome to a new era where advanced statistical methods meet practical trading insights—happy trading and may your patterns always be in your favor!
Note : On this current released beta version, you must manually adjust reversal percentage move based on each market. Further updates may include automated best range detection and probability.
Reversal rehersal v1This indicator was designed to identify potential market reversal zones using a combination of RSI thresholds (shooting range/falling range), candlestick patterns, and Fair Value Gaps (FVGs). By combining all these elements into one indicator, it allow for outputting high probability buy/sell signals for use by scalpers on low timeframes like 1-15 mins, for quick but small profits.
Note: that this has been mainly tested on DE40 index on the 1 min timeframe, and need to be adjusted to whichever timeframe and symbol you intend to use. Refer to the backtester feature for checking if this indicator may work for you.
The indicator use RSI ranges from two timeframes to highlight where momentum is building up. During these areas, it will look for certain candlestick patterns (Sweeps as the primary one) and check for existance of fair value gaps to further enhance the hitrate of the signal.
The logic for FVG detection was based on ©pmk07's work with MTF FVG tiny indicator. Several major changes was implemented though and incorporated into this indicator. Among these are:
Automatically adjustments of FVG boxes when mitigated partially and options to extend/cull boxes for performance and clarity.
Backtesting Table (Experimental):
This indicator also features an optional simplified table to review historical theoretical performance of signals, including win rate, profit/loss, and trade statistics. This does not take commision or slippage into consideration.
Usage Notes:
Setup:
1. Add the indicator to your chart.
2. Decide if you want to use Long or Short (or both).
3. If you're scalping on ie. 1 min time frame, make sure to set FVG's to higher timeframes (ie. 5, 15, 60).
4. Enable the 'Show backtest results' and adjust the 'Signals' og 'Take profit' and 'Stop loss' values until you are satisfied with the results.
Use:
1. Setup an alert based on either of the 'BullishShooting range' or 'BearishFalling range' alerts. This will draw your attention to watch for the possible setups.
2. Verify if there's a significant imbalance prior to the signal before taking the trade. Otherwise this may invalidate the setup.
3. Once a signal is shown on the graph (either Green arrow up for buys/Red arrow down for sells) - you should enter a trade with the given 'Take profit' and 'Stop loss' values.
4. (optional) Setup an alert for either the Strong/Weak signals. Which corresponds to when one of the arrows are printed.
Important: This is the way I use it myself, but use at own risk and remember to combine with other indicators for further confluence. Remember this is no crystal ball and I do not guarantee profitable results. The indicator merely show signals with high probability setups for scalping.
Daily COC Strategy with SHERLOCK WAVESThis indicator implements a unique trading strategy known as the "Daily COC (Candle Over Candle) Strategy" enhanced with "SHERLOCK WAVES" for pattern recognition. It's designed for traders looking to capitalize on specific candlestick formations with a negative risk-reward ratio, with the aim of achieving a high win rate (over 70%) through numerous trading opportunities, despite each trade having a higher risk relative to the reward.
Key Features:
Pattern Recognition: Identifies a setup based on three consecutive candles - a red candle followed by a shooting star, then an entry candle that does not break below the shooting star's low.
Negative Risk/Reward Trade Selection: Focuses on entries where the potential stop loss is greater than the take profit, banking on a high win rate to offset the individual trade's negative risk-reward ratio.
Visual Signals:
Green Label: Marks potential entry points at the high of the candle before the entry.
Green Dot: Indicates a winning trade closure.
Red Dot: Signals a losing trade closure.
Blue Circle: Warns when the current candle is within 2% of breaking above the previous candle's high, suggesting a potential setup is developing.
Green Circle: Plots the take profit level.
Red Circle: Plots the stop loss level.
Dynamic Statistics: A live updating label showing the number of trades, wins, losses, open trades, current account balance, and win percentage.
Customizable Parameters:
Risk % per Trade: Adjust the percentage of your account balance you're willing to risk on each trade.
Initial Account Balance: Set your starting balance for tracking performance.
Start Date for Strategy: Define when the strategy should start calculating from, allowing for backtesting.
Alerts:
An alert condition is set for when a potential trade setup is developing, helping traders prepare for entries.
Usage Tips:
This strategy is predicated on the idea that a high win rate can compensate for the negative risk-reward ratio of individual trades. It might not suit all market conditions or traders' risk profiles.
Use this strategy in conjunction with other analysis methods to validate trade setups.
Note: Always backtest thoroughly before applying to live markets. Consider this tool as part of a broader trading strategy, not a standalone solution. Monitor your win rate and adjust your risk management accordingly to ensure the strategy remains profitable over time.
This description now correctly explains the purpose behind the negative risk-reward ratio in the context of your trading strategy.
High-Low Breakout Strategy with ATR traling Stop LossThis script is a TradingView Pine Script strategy that implements a High-Low Breakout Strategy with ATR Trailing Stop.created by SK WEALTH GURU, Here’s a breakdown of its key components:
Features and Functionality
Custom Timeframe and High-Low Detection
Allows users to select a custom timeframe (default: 30 minutes) to detect high and low levels.
Tracks the high and low within a user-specified period (e.g., first 30 minutes of the session).
Draws horizontal lines for high and low, persisting for a specified number of days.
Trade Entry Conditions
Long Entry: If the closing price crosses above the recorded high.
Short Entry: If the closing price crosses below the recorded low.
The user can choose to trade Long, Short, or Both.
ATR-Based Trailing Stop & Risk Management
Uses Average True Range (ATR) with a multiplier (default: 3.5) to determine a dynamic trailing stop-loss.
Trades reset daily, ensuring a fresh start each day.
Trade Execution and Partial Profit Taking
Stop-loss: Default at 1% of entry price.
Partial profit: Books 50% of the position at 3% profit.
Max 2 trades per day: If the first trade hits stop-loss, the strategy allows one re-entry.
Intraday Exit Condition
All positions close at 3:15 PM to ensure no overnight risk.
Scalping trading system based on 4 ema linesScalping Trading System Based on 4 EMA Lines
Overview:
This is a scalping trading strategy built on signals from 4 EMA moving averages: EMA(8), EMA(12), EMA(24) and EMA(72).
Conditions:
- Time frame: H1 (1 hour).
- Trading assets: Applicable to major currency pairs with high volatility
- Risk management: Use a maximum of 1-2% of capital for each transaction. The order holding time can be from a few hours to a few days, depending on the price fluctuation amplitude.
Trading rules:
Determine the main trend:
Uptrend: EMA(8), EMA(12) and EMA(24) are above EMA(72).
Downtrend: EMA(8), EMA(12) and EMA(24) are below EMA(72).
Trade in the direction of the main trend** (buy in an uptrend and sell in a downtrend).
Entry conditions:
- Only trade in a clearly trending market.
Uptrend:
- Wait for the price to correct to the EMA(24).
- Enter a buy order when the price closes above the EMA(24).
- Place a stop loss below the bottom of the EMA(24) candle that has just been swept.
Downtrend:
- Wait for the price to correct to the EMA(24).
- Enter a sell order when the price closes below the EMA(24).
- Place a stop loss above the top of the EMA(24) candle that has just been swept.
Take profit and order management:
- Take profit when the price moves 20 to 40 pips in the direction of the trade.
Use Trailing Stop to optimize profits instead of setting a fixed Take Profit.
Note:
- Do not trade within 30 minutes before and after the announcement of important economic news, as the price may fluctuate abnormally.
Additional filters:
To increase the success rate and reduce noise, this strategy uses additional conditions:
1. The price is calculated only when the candle closes (no repaint).
2. When sweeping through EMA(24), the price needs to close above EMA(24).
3. The closing price must be higher than 50% of the candle's length.
4. **The bottom of the candle sweeping through EMA(24) must be lower than the bottom of the previous candle (liquidity sweep).
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Alert function:
When the EMA(24) sweep conditions are met, the system will trigger an alert if you have set it up.
- Entry point: The closing price of the candle sweeping through EMA(24).
- Stop Loss:
- Buy Order: Place at the bottom of the sweep candle.
- Sell Order: Place at the top of the sweep candle.
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Note:
This strategy is designed to help traders identify profitable trading opportunities based on trends. However, no strategy is 100% guaranteed to be successful. Please test it thoroughly on a demo account before using it.
Volume Weighted HMA Index | mad_tiger_slayerTitle: 🍉 Volume Weighted HMA Index | mad_tiger_slayer 🐯
Description:
The Volume Weighted HMA Index is a cutting-edge indicator designed to enhance the accuracy and responsiveness of trading signals by combining the power of volume with the Hull Moving Average (HMA). This indicator adjusts the HMA based on volume-weighted price changes, providing faster and more reliable entry and exit signals while reducing the likelihood of false signals.
Intended and Best Uses:
Used for Strategy Creation:
Extremely Quick Entries and Exits
Intended for Higher timeframe however can be used for scalping paired with additional scripts.
Can be paired to create profitable strategies
TREND FOLLOWING NOT MEAN REVERTING!!!!
[Key Features:
Volume Integration: Dynamically adjusts the HMA using volume data to prioritize higher-volume bars, ensuring that market activity plays a crucial role in signal generation.
Enhanced Signal Clarity: The indicator calculates precise long and short signals by detecting volume-weighted HMA crossovers.
Bar Coloring: Visually differentiate bullish and bearish conditions with customizable bar colors, making trends easier to identify.
Custom Signal Plotting: Optional long and short signal markers for a clear visual representation of potential trade opportunities.
Highly Configurable: Adjust parameters such as volume length and calculation source to tailor the indicator to your trading preferences and strategy.
How It Works:
Volume Weighting: The indicator calculates the HMA using a volume-weighted price change, amplifying the influence of high-volume periods on the moving average.
Trend Identification: Crossovers of the volume-weighted HMA with zero determine trend direction, where:
A bullish crossover signals a long condition.
A bearish crossunder signals a short condition.
Visual Feedback: Bar colors and optional signal markers provide real-time insights into trend direction and trading signals.
Use Cases:
Trend Following: Quickly identify emerging trends with volume-accelerated HMA calculations.
Trade Confirmation: Use the indicator to confirm the strength and validity of your trade setups.
Custom Signal Integration: Combine this indicator with your existing strategies to refine entries and exits.
Notes:
Ensure that your trading instrument provides volume data for accurate calculations. If no volume is available, the script will notify you.
This script works best when combined with other indicators or trading frameworks for a comprehensive market view.
Inspired by the community and designed for traders looking to stay ahead of the curve, the Volume Weighted HMA Index is a versatile tool for traders of all levels.
CHAKRA RISS ENGULFING CANDLESTICK STRATEGYChakra RISS Engulfing Candlestick Strategy
Type: Technical Indicator & Strategy
Platform: TradingView
Script Version: Pine Script v6
Overview:
The Chakra RISS Engulfing Candlestick Strategy combines a momentum-based approach using the Relative Strength Index (RSI) with Engulfing Candlestick Patterns to generate buy and sell signals. The strategy filters trades based on price movement relative to a 50-period Simple Moving Average (SMA), making it a trend-following strategy.
The indicator uses color-coded bars to visually represent market conditions, helping traders easily identify bullish and bearish trends. The strategy is designed to be dynamic, adapting to changing market conditions and filtering out noise using key technical indicators.
How It Works:
RSI-Based Color Conditions:
Green Bars: When the RSI crosses above a specified UpLevel (default: 50), indicating a bullish momentum and signaling potential buy conditions.
Red Bars: When the RSI crosses below a specified DownLevel (default: 50), indicating a bearish momentum and signaling potential sell conditions.
Buy Signal:
Triggered when the following conditions are met:
RSI crosses from below the UpLevel (default: 50) to above it, signaling increasing bullish momentum.
The close price is above the 50-period Simple Moving Average (SMA), confirming an uptrend.
The Buy Signal is plotted below the bar with a green arrow and a "BUY" label.
Sell Signal:
Triggered when the following conditions are met:
RSI crosses from above the DownLevel (default: 50) to below it, signaling increasing bearish momentum.
The close price is below the 50-period Simple Moving Average (SMA), confirming a downtrend.
The Sell Signal is plotted above the bar with a red arrow and a "SELL" label.
Stop Loss and Take Profit:
For long trades (buy signals), the stop loss is placed below the previous bar's low, and the take profit is set at 3% above the entry price.
For short trades (sell signals), the stop loss is placed above the previous bar's high, and the take profit is set at 3% below the entry price.
Dynamic Bar Coloring:
The bar colors change dynamically based on RSI levels:
Green Bars: Indicating a potential uptrend (bullish).
Red Bars: Indicating a potential downtrend (bearish).
These visual cues help traders quickly identify market trends and potential reversals.
Trend Filtering:
The 50-period Simple Moving Average (SMA) is used to filter trades based on the overall market trend:
Buy signals are only considered when the price is above the moving average, indicating an uptrend.
Sell signals are only considered when the price is below the moving average, indicating a downtrend.
Alerting System:
Alerts can be set for both buy and sell signals. These alerts notify traders in real-time when potential trades are generated, allowing them to act promptly.
Alerts can be configured to send notifications through email, SMS, or a webhook for integration with other services like IFTTT or Zapier.
Key Features:
RSI and Moving Average-Based Signals: Combines RSI with a moving average for more accurate trade signals.
Stop Loss and Take Profit: Dynamic risk management with custom stop loss and take profit levels based on previous high and low prices.
Buy and Sell Alerts: Provides real-time alerts when a buy or sell signal is triggered.
Trend Confirmation: Uses the 50-period Simple Moving Average to filter signals and confirm the direction of the trend.
Visual Bar Color Changes: Makes it easy to identify bullish or bearish trends with color-coded bars.
Usage:
This strategy is suitable for traders who prefer a trend-following approach and want to combine momentum indicators (RSI) with price action (Engulfing Candlestick patterns). It is particularly useful in volatile markets where quick identification of trend changes can lead to profitable trades.
Best Used For: Day trading, swing trading, and trend-following strategies.
Timeframes: Works well on various timeframes, from 1-minute charts for scalping to daily charts for swing trading.
Markets: Can be applied to any market with sufficient liquidity (stocks, forex, crypto, etc.).
Settings:
UpLevel: The RSI level above which the market is considered bullish (default: 50).
DownLevel: The RSI level below which the market is considered bearish (default: 50).
SMA Length: The period of the Simple Moving Average used to filter trades (default: 50).
Risk Management: Customizable stop loss and take profit settings based on price action (default: 3% above/below the entry price).
Uptrick: Zero Lag HMA Trend Suite1. Name and Purpose
Uptrick: Zero Lag HMA Trend Suite is a Pine Version 6 script that builds upon the Hull Moving Average (HMA) to offer an advanced trend analysis tool. Its purpose is to help traders identify trend direction, potential reversals, and overall market momentum with reduced lag compared to traditional moving averages. By combining the HMA with Average True Range (ATR) thresholds, slope-dependent coloring, Volume Weighted Average Price (VWAP) ribbons, and optional reversal signals, the script aims to give a detailed view of price activity in various market environments.
2. Overview
This script begins with the calculation of a Hull Moving Average, a method that blends Weighted Moving Averages in a way designed to cut down on lag while still smoothing out price fluctuations. Next, several enhancements are applied. The script compares current HMA values to previous ones for slope-based coloring, which highlights uptrends and downtrends at a glance. It also plots buy and sell signals when price moves beyond or below thresholds determined by the ATR and the user’s chosen signal multiplier. An optional VWAP ribbon can be shown to confirm bullish or bearish conditions relative to a volume-weighted benchmark. Additionally, the script can plot reversal signals (labeled with B) at points where price crosses back toward the HMA from above or below. Taken together, these elements allow traders to visualize both the short-term momentum and the broader context of how price interacts with volatility and overall market direction.
3. Why These Indicators Have Been Linked Together
The reason the Hull Moving Average, the Average True Range, and the VWAP have been integrated into one script is to tackle multiple facets of market analysis in a single tool. The Zero Lag Hull Moving Average provides a responsive trend line, the ATR offers a measure of volatility that helps distinguish significant price shifts from typical fluctuations, and the VWAP acts as a reference for fair value based on traded volume. By layering all three, the script helps traders avoid the need to juggle multiple separate indicators and offers a holistic perspective. The slope-based coloring focuses on trend direction, the ATR-based thresholds refine possible buy and sell zones, and the VWAP ribbons provide insight into how price stands relative to an important volume-weighted level. The inclusion of up and down signals and reversal B labels further refines entries and exits.
4. Why Use Uptrick: Zero Lag HMA Trend Suite
The Hull Moving Average is already known for reacting more quickly to price changes compared to other moving averages while retaining a degree of smoothness. This suite enhances the basic HMA by showing colored gradients that make it easy to spot trend direction changes, highlighting potential entry or exit points based on volatility-driven thresholds, and optionally layering a volume-based measure of bullish or bearish market sentiment. By relying on a zero lag approach and additional data points, the script caters to those wanting a more responsive method of identifying shifts in market dynamics. The added reversal signals and up or down alerts give traders extra confirmation for potential turning points.
5. How This Extension Improves on the Basic HMA
This extension not only plots the Hull Moving Average but also includes data-driven alerts and visual cues that traditional HMA lines do not provide. First, it offers multi-layered slope coloring, making up or down trends quickly apparent. Second, it uses ATR-based thresholds to pinpoint moments when price may be extending beyond normal volatility, thus generating buy or sell signals. Third, the script introduces an optional VWAP ribbon to indicate whether the market is trading above or below this pivotal volume-weighted benchmark, adding a further confirmation step for bullish or bearish conditions. Finally, it incorporates optional reversal signals labeled with B, indicating points where price might swing back toward the main HMA line.
6. Core Components
The script can be broken down into several primary functions and features.
a. Zero Lag HMA Calculation
Uses two Weighted Moving Averages (half-length and full-length) combined through a smoothing step based on the square root of the chosen length. This approach is designed to reduce lag significantly compared to other moving averages.
b. Slope Detection
Compares current and prior HMA values to determine if the trend is up or down. The slope-based coloring changes between turquoise shades for upward movement and magenta shades for downward movement, making trend direction immediately visible.
c. ATR-Based Thresholding for Up and Down Signals
The script calculates an Average True Range over a user-defined period, then multiplies it by a signal factor to form two bands around the HMA. When price crosses below the lower band, an up (buy) signal appears; when it crosses above the upper band, a down (sell) signal is shown.
d. Reversal Signals (B Labels)
Tracks when price transitions back toward the main HMA from an extreme zone. When enabled, these reversal points are labeled with a B and can help traders see potential turning points or mean-reversion setups.
e. VWAP Bands
An optional Volume Weighted Average Price ribbon that plots above or below the HMA, indicating bullish or bearish conditions relative to a volume-weighted price benchmark. This can also act as a kind of support/ resistance.
7. User Inputs
a. HMA Length
Controls how quickly the moving average responds to price changes. Shorter lengths react faster but can lead to more frequent signals, whereas longer lengths produce smoother lines.
b. Source
Specifies the price input, such as close or an alternative source, for the calculation. This can help align the HMA with specific trading strategies.
c. ATR Length and Signal Multiplier
Defines how the script calculates average volatility and sets thresholds for buy or sell alerts. Adjusting these values can help filter out noise or highlight more aggressive signals.
d. Slope Index
Determines how many bars to look back for detecting slope direction, influencing how sensitive the slope coloring is to small fluctuations.
e. Show Buy and Sell Signals, Reversal Signals, and VWAP
Lets users toggle the display of these features. Turning off certain elements can reduce chart clutter if traders prefer a simpler layout.
8. Calculation Process
The script’s calculation follows a step-by-step approach. It first computes two Weighted Moving Averages of the selected price source, one over half the specified length and one over the full length. It then combines these using 2*wma1 minus wma2 to reduce lag, followed by applying another weighted average using the square root of the length. Simultaneously, it computes the ATR for a user-defined period. By multiplying ATR by the signal multiplier, it establishes upper and lower bands around the HMA, where crossovers generate buy (up) or sell (down) signals. The script can also plot reversal signals (B labels) when price crosses back from these bands in the opposite direction. For the optional VWAP feature, Pine Script’s ta.vwap function is used, and differences between the HMA and VWAP levels determine the color and opacity of the ribbon.
9. Signal Generation and Filtering
The ATR-based thresholds reduce the influence of small, inconsequential price swings. When price falls below the lower band, the script issues an up (buy) signal. If price breaks above the upper band, a down (sell) signal appears. These signals are visible through labels placed near the bars. Reversal signals, labeled with B, can be turned on to help detect when price retraces from an extended area back toward the main HMA line. Traders can disable or enable these signals to match their preferred level of chart detail or risk tolerance.
10. Visualization on the Chart
The Zero HMA Lag Trend Suite aims for visual clarity. The HMA line is plotted multiple times with increasing transparency to create a gradient effect. Turquoise gradients indicate upward slopes, and magenta gradients signify downward slopes. Bar coloring can be configured to align with the slope direction, providing quick insight into current momentum. When enabled, buy or sell labels are placed under or above the bars as price crosses the ATR-defined boundaries. If the reversal option is active, B labels appear around areas where price changes direction. The optional VWAP ribbons form background bands, using distinct coloration to signal whether price is above or below the volume-weighted metric.
11. Market Adaptability
Because the script’s parameters (HMA length, ATR length, signal multiplier, and slope index) are user-configurable, it can adapt to a wide range of markets and timeframes. Intraday traders may prefer a shorter HMA length for quick signals, while swing or position traders might use a longer HMA length to filter out short-lived price changes. The source setting can also be adjusted, allowing for specialized data inputs beyond just close or open values.
12. Risk Management Considerations
The script’s signals and labels are based on past price data and volatility readings, and they do not guarantee profitable outcomes. Sharp market reversals or unforeseen fundamental events can produce false signals. Traders should combine this tool with broader risk management strategies, including stop-loss placement, position sizing, and independent market analyses. The Zero HMA Lag Trend Suite can help highlight potential opportunities, but it should not be relied upon as the sole basis for trade decisions.
13. Combining with Other Tools
Many traders choose to verify signals from the Zero HMA Lag Trend Suite using popular indicators like the Relative Strength Index (RSI), Moving Average Convergence Divergence (MACD), or even simple volume-based metrics to confirm whether a price movement has sufficient momentum. Conventional techniques such as support and resistance levels, chart patterns, or candlestick analysis can also supplement signals generated by the script’s up, down, or reversal B labels.
14. Parameter Customization and Examples
a. Short-Term Day Trading
Using a shorter HMA length (for instance, 9 or 14) and a slightly higher ATR multiplier might provide timely buy and sell signals, though it may also produce more whipsaws in choppy markets.
b. Swing or Position Trading
Selecting a longer HMA length (such as 50 or 100) with a moderate ATR multiplier can help users track more significant and sustained market moves, potentially reducing the effect of minor fluctuations.
c. Multiple Timeframe Blends
Some traders load two versions of the indicator on the same chart, one for short-term signals (with frequent B label reversals) and another for the broader trend direction, aligning entry and exit decisions with the bigger picture.
15. Realistic Expectations
Even though the Hull Moving Average helps minimize lag and the script incorporates volatility-based filters and optional VWAP overlays, it cannot predict future market behavior with complete accuracy. Periods of low liquidity or sudden market shocks can still lead to signals that do not reflect longer-term trends. Frequent parameter review and manual confirmation are advised before executing trades based solely on the script’s outputs.
16. Theoretical Background
The Hull Moving Average formula aims to balance smoothness with reactivity, accomplished by combining Weighted Moving Averages at varying lengths. By subtracting a slower average from a faster one and then applying another smoothing step with the square root of the original length, the HMA is designed to respond more promptly to price changes than typical exponential or simple moving averages. The ATR component, introduced by J. Welles Wilder, calculates the average range of price movement over a user-defined period, allowing the script to assess volatility and adapt signals accordingly. VWAP provides a volume-weighted benchmark that many institutional traders track to gauge fair intraday value.
17. Originality and Uniqueness
Although multiple HMA-based indicators can be found, Uptrick: Zero Lag HMA Trend Suite sets itself apart by merging slope-based coloring, ATR thresholds, VWAP ribbons, up or down labels, and optional reversal signals all in one cohesive platform. This synergy aims to reduce chart clutter while still giving traders a comprehensive look at trend direction, volatility, and volume-based sentiment.
18. Summary
Uptrick: Zero Lag HMA Trend Suite is a specialized trading script designed to highlight potential market trends and reversals with minimal delay. It leverages the Hull Moving Average for an adaptive yet smooth price line, pairs ATR-based thresholds for detecting possible breakouts or dips, and provides VWAP-based ribbons for added volume-weighted context. Traders can further refine their entries and exits by enabling up or down signals and reversal labels (B) where price may revert toward the HMA. Suitable for a wide range of timeframes and instrument types, the script encourages a disciplined approach to trade management and risk control.
19. Disclaimer
This script is provided for informational and educational purposes only. Trading and investing involve significant financial risk, and no indicator can guarantee success under all conditions. Users should practice robust risk management, including the placement of stop losses and position sizing, and should confirm signals with additional analysis tools. The developer of this script assumes no liability for any trading decisions or outcomes resulting from its use.
RSI OB/OS Strategy Analyzer█ OVERVIEW
The RSI OB/OS Strategy Analyzer is a comprehensive trading tool designed to help traders identify and evaluate overbought/oversold reversal opportunities using the Relative Strength Index (RSI). It provides visual signals, performance metrics, and a detailed table to analyze the effectiveness of RSI-based strategies over a user-defined lookback period.
█ KEY FEATURES
RSI Calculation
Calculates RSI with customizable period (default 14)
Plots dynamic overbought (70) and oversold (30) levels
Adds background coloring for OB/OS regions
Reversal Signals
Identifies signals based on RSI crossing OB/OS levels
Two entry strategies available:
Revert Cross: Triggers when RSI exits OB/OS zone
Cross Threshold: Triggers when RSI enters OB/OS zone
Trade Direction
Users can select a trade bias:
Long: Focuses on oversold reversals (bullish signals)
Short: Focuses on overbought reversals (bearish signals)
Performance Metrics
Calculates three key statistics for each lookback period:
Win Rate: Percentage of profitable trades
Mean Return: Average return across all trades
Median Return: Median return across all trades
Metrics calculated as percentage changes from entry price
Visual Signals
Dual-layer signal display:
BUY: Green triangles + text labels below price
SELL: Red triangles + text labels above price
Semi-transparent background highlighting in OB/OS zones
Performance Table
Interactive table showing metrics for each lookback period
Color-coded visualization:
Win Rate: Gradient from red (low) to green (high)
Returns: Green for positive, red for negative
Time Filtering
Users can define a specific time window for the indicator to analyze trades, ensuring that performance metrics are calculated only for the desired period.
Customizable Display
Adjustable table font sizes: Auto/Small/Normal/Large
Toggle option for table visibility
█ PURPOSE
The RSI OB/OS Strategy Analyzer helps traders:
Identify mean-reversion opportunities through RSI extremes
Backtest entry strategy effectiveness across multiple time horizons
Optimize trade timing through visual historical performance data
Quickly assess strategy robustness with color-coded metrics
█ IDEAL USERS
Counter-Trend Traders: Looking to capitalize on RSI extremes
Systematic Traders: Needing quantitative strategy validation
Educational Users: Studying RSI behavior in different market conditions
Multi-Timeframe Analysts: Interested in forward returns analysis
Hourly Market Movement Pattern Indicator# Hourly Market Movement Pattern Indicator
This versatile technical analysis tool identifies the most active hours for trading by analyzing historical price movements. While it can be viewed on any timeframe chart, the indicator specifically tracks and displays which hours of the day historically show the strongest upward or downward price movements, helping traders optimize their trading schedule around these recurring hourly patterns.
## Core Features
- Tracks the best performing hours for both upward and downward movements
- Viewable on any timeframe chart while maintaining hourly analysis
- Clear visual display through a color-coded table overlay
- Real-time updates with new market data
- Works with all trading instruments (stocks, crypto, forex, futures, etc.)
## Timeframe Applications
### Chart Viewing Options
- Can be viewed on any timeframe chart (1min to Monthly)
- Maintains hourly pattern analysis regardless of chart timeframe
- Helps correlate hourly patterns with your preferred trading timeframe
- Allows detailed visualization of hourly patterns within your analysis period
### Intraday Trading
- Identify the most profitable hours for trading
- Plan trading sessions around historically strong hours
- Optimize entry and exit timing based on hourly patterns
- Structure day trading schedules around peak movement hours
### Swing Trading
- Use hourly statistics to optimize entry/exit timing
- Plan trade executions during historically strong hours
- Time position entries based on hourly success rates
- Enhance swing trading decisions with hourly pattern data
## Practical Applications
### Pattern Recognition
- Track recurring hourly market movements
- Identify institutional trading hour patterns
- Detect regular market cycle hours
- Recognize changes in hourly market behavior
### Risk Management
- Adjust position sizing based on historical hourly patterns
- Plan entries during statistically favorable hours
- Time stop loss adjustments around known volatile hours
- Scale positions according to hourly success rates
### Trade Planning
- Schedule trading sessions during optimal hours
- Plan trade executions around strong movement periods
- Structure trading day around peak hours
- Time position adjustments to favorable hours
## Setup Options
- Timeframe: View on any chart timeframe while tracking hourly patterns
- Visual Display: Non-intrusive table overlay
- Color Coding: Green for upward movements, Red for downward movements
- Hour Display: 24-hour format for global market compatibility
## Trading Strategy Integration
The indicator enhances trading approaches through:
- Optimal hour identification for trade execution
- Historical hourly pattern analysis
- Day trading session optimization
- Position timing based on hourly statistics
## Notes
This indicator proves particularly valuable for:
- Traders seeking to optimize their daily trading schedule
- Day traders focusing on peak market hours
- Swing traders optimizing entry/exit timing
- Traders adapting strategies to specific market hours
- International traders tracking hour-specific patterns across sessions
The tool's hourly pattern analysis provides crucial timing information regardless of your preferred chart timeframe or trading style, helping optimize trade execution around the most statistically favorable hours of the day.
Multi-Band Comparison Strategy (CRYPTO)Multi-Band Comparison Strategy (CRYPTO)
Optimized for Cryptocurrency Trading
This Pine Script strategy is built from the ground up for traders who want to take advantage of cryptocurrency volatility using a confluence of advanced statistical bands. The strategy layers Bollinger Bands, Quantile Bands, and a unique Power-Law Band to map out crucial support/resistance zones. It then focuses on a Trigger Line—the lower standard deviation band of the upper quantile—to pinpoint precise entry and exit signals.
Key Features
Bollinger Band Overlay
The upper Bollinger Band visually shifts to yellow when price exceeds it, turning black otherwise. This offers a straightforward way to gauge heightened momentum or potential market slowdowns.
Quantile & Power-Law Integration
The script calculates upper and lower quantile bands to assess probabilistic price extremes.
A Power-Law Band is also available to measure historically significant return levels, providing further insight into overbought or oversold conditions in fast-moving crypto markets.
Standard Deviation Trigger
The lower standard deviation band of the upper quantile acts as the strategy’s trigger. If price consistently holds above this line, the strategy interprets it as a strong bullish signal (“green” zone). Conversely, dipping below indicates a “red” zone, signaling potential reversals or exits.
Consecutive Bar Confirmation
To reduce choppy signals, you can fine-tune the number of consecutive bars required to confirm an entry or exit. This helps filter out noise and false breaks—critical in the often-volatile crypto realm.
Adaptive for Multiple Timeframes
Whether you’re scalping on a 5-minute chart or swing trading on daily candles, the strategy’s flexible confirmation and overlay options cater to different market conditions and trading styles.
Complete Plot Customization
Easily toggle visibility of each band or line—Bollinger, Quantile, Power-Law, and more.
Built-in Simple and Exponential Moving Averages can be enabled to further contextualize market trends.
Why It Excels at Crypto
Cryptocurrencies are known for rapid price swings, and this strategy addresses exactly that by combining multiple statistical methods. The quantile-based confirmation reduces noise, while Bollinger and Power-Law bands help highlight breakout regions in trending markets. Traders have reported that it works seamlessly across various coins and tokens, adapting its triggers to each asset’s unique volatility profile.
Give it a try on your favorite cryptocurrency pairs. With advanced data handling, crisp visual cues, and adjustable confirmation logic, the Multi-Band Comparison Strategy provides a robust framework to capture profitable moves and mitigate risk in the ever-evolving crypto space.
ADX-DMIThis script manually calculates the Directional Movement Index (DMI) and the Average Directional Index (ADX) using Wilder’s smoothing technique. The DMI indicators are used to assess the strength and direction of a market trend. It includes three main lines: ADX (yellow), DI+ (green), and DI− (red). Traders use these indicators to determine whether a trend is strong and in which direction it is moving.
The process begins by defining the length parameter, which determines how many periods are considered in the calculation. It then calculates the True Range (TR), which is the greatest of three values: the difference between the current high and low, the difference between the current high and the previous close, and the difference between the current low and the previous close. This TR is used to compute the Average True Range (ATR), which smooths out price fluctuations to get a clearer picture of the market’s volatility. Next, the script calculates the +DM (positive directional movement) and -DM (negative directional movement) based on the changes in the highs and lows from one period to the next.
Finally, the script computes the DI+ and DI− values by dividing the smoothed +DM and -DM by the ATR and multiplying by 100 to express them as percentages. The DX value is calculated as the absolute difference between DI+ and DI−, normalized by the sum of both values. The ADX is then derived by smoothing the DX value over the specified length. The three indicators — ADX, DI+, and DI− — are plotted in the lower chart panel, providing traders with visual cues about the trend’s direction (DI+ and DI−) and strength (ADX).
Important Notice:
The use of technical indicators like this one does not guarantee profitable results. This indicator should not be used as a standalone analysis tool. It is essential to combine it with other forms of analysis, such as fundamental analysis, risk management strategies, and awareness of current market conditions. Always conduct thorough research.
Note: The effectiveness of any technical indicator can vary based on market conditions and individual trading styles. It's crucial to test indicators thoroughly using historical data before applying them in live trading scenarios.
Disclaimer:
Trading financial instruments involves substantial risk and may not be suitable for all investors. Past performance is not indicative of future results. This indicator is provided for informational and educational purposes only and should not be considered investment advice. Always conduct your own research before making any trading decisions.
Uptrick: Fisher Eclipse1. Name and Purpose
Uptrick: Fisher Eclipse is a Pine version 6 extension of the basic Fisher Transform indicator that focuses on highlighting potential turning points in price data. Its purpose is to allow traders to spot shifts in momentum, detect divergence, and adapt signals to different market environments. By combining a core Fisher Transform with additional signal processing, divergence detection, and customizable aggressiveness settings, this script aims to help users see when a price move might be losing momentum or gaining strength.
2. Overview
This script uses a Fisher Transform calculation on the average of each bar’s high and low (hl2). The Fisher Transform is designed to amplify price extremes by mapping data into a different scale, making potential reversals more visible than they might be with standard oscillators. Uptrick: Fisher Eclipse takes this concept further by integrating a signal line, divergence detection, bar coloring for momentum intensity, and optional thresholds to reduce unwanted noise.
3. Why Use the Fisher Transform
The Fisher Transform is known for converting relatively smoothed price data into a more pronounced scale. This transformation highlights where markets may be overextended. In many cases, standard oscillators move gently, and traders can miss subtle hints that a reversal might be approaching. The Fisher Transform’s mathematical approach tightens the range of values and sharpens the highs and lows. This behavior can allow traders to see clearer peaks and troughs in momentum. Because it is often quite responsive, it can help anticipate areas where price might change direction, especially when compared to simpler moving averages or traditional oscillators. The result is a more evident signal of possible overbought or oversold conditions.
4. How This Extension Improves on the Basic Fisher Transform
Uptrick: Fisher Eclipse adds multiple features to the classic Fisher framework in order to address different trading styles and market behaviors:
a) Divergence Detection
The script can detect bullish or bearish divergences between price and the oscillator over a chosen lookback period, helping traders anticipate shifts in market direction.
b) Bar Coloring
When momentum exceeds a certain threshold (default 3), bars can be colored to highlight surges of buying or selling pressure. This quick visual reference can assist in spotting periods of heightened activity. After a bar color like this, usually, there is a quick correction as seen in the image below.
c) Signal Aggressiveness Levels
Users can choose between conservative, moderate, or aggressive signal thresholds. This allows them to tune how quickly the indicator flags potential entries or exits. Aggressive settings might suit scalpers who need rapid signals, while conservative settings may benefit swing traders preferring fewer, more robust indications.
d) Minimum Movement Filter
A configurable filter can be set to ensure that the Fisher line and its signal have a sufficient gap before triggering a buy or sell signal. This step is useful for traders seeking to minimize signals during choppy or sideways markets. This can be used to eliminate noise as well.
By combining all these elements into one package, the indicator attempts to offer a comprehensive toolkit for those who appreciate the Fisher Transform’s clarity but also desire more versatility.
5. Core Components
a) Fisher Transform
The script calculates a Fisher value using normalized price over a configurable length, highlighting potential peaks and troughs.
b) Signal Line
The Fisher line is smoothed using a short Simple Moving Average. Crossovers and crossunders are one of the key ways this indicator attempts to confirm momentum shifts.
c) Divergence Logic
The script looks back over a set number of bars to compare current highs and lows of both price and the Fisher oscillator. When price and the oscillator move in opposing directions, a divergence may occur, suggesting a possible upcoming reversal or weakening trend.
d) Thresholds for Overbought and Oversold
Horizontal lines are drawn at user-chosen overbought and oversold levels. These lines help traders see when momentum readings reach particular extremes, which can be especially relevant when combined with crossovers in that region.
e) Intensity Filter and Bar Coloring
If the magnitude of the change in the Fisher Transform meets or exceeds a specified threshold, bars are recolored. This provides a visual cue for significant momentum changes.
6. User Inputs
a) length
Defines how many bars the script looks back to compute the highest high and lowest low for the Fisher Transform. A smaller length reacts more quickly but can be noisier, while a larger length smooths out the indicator at the cost of responsiveness.
b) signal aggressiveness
Adjusts the buy and sell thresholds for conservative, moderate, and aggressive trading styles. This can be key in matching the indicator to personal risk preferences or varying market conditions. Conservative will give you less signals and aggressive will give you more signals.
c) minimum movement filter
Specifies how far apart the Fisher line and its signal line must be before generating a valid crossover signal.
d) divergence lookback
Controls how many bars are examined when determining if price and the oscillator are diverging. A larger setting might generate fewer signals, while a smaller one can provide more frequent alerts.
e) intensity threshold
Determines how large a change in the Fisher value must be for the indicator to recolor bars. Strong momentum surges become more noticeable.
f) overbought level and oversold level
Lets users define where they consider market conditions to be stretched on the upside or downside.
7. Calculation Process
a) Price Input
The script uses the midpoint of each bar’s high and low, sometimes referred to as hl2.
hl2 = (high + low) / 2
b) Range Normalization
Determine the maximum (maxHigh) and minimum (minLow) values over a user-defined lookback period (length).
Scale the hl2 value so it roughly fits between -1 and +1:
value = 2 * ((hl2 - minLow) / (maxHigh - minLow) - 0.5)
This step highlights the bar’s current position relative to its recent highs and lows.
c) Fisher Calculation
Convert the normalized value into the Fisher Transform:
fisher = 0.5 * ln( (1 + value) / (1 - value) ) + 0.5 * fisher_previous
fisher_previous is simply the Fisher value from the previous bar. Averaging half of the new transform with half of the old value smooths the result slightly and can prevent erratic jumps.
ln is the natural logarithm function, which compresses or expands values so that market turns often become more obvious.
d) Signal Smoothing
Once the Fisher value is computed, a short Simple Moving Average (SMA) is applied to produce a signal line. In code form, this often looks like:
signal = sma(fisher, 3)
Crossovers of the fisher line versus the signal line can be used to hint at changes in momentum:
• A crossover occurs when fisher moves from below to above the signal.
• A crossunder occurs when fisher moves from above to below the signal.
e) Threshold Checking
Users typically define oversold and overbought levels (often -1 and +1).
Depending on aggressiveness settings (conservative, moderate, aggressive), these thresholds are slightly shifted to filter out or include more signals.
For example, an oversold threshold of -1 might be used in a moderate setting, whereas -1.5 could be used in a conservative setting to require a deeper dip before triggering.
f) Divergence Checks
The script looks back a specified number of bars (divergenceLookback). For both price and the fisher line, it identifies:
• priceHigh = the highest hl2 within the lookback
• priceLow = the lowest hl2 within the lookback
• fisherHigh = the highest fisher value within the lookback
• fisherLow = the lowest fisher value within the lookback
If price forms a lower low while fisher forms a higher low, it can signal a bullish divergence. Conversely, if price forms a higher high while fisher forms a lower high, a bearish divergence might be indicated.
g) Bar Coloring
The script monitors the absolute change in Fisher values from one bar to the next (sometimes called fisherChange):
fisherChange = abs(fisher - fisher )
If fisherChange exceeds a user-defined intensityThreshold, bars are recolored to highlight a surge of momentum. Aqua might indicate a strong bullish surge, while purple might indicate a strong bearish surge.
This color-coding provides a quick visual cue for traders looking to spot large momentum swings without constantly monitoring indicator values.
8. Signal Generation and Filtering
Buy and sell signals occur when the Fisher line crosses the signal line in regions defined as oversold or overbought. The optional minimum movement filter prevents triggering if Fisher and its signal line are too close, reducing the chance of small, inconsequential price fluctuations creating frequent signals. Divergences that appear in oversold or overbought regions can serve as additional evidence that momentum might soon shift.
9. Visualization on the Chart
Uptrick: Fisher Eclipse plots two lines: the Fisher line in one color and the signal line in a contrasting shade. The chart displays horizontal dashed lines where the overbought and oversold levels lie. When the Fisher Transform experiences a sharp jump or drop above the intensity threshold, the corresponding price bars may change color, signaling that momentum has undergone a noticeable shift. If the indicator detects bullish or bearish divergence, dotted lines are drawn on the oscillator portion to connect the relevant points.
10. Market Adaptability
Because of the different aggressiveness levels and the optional minimum movement filter, Uptrick: Fisher Eclipse can be tailored to multiple trading styles. For instance, a short-term scalper might select a smaller length and more aggressive thresholds, while a swing trader might choose a longer length for smoother readings, along with conservative thresholds to ensure fewer but potentially stronger signals. During strongly trending markets, users might rely more on divergences or large intensity changes, whereas in a range-bound market, oversold or overbought conditions may be more frequent.
11. Risk Management Considerations
Indicators alone do not ensure favorable outcomes, and relying solely on any one signal can be risky. Using a stop-loss or other protections is often suggested, especially in fast-moving or unpredictable markets. Divergence can appear before a market reversal actually starts. Similarly, a Fisher Transform can remain in an overbought or oversold region for extended periods, especially if the trend is strong. Cautious interpretation and confirmation with additional methods or chart analysis can help refine entry and exit decisions.
12. Combining with Other Tools
Traders can potentially strengthen signals from Uptrick: Fisher Eclipse by checking them against other methods. If a moving average cross or a price pattern aligns with a Fisher crossover, the combined evidence might provide more certainty. Volume analysis may confirm whether a shift in market direction has participation from a broad set of traders. Support and resistance zones could reinforce overbought or oversold signals, particularly if price reaches a historical boundary at the same time the oscillator indicates a possible reversal.
13. Parameter Customization and Examples
Some short-term traders run a 15-minute chart, with a shorter length setting, aggressively tight oversold and overbought thresholds, and a smaller divergence lookback. This approach produces more frequent signals, which may appeal to those who enjoy fast-paced trading. More conservative traders might apply the indicator to a daily chart, using a larger length, moderate threshold levels, and a bigger divergence lookback to focus on broader market swings. Results can differ, so it may be helpful to conduct thorough historical testing to see which combination of parameters aligns best with specific goals.
14. Realistic Expectations
While the Fisher Transform can reveal potential turning points, no mathematical tool can predict future price behavior with full certainty. Markets can behave erratically, and a period of strong trending may see the oscillator pinned in an extreme zone without a significant reversal. Divergence signals sometimes appear well before an actual trend change occurs. Recognizing these limitations helps traders manage risk and avoids overreliance on any one aspect of the script’s output.
15. Theoretical Background
The Fisher Transform uses a logarithmic formula to map a normalized input, typically ranging between -1 and +1, into a scale that can fluctuate around values like -3 to +3. Because the transformation exaggerates higher and lower readings, it becomes easier to spot when the market might have stretched too far, too fast. Uptrick: Fisher Eclipse builds on that foundation by adding a series of practical tools that help confirm or refine those signals.
16. Originality and Uniqueness
Uptrick: Fisher Eclipse is not simply a duplicate of the basic Fisher Transform. It enhances the original design in several ways, including built-in divergence detection, bar-color triggers for momentum surges, thresholds for overbought and oversold levels, and customizable signal aggressiveness. By unifying these concepts, the script seeks to reduce noise and highlight meaningful shifts in market direction. It also places greater emphasis on helping traders adapt the indicator to their specific style—whether that involves frequent intraday signals or fewer, more robust alerts over longer timeframes.
17. Summary
Uptrick: Fisher Eclipse is an expanded take on the original Fisher Transform oscillator, including divergence detection, bar coloring based on momentum strength, and flexible signal thresholds. By adjusting parameters like length, aggressiveness, and intensity thresholds, traders can configure the script for day-trading, swing trading, or position trading. The indicator endeavors to highlight where price might be shifting direction, but it should still be combined with robust risk management and other analytical methods. Doing so can lead to a more comprehensive view of market conditions.
18. Disclaimer
No indicator or script can guarantee profitable outcomes in trading. Past performance does not necessarily suggest future results. Uptrick: Fisher Eclipse is provided for educational and informational purposes. Users should apply their own judgment and may want to confirm signals with other tools and methods. Deciding to open or close a position remains a personal choice based on each individual’s circumstances and risk tolerance.
MMXM ICT [TradingFinder] Market Maker Model PO3 CHoCH/CSID + FVG🔵 Introduction
The MMXM Smart Money Reversal leverages key metrics such as SMT Divergence, Liquidity Sweep, HTF PD Array, Market Structure Shift (MSS) or (ChoCh), CISD, and Fair Value Gap (FVG) to identify critical turning points in the market. Designed for traders aiming to analyze the behavior of major market participants, this setup pinpoints strategic areas for making informed trading decisions.
The document introduces the MMXM model, a trading strategy that identifies market maker activity to predict price movements. The model operates across five distinct stages: original consolidation, price run, smart money reversal, accumulation/distribution, and completion. This systematic approach allows traders to differentiate between buyside and sellside curves, offering a structured framework for interpreting price action.
Market makers play a pivotal role in facilitating these movements by bridging liquidity gaps. They continuously quote bid (buy) and ask (sell) prices for assets, ensuring smooth trading conditions.
By maintaining liquidity, market makers prevent scenarios where buyers are left without sellers and vice versa, making their activity a cornerstone of the MMXM strategy.
SMT Divergence serves as the first signal of a potential trend reversal, arising from discrepancies between the movements of related assets or indices. This divergence is detected when two or more highly correlated assets or indices move in opposite directions, signaling a likely shift in market trends.
Liquidity Sweep occurs when the market targets liquidity in specific zones through false price movements. This process allows major market participants to execute their orders efficiently by collecting the necessary liquidity to enter or exit positions.
The HTF PD Array refers to premium and discount zones on higher timeframes. These zones highlight price levels where the market is in a premium (ideal for selling) or discount (ideal for buying). These areas are identified based on higher timeframe market behavior and guide traders toward lucrative opportunities.
Market Structure Shift (MSS), also referred to as ChoCh, indicates a change in market structure, often marked by breaking key support or resistance levels. This shift confirms the directional movement of the market, signaling the start of a new trend.
CISD (Change in State of Delivery) reflects a transition in price delivery mechanisms. Typically occurring after MSS, CISD confirms the continuation of price movement in the new direction.
Fair Value Gap (FVG) represents zones where price imbalance exists between buyers and sellers. These gaps often act as price targets for filling, offering traders opportunities for entry or exit.
By combining all these metrics, the Smart Money Reversal provides a comprehensive tool for analyzing market behavior and identifying key trading opportunities. It enables traders to anticipate the actions of major players and align their strategies accordingly.
MMBM :
MMSM :
🔵 How to Use
The Smart Money Reversal operates in two primary states: MMBM (Market Maker Buy Model) and MMSM (Market Maker Sell Model). Each state highlights critical structural changes in market trends, focusing on liquidity behavior and price reactions at key levels to offer precise and effective trading opportunities.
The MMXM model expands on this by identifying five distinct stages of market behavior: original consolidation, price run, smart money reversal, accumulation/distribution, and completion. These stages provide traders with a detailed roadmap for interpreting price action and anticipating market maker activity.
🟣 Market Maker Buy Model
In the MMBM state, the market transitions from a bearish trend to a bullish trend. Initially, SMT Divergence between related assets or indices reveals weaknesses in the bearish trend. Subsequently, a Liquidity Sweep collects liquidity from lower levels through false breakouts.
After this, the price reacts to discount zones identified in the HTF PD Array, where major market participants often execute buy orders. The market confirms the bullish trend with a Market Structure Shift (MSS) and a change in price delivery state (CISD). During this phase, an FVG emerges as a key trading opportunity. Traders can open long positions upon a pullback to this FVG zone, capitalizing on the bullish continuation.
🟣 Market Maker Sell Model
In the MMSM state, the market shifts from a bullish trend to a bearish trend. Here, SMT Divergence highlights weaknesses in the bullish trend. A Liquidity Sweep then gathers liquidity from higher levels.
The price reacts to premium zones identified in the HTF PD Array, where major sellers enter the market and reverse the price direction. A Market Structure Shift (MSS) and a change in delivery state (CISD) confirm the bearish trend. The FVG then acts as a target for the price. Traders can initiate short positions upon a pullback to this FVG zone, profiting from the bearish continuation.
Market makers actively bridge liquidity gaps throughout these stages, quoting continuous bid and ask prices for assets. This ensures that trades are executed seamlessly, even during periods of low market participation, and supports the structured progression of the MMXM model.
The price’s reaction to FVG zones in both states provides traders with opportunities to reduce risk and enhance precision. These pullbacks to FVG zones not only represent optimal entry points but also create avenues for maximizing returns with minimal risk.
🔵 Settings
Higher TimeFrame PD Array : Selects the timeframe for identifying premium/discount arrays on higher timeframes.
PD Array Period : Specifies the number of candles for identifying key swing points.
ATR Coefficient Threshold : Defines the threshold for acceptable volatility based on ATR.
Max Swing Back Method : Choose between analyzing all swings ("All") or a fixed number ("Custom").
Max Swing Back : Sets the maximum number of candles to consider for swing analysis (if "Custom" is selected).
Second Symbol for SMT : Specifies the second asset or index for detecting SMT divergence.
SMT Fractal Periods : Sets the number of candles required to identify SMT fractals.
FVG Validity Period : Defines the validity duration for FVG zones.
MSS Validity Period : Sets the validity duration for MSS zones.
FVG Filter : Activates filtering for FVG zones based on width.
FVG Filter Type : Selects the filtering level from "Very Aggressive" to "Very Defensive."
Mitigation Level FVG : Determines the level within the FVG zone (proximal, 50%, or distal) that price reacts to.
Demand FVG : Enables the display of demand FVG zones.
Supply FVG : Enables the display of supply FVG zones.
Zone Colors : Allows customization of colors for demand and supply FVG zones.
Bottom Line & Label : Enables or disables the SMT divergence line and label from the bottom.
Top Line & Label : Enables or disables the SMT divergence line and label from the top.
Show All HTF Levels : Displays all premium/discount levels on higher timeframes.
High/Low Levels : Activates the display of high/low levels.
Color Options : Customizes the colors for high/low lines and labels.
Show All MSS Levels : Enables display of all MSS zones.
High/Low MSS Levels : Activates the display of high/low MSS levels.
Color Options : Customizes the colors for MSS lines and labels.
🔵 Conclusion
The Smart Money Reversal model represents one of the most advanced tools for technical analysis, enabling traders to identify critical market turning points. By leveraging metrics such as SMT Divergence, Liquidity Sweep, HTF PD Array, MSS, CISD, and FVG, traders can predict future price movements with precision.
The price’s interaction with key zones such as PD Array and FVG, combined with pullbacks to imbalance areas, offers exceptional opportunities with favorable risk-to-reward ratios. This approach empowers traders to analyze the behavior of major market participants and adopt professional strategies for entry and exit.
By employing this analytical framework, traders can reduce errors, make more informed decisions, and capitalize on profitable opportunities. The Smart Money Reversal focuses on liquidity behavior and structural changes, making it an indispensable tool for financial market success.
Forex Hammer and Hanging Man StrategyThe strategy is based on two key candlestick chart patterns: Hammer and Hanging Man. These chart patterns are widely used in technical analysis to identify potential reversal points in the market. Their relevance in the Forex market, known for its high liquidity and volatile price movements, is particularly pronounced. Both patterns provide insights into market sentiment and trader psychology, which are critical in currency trading, where short-term volatility plays a significant role.
1. Hammer:
• Typically occurs after a downtrend.
• Signals a potential trend reversal to the upside.
• A Hammer has:
• A small body (close and open are close to each other).
• A long lower shadow, at least twice as long as the body.
• No or a very short upper shadow.
2. Hanging Man:
• Typically occurs after an uptrend.
• Signals a potential reversal to the downside.
• A Hanging Man has:
• A small body, similar to the Hammer.
• A long lower shadow, at least twice as long as the body.
• A small or no upper shadow.
These patterns are a manifestation of market psychology, specifically the tug-of-war between buyers and sellers. The Hammer reflects a situation where sellers tried to push the price down but were overpowered by buyers, while the Hanging Man shows that buyers failed to maintain the upward movement, and sellers could take control.
Relevance of Chart Patterns in Forex
In the Forex market, chart patterns are vital tools because they offer insights into price action and market sentiment. Since Forex trading often involves large volumes of trades, chart patterns like the Hammer and Hanging Man are important for recognizing potential shifts in market momentum. These patterns are a part of technical analysis, which aims to forecast future price movements based on historical data, relying on the psychology of market participants.
Scientific Literature on the Relevance of Candlestick Patterns
1. Behavioral Finance and Candlestick Patterns:
Research on behavioral finance supports the idea that candlestick patterns, such as the Hammer and Hanging Man, are relevant because they reflect shifts in trader psychology and sentiment. According to Lo, Mamaysky, and Wang (2000), patterns like these could be seen as representations of collective investor behavior, influenced by overreaction, optimism, or pessimism, and can often signal reversals in market trends.
2. Statistical Validation of Chart Patterns:
Studies by Brock, Lakonishok, and LeBaron (1992) explored the profitability of technical analysis strategies, including candlestick patterns, and found evidence that certain patterns, such as the Hammer, can have predictive value in financial markets. While their study primarily focused on stock markets, their findings are generally applicable to the Forex market as well.
3. Market Efficiency and Candlestick Patterns:
The efficient market hypothesis (EMH) posits that all available information is reflected in asset prices, but some studies suggest that markets may not always be perfectly efficient, allowing for profitable exploitation of certain chart patterns. For instance, Jegadeesh and Titman (1993) found that momentum strategies, which often rely on price patterns and trends, could generate significant returns, suggesting that patterns like the Hammer or Hanging Man may provide a slight edge, particularly in short-term Forex trading.
Testing the Strategy in Forex Using the Provided Script
The provided script allows traders to test and evaluate the Hammer and Hanging Man patterns in Forex trading by entering positions when these patterns appear and holding the position for a specified number of periods. This strategy can be tested to assess its performance across different currency pairs and timeframes.
1. Testing on Different Timeframes:
• The effectiveness of candlestick patterns can vary across different timeframes, as market dynamics change with the level of detail in each timeframe. Shorter timeframes may provide more frequent signals, but with higher noise, while longer timeframes may produce more reliable signals, but with fewer opportunities. This multi-timeframe analysis could be an area to explore to enhance the strategy’s robustness.
2. Exit Strategies:
• The script incorporates an exit strategy where positions are closed after holding them for a specified number of periods. This is useful for testing how long the reversal patterns typically take to play out and when the optimal exit occurs for maximum profitability. It can also help to adjust the exit logic based on real-time market behavior.
Conclusion
The Hammer and Hanging Man patterns are widely recognized in technical analysis as potential reversal signals, and their application in Forex trading is valuable due to the market’s high volatility and liquidity. This strategy leverages these candlestick patterns to enter and exit trades based on shifts in market sentiment and psychology. Testing and optimization, as offered by the script, can help refine the strategy and improve its effectiveness.
For further refinement, it could be valuable to consider combining candlestick patterns with other technical indicators or using multi-timeframe analysis to confirm patterns and increase the probability of successful trades.
References:
• Lo, A. W., Mamaysky, H., & Wang, J. (2000). Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation. The Journal of Finance, 55(4), 1705-1770.
• Brock, W., Lakonishok, J., & LeBaron, B. (1992). Simple Technical Trading Rules and the Stochastic Properties of Stock Returns. The Journal of Finance, 47(5), 1731-1764.
• Jegadeesh, N., & Titman, S. (1993). Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency. The Journal of Finance, 48(1), 65-91.
This provides a theoretical basis for the use of candlestick patterns in trading, supported by academic literature and research on market psychology and efficiency.
Adaptive Momentum Reversion StrategyThe Adaptive Momentum Reversion Strategy: An Empirical Approach to Market Behavior
The Adaptive Momentum Reversion Strategy seeks to capitalize on market price dynamics by combining concepts from momentum and mean reversion theories. This hybrid approach leverages a Rate of Change (ROC) indicator along with Bollinger Bands to identify overbought and oversold conditions, triggering trades based on the crossing of specific thresholds. The strategy aims to detect momentum shifts and exploit price reversions to their mean.
Theoretical Framework
Momentum and Mean Reversion: Momentum trading assumes that assets with a recent history of strong performance will continue in that direction, while mean reversion suggests that assets tend to return to their historical average over time (Fama & French, 1988; Poterba & Summers, 1988). This strategy incorporates elements of both, looking for periods when momentum is either overextended (and likely to revert) or when the asset’s price is temporarily underpriced relative to its historical trend.
Rate of Change (ROC): The ROC is a straightforward momentum indicator that measures the percentage change in price over a specified period (Wilder, 1978). The strategy calculates the ROC over a 2-period window, making it responsive to short-term price changes. By using ROC, the strategy aims to detect price acceleration and deceleration.
Bollinger Bands: Bollinger Bands are used to identify volatility and potential price extremes, often signaling overbought or oversold conditions. The bands consist of a moving average and two standard deviation bounds that adjust dynamically with price volatility (Bollinger, 2002).
The strategy employs two sets of Bollinger Bands: one for short-term volatility (lower band) and another for longer-term trends (upper band), with different lengths and standard deviation multipliers.
Strategy Construction
Indicator Inputs:
ROC Period: The rate of change is computed over a 2-period window, which provides sensitivity to short-term price fluctuations.
Bollinger Bands:
Lower Band: Calculated with a 18-period length and a standard deviation of 1.7.
Upper Band: Calculated with a 21-period length and a standard deviation of 2.1.
Calculations:
ROC Calculation: The ROC is computed by comparing the current close price to the close price from rocPeriod days ago, expressing it as a percentage.
Bollinger Bands: The strategy calculates both upper and lower Bollinger Bands around the ROC, using a simple moving average as the central basis. The lower Bollinger Band is used as a reference for identifying potential long entry points when the ROC crosses above it, while the upper Bollinger Band serves as a reference for exits, when the ROC crosses below it.
Trading Conditions:
Long Entry: A long position is initiated when the ROC crosses above the lower Bollinger Band, signaling a potential shift from a period of low momentum to an increase in price movement.
Exit Condition: A position is closed when the ROC crosses under the upper Bollinger Band, or when the ROC drops below the lower band again, indicating a reversal or weakening of momentum.
Visual Indicators:
ROC Plot: The ROC is plotted as a line to visualize the momentum direction.
Bollinger Bands: The upper and lower bands, along with their basis (simple moving averages), are plotted to delineate the expected range for the ROC.
Background Color: To enhance decision-making, the strategy colors the background when extreme conditions are detected—green for oversold (ROC below the lower band) and red for overbought (ROC above the upper band), indicating potential reversal zones.
Strategy Performance Considerations
The use of Bollinger Bands in this strategy provides an adaptive framework that adjusts to changing market volatility. When volatility increases, the bands widen, allowing for larger price movements, while during quieter periods, the bands contract, reducing trade signals. This adaptiveness is critical in maintaining strategy effectiveness across different market conditions.
The strategy’s pyramiding setting is disabled (pyramiding=0), ensuring that only one position is taken at a time, which is a conservative risk management approach. Additionally, the strategy includes transaction costs and slippage parameters to account for real-world trading conditions.
Empirical Evidence and Relevance
The combination of momentum and mean reversion has been widely studied and shown to provide profitable opportunities under certain market conditions. Studies such as Jegadeesh and Titman (1993) confirm that momentum strategies tend to work well in trending markets, while mean reversion strategies have been effective during periods of high volatility or after sharp price movements (De Bondt & Thaler, 1985). By integrating both strategies into one system, the Adaptive Momentum Reversion Strategy may be able to capitalize on both trending and reverting market behavior.
Furthermore, research by Chan (1996) on momentum-based trading systems demonstrates that adaptive strategies, which adjust to changes in market volatility, often outperform static strategies, providing a compelling rationale for the use of Bollinger Bands in this context.
Conclusion
The Adaptive Momentum Reversion Strategy provides a robust framework for trading based on the dual concepts of momentum and mean reversion. By using ROC in combination with Bollinger Bands, the strategy is capable of identifying overbought and oversold conditions while adapting to changing market conditions. The use of adaptive indicators ensures that the strategy remains flexible and can perform across different market environments, potentially offering a competitive edge for traders who seek to balance risk and reward in their trading approaches.
References
Bollinger, J. (2002). Bollinger on Bollinger Bands. McGraw-Hill Professional.
Chan, L. K. C. (1996). Momentum, Mean Reversion, and the Cross-Section of Stock Returns. Journal of Finance, 51(5), 1681-1713.
De Bondt, W. F., & Thaler, R. H. (1985). Does the Stock Market Overreact? Journal of Finance, 40(3), 793-805.
Fama, E. F., & French, K. R. (1988). Permanent and Temporary Components of Stock Prices. Journal of Political Economy, 96(2), 246-273.
Jegadeesh, N., & Titman, S. (1993). Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency. Journal of Finance, 48(1), 65-91.
Poterba, J. M., & Summers, L. H. (1988). Mean Reversion in Stock Prices: Evidence and Implications. Journal of Financial Economics, 22(1), 27-59.
Wilder, J. W. (1978). New Concepts in Technical Trading Systems. Trend Research.
rsi wf breakoutRSI Breakout Asif
RSI Breakout Asif Indicator
Overview:
The RSI Breakout Asif indicator is a custom script designed to analyze and highlight potential
breakout points using the Relative Strength Index (RSI) combined with Williams Fractals. This
indicator is specifically developed for traders who want to identify key momentum shifts in the
market.
Features:
1. RSI Analysis:
- The RSI is calculated using a user-defined length and price source.
- Horizontal lines are plotted at levels 70 (overbought), 50 (neutral), and 30 (oversold) to visually
aid decision-making.
2. Williams Fractals on RSI:
- Detects fractal highs and lows based on RSI values.
- Highlights these fractal points with dynamic, symmetrical lines for better visibility.
3. Customization:
- Users can adjust the RSI length and price source for personalized analysis.
- Fractal settings (left and right bar length) are also adjustable, making the indicator versatile for
different trading styles.
4. Visual Enhancements:
- Fractal highs are marked in red, while fractal lows are marked in green.
Asif - Page 1
RSI Breakout Asif
- Precise line placement ensures clarity and reduces chart clutter.
5. Practical Utility:
- Use the fractal breakout signals in conjunction with other technical indicators for enhanced
decision-making.
Usage:
- Add the RSI Breakout Asif indicator to your TradingView chart.
- Adjust the settings according to your trading strategy.
- Observe the RSI values and fractal points to identify potential breakout zones.
Disclaimer:
This indicator is a technical analysis tool and should be used in combination with other analysis
methods. It does not guarantee profitable trades.
Watermarked by Asif.
Asif - Page 2
Gold Trade Setup Strategy
Title: Profitable Gold Setup Strategy with Adaptive Moving Average & Supertrend
Introduction:
This trading strategy for Gold (XAU/USD) combines the Adaptive Moving Average (AMA) and Supertrend, tailored for high-probability setups during specific trading hours. The AMA identifies the trend, while the Supertrend confirms entry and exit points. The strategy is optimized for swing and intraday traders looking to capitalize on Gold’s price movements with precise trade timing.
Strategy Components:
1. Adaptive Moving Average (AMA):
• Reacts dynamically to market conditions, filtering noise in choppy markets.
• Serves as the primary trend indicator.
2. Supertrend:
• Confirms entry signals with clear buy and sell levels.
• Acts as a trailing stop-loss to protect profits.
Trading Rules:
Trading Hours:
• Only take trades between 8:30 AM and 10:30 PM IST.
• Avoid trading outside these hours to reduce noise and low-volume setups.
Buy Setup:
1. Trend Confirmation: The Adaptive Moving Average (AMA) must be green.
2. Signal Confirmation: The Supertrend should turn green after the AMA is green.
3. Trigger: Take the trade when the high of the trigger candle (the candle that turned Supertrend green) is broken.
Sell Setup (Optional if included):
• Reverse the rules for a short trade: AMA and Supertrend should both indicate bearish conditions (red), and take the trade when the low of the trigger candle is broken.
Stop-Loss and Targets:
• Place the stop-loss at the low of the trigger candle for long trades.
• Set a 1:2 risk-reward ratio or use the Supertrend line as a trailing stop-loss.
Timeframes:
• Recommended timeframes: 1H, 4H, or Daily for swing trading.
• For intraday trading, use 15-minute or 30-minute charts.
Why This Strategy Works:
• Combines trend-following (AMA) with momentum-based entries (Supertrend).
• Focused trading hours filter out low-probability setups.
• Provides precise entry, stop-loss, and target levels for disciplined trading.
Conclusion:
This Gold Setup Strategy is designed for traders seeking a structured approach to trading Gold. Follow the rules strictly, backtest the strategy extensively, and share your results. Let’s master the Gold market together!
Tags: #Gold #XAUUSD #SwingTrading #Intraday #Supertrend #AMA #TechnicalAnalysis #GoldStrategy
ChristmasWishing You All a Merry Christmas! 🎄☃️❄️
May your charts be ever-green, your trades profitable, and your holiday season filled with joy, warmth, and magic. Happy Holidays!
