Market Sentiment Fear and Greed [AlgoAlpha]Unleash the power of sentiment analysis with the Market Sentiment Fear and Greed Indicator! 📈💡 This tool provides insights into market sentiment, helping you make informed trading decisions. Let's dive into its key features and how it works. 🚀✨
Key Features 🎯
🧠 Sentiment Analysis : Calculates market sentiment using volume and price data. 📊
📅 Customizable Lookback Window : Adjust the lookback period to fine-tune sensitivity. 🔧
🎨 Bullish and Bearish Colors : Visualize trends with customizable colors. 🟢🔴
🚀 Impulse Detection : Identifies bullish and bearish impulses for trend confirmation. 🔍
📉 Normalized Sentiment Index : Offers a normalized view of market sentiment. 📊
🔔 Alerts : Set alerts for key sentiment changes and trend impulses. 🚨
🟢🔴 Table Visualization : Displays sentiment strength using a gradient color table. 🗂️
How to Use 📖
Maximize your trading potential with this indicator by following these steps:
🔍 Add the Indicator : Search for "Market Sentiment Fear and Greed " in TradingView's Indicators & Strategies. Customize settings like the lookback window and trend breakout threshold to suit your trading strategy.
📊 Monitor Sentiment : Watch the sentiment gauge and plot changes to detect market sentiment shifts. Use the Normalized Sentiment Index for a more balanced view.
🚨 Set Alerts : Enable alerts for sentiment flips and trend impulses to stay ahead of market movements.
How It Works ⚙️
The indicator calculates market sentiment by averaging the volume and closing prices over a user-defined lookback period, creating a sentiment score. It differentiates between bullish and bearish sentiment by evaluating whether the closing price is higher or lower than the opening price, summing the respective volumes. The true sentiment is determined by comparing these summed values, with a positive score indicating bullish sentiment and a negative score indicating bearish sentiment. The indicator further normalizes this sentiment score by dividing it by the EMA of the highest high minus the lowest low over double the lookback period, ensuring values are constrained between -1 and 1. Bullish and bearish impulses are identified using Hull Moving Averages (HMA) of the positive and negative sentiments, respectively. When these impulses exceed a calculated threshold based on the standard deviation of the sentiment, it indicates a significant trend change. The script also includes a gradient color table to visually represent the strength of sentiment, and customizable alerts to notify users of key sentiment changes and trend impulses.
Unlock deeper insights into market sentiment and elevate your trading strategy with the Market Sentiment Fear and Greed Indicator! 📈✨
Meanreversion
Rolling Price Activity Heatmap [AlgoAlpha]📈 Rolling Price Activity Heatmap 🔥
Enhance your trading experience with the Rolling Price Activity Heatmap , designed by AlgoAlpha to provide a dynamic view of price activity over a rolling lookback period. This indicator overlays a heatmap on your chart, highlighting areas of significant price activity, allowing traders to spot key price levels at a glance.
🌟 Key Features
📊 Rolling Heatmap: Visualize historical price activity intensity over a user-defined lookback period.
🔄 Customizable Lookback: Adjust the heatmap lookback period to suit your trading style.
🌫️ Transparency Filter: Fine-tune the heatmap’s transparency to filter out less significant areas.
🎨 Color Customization: Choose colors for up, down, and highlight areas to fit your chart’s theme.
🔄 Inverse Heatmap Option: Flip the heatmap to highlight less active areas if needed.
🛠 Add the Indicator: Add the Indicator to favorites. Customize settings like lookback period, transparency filter, and colors to fit your trading style.
📊 Market Analysis: Watch for areas of high price activity indicated by the heatmap to identify potential support and resistance levels.
🔧 How it Works
This script calculates the highest and lowest prices within a specified lookback period and divides the price range into 15 segments. It counts the number of candles that fall within each segment to determine areas of high and low price activity. The script then plots the heatmap on the chart, using varying levels of transparency to indicate the strength of price activity in each segment, providing a clear visual representation of where significant trading occurs.
Stay ahead of the market with this powerful visualization tool and make informed trading decisions! 📈💼
Efficiency Weighted OrderFlow [AlgoAlpha]Introducing the Efficiency Weighted Orderflow Indicator by AlgoAlpha! 📈✨
Elevate your trading game with our cutting-edge Efficiency Weighted Orderflow Indicator, designed to provide clear insights into market trends and potential reversals. This tool is perfect for traders seeking to understand the underlying market dynamics through efficiency-weighted volume calculations.
🌟 Key Features 🌟
✨ Smooth OrderFlow Calculation : Option to smooth order flow data for more consistent signals.
🔧 Customizable Parameters : Adjust the Order Flow Period and HMA Smoothing Length to fit your trading strategy.
🔍 Visual Clarity : Easily distinguish between bullish and bearish trends with customizable colors.
📊 Standard Deviation Normalization : Keeps order flow values normalized for better comparison across different market conditions.
🔔 Trend Reversal Alerts : Stay ahead with built-in alert conditions for significant order flow changes.
🚀 Quick Guide to Using the Efficiency Weighted Orderflow Indicator
🛠 Add the Indicator: Search for "Efficiency Weighted Orderflow " in TradingView's Indicators & Strategies. Customize settings like smoothing and order flow period to fit your trading style.
📊 Market Analysis: Watch for trend reversal alerts to capture trading opportunities by studying the behaviour of the indicator.
🔔 Alerts: Enable notifications for significant order flow changes to stay updated on market trends.
🔍 How It Works
The Efficiency Weighted Orderflow Indicator starts by calculating the efficiency of price movements using the absolute difference between the close and open prices, divided by volume. The order flow is then computed by summing these efficiency-weighted volumes over a specified period, with an option to apply Hull Moving Average (HMA) smoothing for enhanced signal stability. To ensure robust comparison, the order flow is normalized using standard deviation. The indicator plots these values as columns, with distinct colors representing bullish and bearish trends. Customizable parameters for period length and smoothing allow traders to tailor the indicator to their strategies. Additionally, visual cues and alert conditions for trend reversals and significant order flow changes keep traders informed and ready to act. This indicator improves on the Orderflow aspect of our Standardized Orderflow indicator. The Efficiency Weighted Orderflow is less susceptible to noise and is also quicker at detecting trend changes.
AlgoBuilder [Mean-Reversion] | FractalystWhat's the strategy's purpose and functionality?
This strategy is designed for both traders and investors looking to rely and trade based on historical and backtested data using automation.
The main goal is to build profitable mean-reversion strategies that outperform the underlying asset in terms of returns while minimizing drawdown.
For example, as for a benchmark, if the S&P 500 (SPX) has achieved an estimated 10% annual return with a maximum drawdown of -57% over the past 20 years, using this strategy with different entry and exit techniques, users can potentially seek ways to achieve a higher Compound Annual Growth Rate (CAGR) while maintaining a lower maximum drawdown.
Although the strategy can be applied to all markets and timeframes, it is most effective on stocks, indices, future markets, cryptocurrencies, and commodities and JPY currency pairs given their trending behaviors.
In trending market conditions, the strategy employs a combination of moving averages and diverse entry models to identify and capitalize on upward market movements. It integrates market structure-based moving averages and bands mechanisms across different timeframes and provides exit techniques, including percentage-based and risk-reward (RR) based take profit levels.
Additionally, the strategy has also a feature that includes a built-in probability function for traders who want to implement probabilities right into their trading strategies.
Performance summary, weekly, and monthly tables enable quick visualization of performance metrics like net profit, maximum drawdown, profit factor, average trade, average risk-reward ratio (RR), and more.
This aids optimization to meet specific goals and risk tolerance levels effectively.
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How does the strategy perform for both investors and traders?
The strategy has two main modes, tailored for different market participants: Traders and Investors.
Trading:
1. Trading:
- Designed for traders looking to capitalize on bullish trending markets.
- Utilizes a percentage risk per trade to manage risk and optimize returns.
- Suitable for active trading with a focus on mean-reversion and risk per trade approach.
◓: Mode | %: Risk percentage per trade
3. Investing:
- Geared towards investors who aim to capitalize on bullish trending markets without using leverage while mitigating the asset's maximum drawdown.
- Utilizes pre-define percentage of the equity to buy, hold, and manage the asset.
- Focuses on long-term growth and capital appreciation by fully investing in the asset during bullish conditions.
- ◓: Mode | %: Risk not applied (In investing mode, the strategy uses 10% of equity to buy the asset)
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What's is FRMA? How does the triple bands work? What are the underlying calculations?
Middle Band (FRMA):
The middle band is the core of the FRMA system. It represents the Fractalyst Moving Average, calculated by identifying the most recent external swing highs and lows in the market structure.
By determining these external swing pivot points, which act as significant highs and lows within the market range, the FRMA provides a unique moving average that adapts to market structure changes.
Upper Band:
The upper band shows the average price of the most recent external swing highs.
External swing highs are identified as the highest points between pivot points in the market structure.
This band helps traders identify potential overbought conditions when prices approach or exceed this upper band.
Lower Band:
The lower band shows the average price of the most recent external swing lows.
External swing lows are identified as the lowest points between pivot points in the market structure.
The script utilizes this band to identify potential oversold conditions, triggering entry signals as prices approach or drop below the lower band.
Adjustments Based on User Inputs:
Users can adjust how the upper and lower bands are calculated based on their preferences:
Upper/Lower: This method calculates the average bands using the prices of external swing highs and lows identified in the market.
Percentage Deviation from FRMA: Alternatively, users can opt to calculate the bands based on a percentage deviation from the middle FRMA. This approach provides flexibility to adjust the width of the bands relative to market conditions and volatility.
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What's the purpose of using moving averages in this strategy? What are the underlying calculations?
Using moving averages is a widely-used technique to trade with the trend.
The main purpose of using moving averages in this strategy is to filter out bearish price action and to only take trades when the price is trading ABOVE specified moving averages.
The script uses different types of moving averages with user-adjustable timeframes and periods/lengths, allowing traders to try out different variations to maximize strategy performance and minimize drawdowns.
By applying these calculations, the strategy effectively identifies bullish trends and avoids market conditions that are not conducive to profitable trades.
The MA filter allows traders to choose whether they want a specific moving average above or below another one as their entry condition.
This comparison filter can be turned on (>) or off.
For example, you can set the filter so that MA#1 > MA#2, meaning the first moving average must be above the second one before the script looks for entry conditions. This adds an extra layer of trend confirmation, ensuring that trades are only taken in more favorable market conditions.
⍺: MA Period | Σ: MA Timeframe
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What entry modes are used in this strategy? What are the underlying calculations?
The strategy by default uses two different techniques for the entry criteria with user-adjustable left and right bars: Breakout and Fractal.
1. Breakout Entries :
- The strategy looks for pivot high points with a default period of 3.
- It stores the most recent high level in a variable.
- When the price crosses above this most recent level, the strategy checks if all conditions are met and the bar is closed before taking the buy entry.
◧: Pivot high left bars period | ◨: Pivot high right bars period
2. Fractal Entries :
- The strategy looks for pivot low points with a default period of 3.
- When a pivot low is detected, the strategy checks if all conditions are met and the bar is closed before taking the buy entry.
◧: Pivot low left bars period | ◨: Pivot low right bars period
2. Hunt Entries :
- The strategy identifies a candle that wicks through the lower FRMA band.
- It waits for the next candle to close above the low of the wick candle.
- When this condition is met and the bar is closed, the strategy takes the buy entry.
By utilizing these entry modes, the strategy aims to capitalize on bullish price movements while ensuring that the necessary conditions are met to validate the entry points.
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What type of stop-loss identification method are used in this strategy? What are the underlying calculations?
Initial Stop-Loss:
1. ATR Based:
The Average True Range (ATR) is a method used in technical analysis to measure volatility. It is not used to indicate the direction of price but to measure volatility, especially volatility caused by price gaps or limit moves.
Calculation:
- To calculate the ATR, the True Range (TR) first needs to be identified. The TR takes into account the most current period high/low range as well as the previous period close.
The True Range is the largest of the following:
- Current Period High minus Current Period Low
- Absolute Value of Current Period High minus Previous Period Close
- Absolute Value of Current Period Low minus Previous Period Close
- The ATR is then calculated as the moving average of the TR over a specified period. (The default period is 14).
Example - ATR (14) * 2
⍺: ATR period | Σ: ATR Multiplier
2. ADR Based:
The Average Day Range (ADR) is an indicator that measures the volatility of an asset by showing the average movement of the price between the high and the low over the last several days.
Calculation:
- To calculate the ADR for a particular day:
- Calculate the average of the high prices over a specified number of days.
- Calculate the average of the low prices over the same number of days.
- Find the difference between these average values.
- The default period for calculating the ADR is 14 days. A shorter period may introduce more noise, while a longer period may be slower to react to new market movements.
Example - ADR (20) * 2
⍺: ADR period | Σ: ADR Multiplier
3. PL Based:
This method places the stop-loss at the low of the previous candle.
If the current entry is based on the hunt entry strategy, the stop-loss will be placed at the low of the candle that wicks through the lower FRMA band.
Example:
If the previous candle's low is 100, then the stop-loss will be set at 100.
This method ensures the stop-loss is placed just below the most recent significant low, providing a logical and immediate level for risk management.
Application in Strategy (ATR/ADR):
- The strategy calculates the current bar's ADR/ATR with a user-defined period.
- It then multiplies the ADR/ATR by a user-defined multiplier to determine the initial stop-loss level.
By using these methods, the strategy dynamically adjusts the initial stop-loss based on market volatility, helping to protect against adverse price movements while allowing for enough room for trades to develop.
Each market behaves differently across various timeframes, and it is essential to test different parameters and optimizations to find out which trailing stop-loss method gives you the desired results and performance.
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What type of break-even and take profit identification methods are used in this strategy? What are the underlying calculations?
For Break-Even:
Percentage (%) Based:
Moves the initial stop-loss to the entry price when the price reaches a certain percentage above the entry.
Calculation:
Break-even level = Entry Price * (1 + Percentage / 100)
Example:
If the entry price is $100 and the break-even percentage is 5%, the break-even level is $100 * 1.05 = $105.
Risk-to-Reward (RR) Based:
Moves the initial stop-loss to the entry price when the price reaches a certain RR ratio.
Calculation:
Break-even level = Entry Price + (Initial Risk * RR Ratio)
Example:
If the entry price is $100, the initial risk is $10, and the RR ratio is 2, the break-even level is $100 + ($10 * 2) = $120.
FRMA Based:
Moves the stop-loss to break-even when the price hits the FRMA level at which the entry was taken.
Calculation:
Break-even level = FRMA level at the entry
Example:
If the FRMA level at entry is $102, the break-even level is set to $102 when the price reaches $102.
For TP1 (Take Profit 1):
- You can choose to set a take profit level at which your position gets fully closed or 50% if the TP2 boolean is enabled.
- Similar to break-even, you can select either a percentage (%) or risk-to-reward (RR) based take profit level, allowing you to set your TP1 level as a percentage amount above the entry price or based on RR.
For TP2 (Take Profit 2):
- You can choose to set a take profit level at which your position gets fully closed.
- As with break-even and TP1, you can select either a percentage (%) or risk-to-reward (RR) based take profit level, allowing you to set your TP2 level as a percentage amount above the entry price or based on RR.
When Both Percentage (%) Based and RR Based Take Profit Levels Are Off:
The script will adjust the take profit level to the higher FRMA band set within user inputs.
Calculation:
Take profit level = Higher FRMA band length/timeframe specified by the user.
This ensures that when neither percentage-based nor risk-to-reward-based take profit methods are enabled, the strategy defaults to using the higher FRMA band as the take profit level, providing a consistent and structured approach to profit-taking.
For TP1 and TP2, it's specifying the price levels at which the position is partially or fully closed based on the chosen method (percentage or RR) above the entry price.
These calculations are crucial for managing risk and optimizing profitability in the strategy.
⍺: BE/TP type (%/RR) | Σ: how many RR/% above the current price
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What's the ADR filter? What does it do? What are the underlying calculations?
The Average Day Range (ADR) measures the volatility of an asset by showing the average movement of the price between the high and the low over the last several days.
The period of the ADR filter used in this strategy is tied to the same period you've used for your initial stop-loss.
Users can define the minimum ADR they want to be met before the script looks for entry conditions.
ADR Bias Filter:
- Compares the current bar ADR with the ADR (Defined by user):
- If the current ADR is higher, it indicates that volatility has increased compared to ADR (DbU).(⬆)
- If the current ADR is lower, it indicates that volatility has decreased compared to ADR (DbU).(⬇)
Calculations:
1. Calculate ADR:
- Average the high prices over the specified period.
- Average the low prices over the same period.
- Find the difference between these average values in %.
2. Current ADR vs. ADR (DbU):
- Calculate the ADR for the current bar.
- Calculate the ADR (DbU).
- Compare the two values to determine if volatility has increased or decreased.
By using the ADR filter, the strategy ensures that trades are only taken in favorable market conditions where volatility meets the user's defined threshold, thus optimizing entry conditions and potentially improving the overall performance of the strategy.
>: Minimum required ADR for entry | %: Current ADR comparison to ADR of 14 days ago.
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What's the probability filter? What are the underlying calculations?
The probability filter is designed to enhance trade entries by using buyside liquidity and probability analysis to filter out unfavorable conditions.
This filter helps in identifying optimal entry points where the likelihood of a profitable trade is higher.
Calculations:
1. Understanding Swing highs and Swing Lows
Swing High: A Swing High is formed when there is a high with 2 lower highs to the left and right.
Swing Low: A Swing Low is formed when there is a low with 2 higher lows to the left and right.
2. Understanding the purpose and the underlying calculations behind Buyside, Sellside and Equilibrium levels.
3. Understanding probability calculations
1. Upon the formation of a new range, the script waits for the price to reach and tap into equilibrium or the 50% level. Status: "⏸" - Inactive
2. Once equilibrium is tapped into, the equilibrium status becomes activated and it waits for either liquidity side to be hit. Status: "▶" - Active
3. If the buyside liquidity is hit, the script adds to the count of successful buyside liquidity occurrences. Similarly, if the sellside is tapped, it records successful sellside liquidity occurrences.
5. Finally, the number of successful occurrences for each side is divided by the overall count individually to calculate the range probabilities.
Note: The calculations are performed independently for each directional range. A range is considered bearish if the previous breakout was through a sellside liquidity. Conversely, a range is considered bullish if the most recent breakout was through a buyside liquidity.
Example - BSL > 55%
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What's the range length Filter? What are the underlying calculations?
The range length filter identifies the price distance between buyside and sellside liquidity levels in percentage terms. When enabled, the script only looks for entries when the minimum range length is met. This helps ensure that trades are taken in markets with sufficient price movement.
Calculations:
Range Length (%) = ( ( Buyside Level − Sellside Level ) / Current Price ) ×100
Range Bias Identification:
Bullish Bias: The current range price has broken above the previous external swing high.
Bearish Bias: The current range price has broken below the previous external swing low.
Example - Range length filter is enabled | Range must be above 1%
>: Minimum required range length for entry | %: Current range length percentage in a (Bullish/Bearish) range
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What's the day filter Filter, what does it do?
The day filter allows users to customize the session time and choose the specific days they want to include in the strategy session. This helps traders tailor their strategies to particular trading sessions or days of the week when they believe the market conditions are more favorable for their trading style.
Customize Session Time:
Users can define the start and end times for the trading session.
This allows the strategy to only consider trades within the specified time window, focusing on periods of higher market activity or preferred trading hours.
Select Days:
Users can select which days of the week to include in the strategy.
This feature is useful for excluding days with historically lower volatility or unfavorable trading conditions (e.g., Mondays or Fridays).
Benefits:
Focus on Optimal Trading Periods:
By customizing session times and days, traders can focus on periods when the market is more likely to present profitable opportunities.
Avoid Unfavorable Conditions:
Excluding specific days or times can help avoid trading during periods of low liquidity or high unpredictability, such as major news events or holidays.
Increased Flexibility: The filter provides increased flexibility, allowing traders to adapt the strategy to their specific needs and preferences.
Example - Day filter | Session Filter
θ: Session time | Exchange time-zone
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What tables are available in this script?
Table Type:
- Summary: Provides a general overview, displaying key performance parameters such as Net Profit, Profit Factor, Max Drawdown, Average Trade, Closed Trades and more.
Avg Trade: The sum of money gained or lost by the average trade generated by a strategy. Calculated by dividing the Net Profit by the overall number of closed trades. An important value since it must be large enough to cover the commission and slippage costs of trading the strategy and still bring a profit.
MaxDD: Displays the largest drawdown of losses, i.e., the maximum possible loss that the strategy could have incurred among all of the trades it has made. This value is calculated separately for every bar that the strategy spends with an open position.
Profit Factor: The amount of money a trading strategy made for every unit of money it lost (in the selected currency). This value is calculated by dividing gross profits by gross losses.
Avg RR: This is calculated by dividing the average winning trade by the average losing trade. This field is not a very meaningful value by itself because it does not take into account the ratio of the number of winning vs losing trades, and strategies can have different approaches to profitability. A strategy may trade at every possibility in order to capture many small profits, yet have an average losing trade greater than the average winning trade. The higher this value is, the better, but it should be considered together with the percentage of winning trades and the net profit.
Winrate: The percentage of winning trades generated by a strategy. Calculated by dividing the number of winning trades by the total number of closed trades generated by a strategy. Percent profitable is not a very reliable measure by itself. A strategy could have many small winning trades, making the percent profitable high with a small average winning trade, or a few big winning trades accounting for a low percent profitable and a big average winning trade. Most mean-reversion successful strategies have a percent profitability of 40-80% but are profitable due to risk management control.
BE Trades: Number of break-even trades, excluding commission/slippage.
Losing Trades: The total number of losing trades generated by the strategy.
Winning Trades: The total number of winning trades generated by the strategy.
Total Trades: Total number of taken traders visible your charts.
Net Profit: The overall profit or loss (in the selected currency) achieved by the trading strategy in the test period. The value is the sum of all values from the Profit column (on the List of Trades tab), taking into account the sign.
- Monthly: Displays performance data on a month-by-month basis, allowing users to analyze performance trends over each month.
- Weekly: Displays performance data on a week-by-week basis, helping users to understand weekly performance variations.
- OFF: Hides the performance table.
Profit Color:
- Allows users to set the color for representing profit in the performance table, helping to quickly distinguish profitable periods.
Loss Color:
- Allows users to set the color for representing loss in the performance table, helping to quickly identify loss-making periods.
These customizable tables provide traders with flexible and detailed performance analysis, aiding in better strategy evaluation and optimization.
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User-input styles and customizations:
To facilitate studying historical data, all conditions and rules can be applied to your charts. By plotting background colors on your charts, you'll be able to identify what worked and what didn't in certain market conditions.
Please note that all background colors in the style are disabled by default to enhance visualization.
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How to Use This Algobuilder to Create a Profitable Edge and System:
Choose Your Strategy mode:
- Decide whether you are creating an investing strategy or a trading strategy.
Select a Market:
- Choose a one-sided market such as stocks, indices, or cryptocurrencies.
Historical Data:
- Ensure the historical data covers at least 10 years of price action for robust backtesting.
Timeframe Selection:
- Choose the timeframe you are comfortable trading with. It is strongly recommended to use a timeframe above 15 minutes to minimize the impact of commissions/slippage on your profits.
Set Commission and Slippage:
- Properly set the commission and slippage in the strategy properties according to your broker or prop firm specifications.
Parameter Optimization:
- Use trial and error to test different parameters until you find the performance results you are looking for in the summary table or, preferably, through deep backtesting using the strategy tester.
Trade Count:
- Ensure the number of trades is 100 or more; the higher, the better for statistical significance.
Positive Average Trade:
- Make sure the average trade value is above zero.
(An important value since it must be large enough to cover the commission and slippage costs of trading the strategy and still bring a profit.)
Performance Metrics:
- Look for a high profit factor, and net profit with minimum drawdown.
- Ideally, aim for a drawdown under 20-30%, depending on your risk tolerance.
Refinement and Optimization:
- Try out different markets and timeframes.
- Continue working on refining your edge using the available filters and components to further optimize your strategy.
Automation:
- Once you’re confident in your strategy, you can use the automation section to connect the algorithm to your broker or prop firm.
- Trade a fully automated and backtested trading strategy, allowing for hands-free execution and management.
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What makes this strategy original?
1. Incorporating direct integration of probabilities into the strategy.
2. Utilizing built-in market structure-based moving averages across various timeframes.
4. Offering both investing and trading strategies, facilitating optimization from different perspectives.
5. Automation for efficient execution.
6. Providing a summary table for instant access to key parameters of the strategy.
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How to use automation?
For Traders:
1. Ensure the strategy parameters are properly set based on your optimized parameters.
2. Enter your PineConnector License ID in the designated field.
3. Specify the desired risk level.
4. Provide the Metatrader symbol.
5. Check for chart updates to ensure the automation table appears on the top right corner, displaying your License ID, risk, and symbol.
6. Set up an alert with the strategy selected as Condition and the Message as {{strategy.order.alert_message}}.
7. Activate the Webhook URL in the Notifications section, setting it as the official PineConnector webhook address.
8. Double-check all settings on PineConnector to ensure the connection is successful.
9. Create the alert for entry/exit automation.
For Investors:
1. Ensure the strategy parameters are properly set based on your optimized parameters.
2. Choose "Investing" in the user-input settings.
3. Create an alert with a specified name.
4. Customize the notifications tab to receive alerts via email.
5. Buying/selling alerts will be triggered instantly upon entry or exit order execution.
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Terms and Conditions | Disclaimer
Our charting tools are provided for informational and educational purposes only and should not be construed as financial, investment, or trading advice. They are not intended to forecast market movements or offer specific recommendations. Users should understand that past performance does not guarantee future results and should not base financial decisions solely on historical data.
Built-in components, features, and functionalities of our charting tools are the intellectual property of @Fractalyst Unauthorized use, reproduction, or distribution of these proprietary elements is prohibited.
By continuing to use our charting tools, the user acknowledges and accepts the Terms and Conditions outlined in this legal disclaimer and agrees to respect our intellectual property rights and comply with all applicable laws and regulations.
Visible Range Support and Resistance [AlgoAlpha]🌟 Introducing the Visible Range Support and Resistance 🌟
Discover key support and resistance levels with the innovative "Visible Range Support and Resistance" indicator by AlgoAlpha! 🚀📈 This advanced tool dynamically identifies significant price zones based on the visible range of your chart, providing traders with crucial insights for making informed decisions.
Key Features:
Dynamic support and resistance levels based on visible chart range 📏
User-defined resolution for tailored analysis 🎯
Clear visual representation of significant key zones 🖼️
Easy integration with any trading strategy 💼
How to Use:
🛠 Add the Indicator : Add the indicator to favourites. Adjust settings like resolution and horizontal extension to suit your trading style.
📊 Market Analysis : Identify key support and resistance zones based on the highlighted areas. These zones indicate significant price levels where the market may react.
How it Works:
The indicator segments the price range into user-defined resolutions, analyzing the highest and lowest points to establish boundaries. It calculates the frequency of price action within these segments, highlighting key levels where price movements are least concentrated (areas where price tends to pivot). Customizable settings like resolution and horizontal extension allow for tailored analysis, while the intuitive visual representation makes it easy to spot potential support and resistance zones directly on your chart.
By leveraging this indicator, you can gain deeper insights into market dynamics and improve your trading strategy with data driven support and resistance analysis. Happy trading! 💹✨
Institutional Liquidity and Price Action Concepts [AlgoAlpha]🚀 Introducing the Institutional Liquidity and Price Action Concepts™ (ILPAC) , a comprehensive toolkit developed by AlgoAlpha as part of our Premium Collection. This All-in-One indicator offers a robust approach to understanding price action and liquidity, empowering traders with hyper customizable features to tailor their analysis to their specific trading strategies.
Designed with efficiency and compactness in mind, the script shows Price action and liquidity through four methods: Market Structure , Liquidity Heatmap , Trend Lines , and FOMO Bubbles . Additionally, the script also includes a fully customizable interface, to match each individual's trading style. By utilizing a blend of advanced algorithms and customizable parameters, Institutional Liquidity and Price Action Concepts™ (ILPAC) provides traders with a vast array of trading strategies ranging from high frequency scalping to timing better entries on long-term swing and investing positions.
The ILPAC ™ can be used with or without other AlgoAlpha Premium Collection indicators as this indicator has been designed to be able to act as a standalone toolkit.
Let's delve into the key features and functionalities of this versatile indicator:
🎯 Key Features (summary):
Market Structure Analysis :
Customizable time-horizon
BOS confirmation methods
Adjustable CHoCH/BOS line styles
Swing point highlighting
Color customization
Liquidity Heatmap:
Configurable look-back period
Adjustable resolution
Customizable scale colors
Trend Lines :
Look-back period settings
Noise filter factor
Trend line signals with color options
FOMO Bubbles :
Configurable look-back period
Adjustable noise filter factor
Customizable bubble colors
🎯 Key Features (in-depth):
The Market Structure component within ILPAC ™ shows the underlying trend of the market using swing high and lows and is purely price action based. Higher Highs(HH), Higher Lows(HL) labels generally indicate an uptrend and Lower Highs(LH) and Lower Lows(LL) indicate a downtrend. The trend of the market is also determined by Change of Characters (CHoCH) and Break of Structure patterns (BOS). The Market Structure component marks out all these automatically and colours the bars on your chart for easy visualisation of trend.
The Liquidity Heatmap component within ILPAC ™ visualizes areas of high and low liquidity in the market. It identifies zones where liquidity is concentrated not only at specific price levels but also over time, giving the user a 3 Dimensional view of liquidity. The heatmap colours represent different levels of liquidity, making it easy to see where large volumes of orders may exist. This component helps traders understand the liquidity landscape and make informed decisions based on potential support and resistance levels.
The Trend Lines component within ILPAC ™ automatically draws trend lines based on historical price data. It identifies significant highs and lows, connecting them to form trend lines that highlight the overall market direction as well as give breakout signals as shown in the image below. The component also includes a noise filter to reduce false signals and ensure only valid trend breakouts are displayed. Customizable colour settings allow traders to personalize the visual representation of trend lines on their charts.
The FOMO Bubbles component within ILPAC ™ identifies periods of market activity driven by Fear of Missing Out (FOMO). By analysing price action and volume, it highlights bubbles where traders are likely entering positions impulsively. These bubbles are displayed on the chart with customizable colours, providing a visual cue for potential overbought or oversold conditions. This component helps traders recognize and potentially capitalize on market exuberance or panic.
🎯Usage Examples:
At its core, the components within ILPAC ™ were designed to operate with each other as a form of confluence and robust analysis. Typically, Price action components such as the Market Structure and Trend Lines can be used for entries while the Liquidity components like FOMO Bubbles and the Heatmap can be used to find exit points. Here are some examples of how they can be used.
Trend Trading
Using the Market Structure component, enter a trade during a CHoCH and set TP at key areas of liquidity using the heatmap. Users can also choose to enter into a BOS which is an indication of a trend continuation.
Reversal Trading
Using the Liquidity Heatmap to find areas of liquidity for possible reversals, wait for a rejection from a liquidity zone and use the Trend Line Breakout signals as confluence for an entry. Exits can be set at liquidity zones or using FOMO Bubbles as take profit signals.
(These are just examples for reference, the ILPAC ™ offers significantly more possibilities for customisation and fine tuning of your trading strategy.)
🎯Conclusion:
The Institutional Liquidity and Price Action Concepts™ (ILPAC) indicator by AlgoAlpha is a powerful tool for traders, offering in-depth market insights through its Market Structure, Liquidity Heatmap, Trend Lines, and FOMO Bubbles components. By integrating Price Action based analysis with Liquidity analysis, ILPAC ™ boasts a superior design for the confluence between its components, using Price Action components for entry opportunities and Liquidity based components for exit opportunities. With its highly customizable settings, this indicator caters to all trading styles, from scalping to long-term investing. By providing clear visualizations and automatic trend and liquidity detection, ILPAC ™ empowers traders to make informed decisions, enhancing their trading strategies and improving overall market understanding.
RSI DeviationAn oscillator which de-trends the Relative Strength Index. Rather, it takes a moving average of RSI and plots it's standard deviation from the MA, similar to a Bollinger %B oscillator. This seams to highlight short term peaks and troughs, Indicating oversold and overbought conditions respectively. It is intended to be used with a Dollar Cost Averaging strategy, but may also be useful for Swing Trading, or Scalping on lower timeframes.
When the line on the oscillator line crosses back into the channel, it signals a trade opportunity.
~ Crossing into the band from the bottom, indicates the end of an oversold condition, signaling a potential reversal. This would be a BUY signal.
~ Crossing into the band from the top, indicates the end of an overbought condition, signaling a potential reversal. This would be a SELL signal.
For ease of use, I've made the oscillator highlight the main chart when Overbought/Oversold conditions are occurring, and place fractals upon reversion to the Band. These repaint as they are calculated at close. The earliest trade would occur upon open of the following day.
I have set the default St. Deviation to be 2, but in my testing I have found 1.5 to be quite reliable. By decreasing the St. Deviation you will increase trade frequency, to a point, at the expense of efficiency.
Cheers
DJSnoWMan06
Log Regression Channel [UAlgo]The "Log Regression Channel " channel is useful for analyzing price trends and volatility in a financial instrument over a specified period. By using logarithmic scaling, this indicator can more effectively handle the wide range of price movements seen in many financial markets, making it particularly valuable for assets with exponential growth characteristics.
The indicator plots the central regression line along with upper and lower deviation bands, providing a visual representation of potential support and resistance levels.
🔶 Key Features
Logarithmic Regression Line: The central line represents the logarithmic regression, which fits the price data over the specified length using a logarithmic scale. This helps in identifying the overall trend direction.
Deviation Bands: The upper and lower bands are plotted at a specified multiple of the standard deviation from the regression line, highlighting areas of potential overbought and oversold conditions.
Customizable Parameters: Users can adjust the length of the regression, the deviation multiplier, the color of the labels, and the size of the text labels to suit their preferences.
R-Squared Display: The R-squared value, which measures the goodness of fit of the regression model, is displayed on the chart. This helps traders assess the reliability of the regression line.
🔶 Calculations
The indicator performs several key calculations to plot the logarithmic regression channel:
Logarithmic Transformation: The prices and time indices are transformed using the natural logarithm to handle exponential growth in price data.
Regression Coefficients: The slope and intercept of the regression line are calculated using the least squares method on the transformed data.
Predicted Values: The regression equation is used to calculate predicted values for each data point.
Standard Deviation: The standard deviation of the residuals (differences between actual and predicted values) is computed to determine the width of the deviation bands.
Deviation Bands: Upper and lower bands are plotted at a specified multiple of the standard deviation above and below the regression line.
R-Squared Value: The R-squared value is calculated to measure how well the regression line fits the data. This value is displayed on the chart to inform the user of the model's reliability.
🔶 Disclaimer
The "Log Regression Channel " indicator is provided for educational and informational purposes only.
It is not intended as investment advice or a recommendation to buy or sell any financial instrument. Trading financial instruments involves substantial risk and may not be suitable for all investors.
Past performance is not indicative of future results. Users should conduct their own research.
Activity and Volume Orderflow Profile [AlgoAlpha]🔍 Activity and Volume Orderflow Profile 📊
🚀 Unlock the power of market order flow analysis with the Activity and Volume Orderflow Profile indicator by AlgoAlpha . This versatile tool helps you visualize and understand the dynamics of buying and selling pressure within a specified lookback period. Perfect for traders who want to dig deeper into volume-based market insights!
Key Features:
📊 Profile Type Options : Choose between "Comparison" and "Net Order Flow" to analyze market activity based on your preferred method.
🔎 Adjustable Lookback Period : Customize the lookback period to fit your trading strategy.
🎨 Flexible Appearance Settings : Toggle the display of the profile, lookback period visualization, and heatmap to suit your preferences.
🖍 Color Customization : Set your preferred colors for up and down volumes.
🕹 High Activity Highlight : Use the minimum transparency setting to highlight areas of significant activity.
Quick Guide to Using the Activity and Volume Orderflow Profile
🛠 Add the Indicator: Add the indicator to your favorites. Customize settings like profile type, lookback period, and resolution to fit your trading style.
📊 Market Analysis: Use the profile to identify areas of high buying or selling pressure. In "Comparison" mode, look for significant volume differences; in "Net Order Flow" mode, focus on net volume changes. Additionally, you can use the activity heatmap to find key levels that can act as support and resistance as price is likely to react to the zones as indicated by the heatmap.
How it Works:
The indicator operates by first gathering data on high and low prices, as well as buy and sell volumes, over a user-defined lookback period. It then calculates the maximum and minimum prices during this period and divides this range into bins based on the chosen resolution. For each bin, it computes the total volume of buy and sell orders. In "Comparison" mode, it displays side-by-side boxes representing buy and sell volumes, while in "Net Order Flow" mode, it shows the net volume difference. The indicator visually presents these profiles on the chart with customizable colors, transparency levels, and the option to display a heatmap for enhanced volume activity insights.
Maximize your trading with the Activity and Volume Orderflow Profile from AlgoAlpha! 🚀✨
Dickey-Fuller Test for Mean Reversion and Stationarity **IF YOU NEED EXTRA SPECIAL HELP UNDERSTANDING THIS INDICATOR, GO TO THE BOTTOM OF THE DESCRIPTION FOR AN EVEN SIMPLER DESCRIPTION**
Dickey Fuller Test:
The Dickey-Fuller test is a statistical test used to determine whether a time series is stationary or has a unit root (a characteristic of a time series that makes it non-stationary), indicating that it is non-stationary. Stationarity means that the statistical properties of a time series, such as mean and variance, are constant over time. The test checks to see if the time series is mean-reverting or not. Many traders falsely assume that raw stock prices are mean-reverting when they are not, as evidenced by many different types of statistical models that show how stock prices are almost always positively autocorrelated or statistical tests like this one, which show that stock prices are not stationary.
Note: This indicator uses past results, and the results will always be changing as new data comes in. Just because it's stationary during a rare occurrence doesn't mean it will always be stationary. Especially in price, where this would be a rare occurrence on this test. (The Test Statistic is below the critical value.)
The indicator also shows the option to either choose Raw Price, Simple Returns, or Log Returns for the test.
Raw Prices:
Stock prices are usually non-stationary because they follow some type of random walk, exhibiting positive autocorrelation and trends in the long term.
The Dickey-Fuller test on raw prices will indicate non-stationary most of the time since prices are expected to have a unit root. (If the test statistic is higher than the critical value, it suggests the presence of a unit root, confirming non-stationarity.)
Simple Returns and Log Returns:
Simple and log returns are more stationary than prices, if not completely stationary, because they measure relative changes rather than absolute levels.
This test on simple and log returns may indicate stationary behavior, especially over longer periods. (The test statistic being below the critical value suggests the absence of a unit root, indicating stationarity.)
Null Hypothesis (H0): The time series has a unit root (it is non-stationary).
Alternative Hypothesis (H1): The time series does not have a unit root (it is stationary)
Interpretation: If the test statistic is less than the critical value, we reject the null hypothesis and conclude that the time series is stationary.
Types of Dickey-Fuller Tests:
1. (What this indicator uses) Standard Dickey-Fuller Test:
Tests the null hypothesis that a unit root is present in a simple autoregressive model.
This test is used for simple cases where we just want to check if the series has a consistent statistical property over time without considering any trends or additional complexities.
It examines the relationship between the current value of the series and its previous value to see if the series tends to drift over time or revert to the mean.
2. Augmented Dickey-Fuller (ADF) Test:
Tests for a unit root while accounting for more complex structures like trends and higher-order correlations in the data.
This test is more robust and is used when the time series has trends or other patterns that need to be considered.
It extends the regular test by including additional terms to account for the complexities, and this test may be more reliable than the regular Dickey-Fuller Test.
For things like stock prices, the ADF would be more appropriate because stock prices are almost always trending and positively autocorrelated, while the Dickey-Fuller Test is more appropriate for more simple time series.
Critical Values
This indicator uses the following critical values that are essential for interpreting the Dickey-Fuller test results. The critical values depend on the chosen significance levels:
1% Significance Level: Critical value of -3.43.
5% Significance Level: Critical value of -2.86.
10% Significance Level: Critical value of -2.57.
These critical values are thresholds that help determine whether to reject the null hypothesis of a unit root (non-stationarity). If the test statistic is less than (or more negative than) the critical value, it indicates that the time series is stationary. Conversely, if the test statistic is greater than the critical value, the series is considered non-stationary.
This indicator uses a dotted blue line by default to show the critical value. If the test-static, which is the gray column, goes below the critical value, then the test-static will become yellow, and the test will indicate that the time series is stationary or mean reverting for the current period of time.
What does this mean?
This is the weekly chart of BTCUSD with the Dickey-Fuller Test, with a length of 100 and a critical value of 1%.
So basically, in the long term, mean-reversion strategies that involve raw prices are not a good idea. You don't really need a statistical test either for this; just from seeing the chart itself, you can see that prices in the long term are trending and no mean reversion is present.
For the people who can't understand that the gray column being above the blue dotted line means price doesn't mean revert, here is a more simple description (you know you are):
Average (I have to include the meaning because they may not know what average is): The middle number is when you add up all the numbers and then divide by how many numbers there are. EX: If you have the numbers 2, 4, and 6, you add them up to get 12, and then divide by 3 (because there are 3 numbers), so the average is 4. It tells you what a typical number is in a group of numbers.
This indicator checks if a time series (like stock prices) tends to return to its average value or time.
Raw prices, which is just the regular price chart, are usually not mean-reverting (It's "always" positively autocorrelating but this group of people doesn't like that word). Price follows trends.
Simple returns and log returns are more likely to have periods of mean reversion.
How to use it:
Gray Column (the gray bars) Above the Blue Dotted Line: The price does not mean revert (non-stationary).
Gray Column Below Blue Line: The time series mean reverts (stationary)
So, if the test statistic (gray column) is below the critical value, which is the blue dotted line, then the series is stationary and mean reverting, but if it is above the blue dotted line, then the time series is not stationary or mean reverting, and strategies involving mean reversion will most likely result in a loss given enough occurrences.
Swing Failure Zones and Signals [AlgoAlpha]Elevate your trading strategy with the Swing Failure Zones and Signals indicator by AlgoAlpha! This powerful tool helps you identify potential swing failure zones, offering clear bullish and bearish signals to guide your trading decisions. 📈💡
🎨 Bullish/Bearish Color Customization : Easily set the colors for bullish and bearish signals to match your chart preferences.
🧹 Mitigated Zone Removal : Option to remove mitigated zones from the chart for a cleaner view.
🔍 Range High/Low Lookback : Adjustable lookback period for determining significant highs and lows.
🖌 Dynamic Zone Creation : Automatically draws zones based on swing failure criteria.
🔔 Alert Conditions : Set alerts for both bullish and bearish swing failure conditions to stay informed without constant monitoring.
Quick Guide to Using the Swing Failure Zones and Signals Indicator
🛠 Add the Indicator : Search for "Swing Failure Zones and Signals " in TradingView's Indicators & Strategies. Customize settings like lookback period, colors, and zone removal options to fit your trading style.
📊 Market Analysis : Watch for the appearance of the zones and the directional arrows for potential reversal signals. Use these signals to identify key market entries and exits.
🔔 Alerts : Enable alerts for bullish and bearish swing failure conditions to capture trading opportunities without constant chart monitoring.
How it works
The indicator calculates the direction and length of each candle to identify swing failure points by comparing current high and low prices with those from the lookback period. A bullish swing failure is detected when the current low is lower than the previous low and the close is higher than the previous high, while a bearish swing failure occurs when the current high is higher than the previous high and the close is lower than the previous low. Upon detection, the script creates zones on the chart to indicate these failure points and manages them by removing invalidated zones based on the user's settings. Visual signals are plotted on the chart as arrows, and alerts are set for these conditions to help traders capture potential entry opportunities efficiently.
Enhance your trading edge with this robust tool designed to spotlight critical swing failure points in the market! 💪📈
Trend Strength Signals [AlgoAlpha]🌟Introducing the Trend and Strength Signals indicator by AlgoAlpha ! This tool is designed to help you identify trends and gauge market strength with precision and ease. 📈🚀
🛠 Customizable Parameters : Adjust the period, standard deviation multiplier, gauge size, and colors to fit your trading style.
📊 Trend Detection : Visualize trends with clear color-coded signals for uptrends and downtrends.
📈 Strength Gauge : Assess market strength with a dynamic gauge that adapts to the current price action.
🔔 Alerts : Set alerts for bullish and bearish trend crossovers and take profit points to stay ahead of the market.
🎨 Visual Enhancements : Enjoy a clutter-free chart with the integration of plot shapes, color fills, and gradient gauges.
🚀 Quick Guide to Using the Trend and Strength Signals Indicator
Maximize your trading with the Trend and Strength Signals indicator by following these streamlined steps! 🎯✨
🛠 Add the Indicator : Add the indicator to your favorites. Customize settings like period, standard deviation multiplier, and colors to fit your trading style.
📊 Market Analysis : Observe the color-coded candles and gauge to understand market trend direction and strength. Use the alerts for key trading signals.
🔔 Alerts : Enable notifications for trend crossovers and take profit points to catch trading opportunities without constantly monitoring the chart.
⚙️ How it works
This indicator calculates the moving average and standard deviation of the closing price over a customizable period to identify the upper and lower bounds. When the price crosses these bounds, it signals an uptrend or downtrend. The gauge measures market strength by comparing the price to the moving average and scaling it over a customizable range, while the underlying logic uses concepts from the Bollinger Bands, this indicator gives a unique perspective on price behavior through added features and signals derived from it.
Unleash the power of trend and strength analysis with this comprehensive indicator! Happy trading! 🚀📈✨
Cipher Mean ReversionThe Cipher Mean Reversion Indicator is an advanced trading tool that dynamically adjusts to market volatility to provide optimal entry and exit signals. This indicator is designed to identify significant deviations from a calculated mean, signaling potential reversal points where prices might revert to their average.
Core Functionality:
Cipher Mean Reversion uses an Exponential Moving Average (EMA) as the foundation for its mean price calculation. What sets Cipher apart is its dynamic adjustment mechanism that adapts the sensitivity of the EMA based on a volatility index. This index assesses both the rate and magnitude of price changes over a user-specified period, utilizing standard deviation and average true range calculations to gauge market volatility.
Unique Features:
Dynamic Sensitivity Adjustment: The sensitivity of our mean reversion detection changes in real-time, driven by our proprietary volatility index. This index is calculated using a combination of standard deviation and average true range, providing a robust measure of market volatility that informs the adjustment of our signal thresholds.
Adaptive Signal Thresholds: Instead of static buy and sell thresholds, Cipher uses thresholds that adapt to ongoing market conditions. These thresholds expand during periods of high volatility to reduce the risk of false signals and contract during quieter market conditions to capture smaller price reversals.
Signal Generation:
Buy Signals: Generated when the price falls significantly below the dynamically adjusted lower threshold, indicating an oversold condition ripe for reversal.
Sell Signals: Occur when the price exceeds the dynamically adjusted upper threshold, suggesting an overbought condition likely to revert.
Usage Tips:
Parameter Customization: Users can adjust the lookback period for the volatility assessment and the length of the EMA to better fit different assets and trading styles.
Complementary Analysis: For enhanced trading decisions, combine the Cipher Mean Reversion with other analytical tools such as volume indicators or momentum oscillators.
Risk Management: Employ risk management strategies, including predefined stop-loss and take-profit levels, tailored to the volatility insights provided by the indicator.
Originality and Usefulness:
The Cipher Mean Reversion Indicator offers a novel approach to mean reversion analysis by integrating real-time volatility adaptations into the signal generation process. This methodology ensures that the indicator remains highly responsive to changing market dynamics, providing traders with signals that are both timely and relevant.
Intended Use:
Cipher is versatile and can be used across various asset classes, including stocks, forex, and commodities. It is ideal for traders who require an indicator that can adapt to different market environments, from fast-moving markets to more stable conditions.
Volume Weighted Relative Strength Index (VWRSI) [AlgoAlpha]Volume Weighted Relative Strength Index 📈✨
The Volume Weighted Relative Strength Index (VWRSI) by AlgoAlpha enhances traditional RSI by incorporating volume weighting, providing a more nuanced view of market strength. It uses custom range detection to measure consolidation strength, applying dynamic scoring to highlight trend phases. The indicator includes customizable moving averages (SMA, EMA, WMA, VWMA) and color-coded visual cues for uptrends and downtrends. Additionally, it marks significant bullish and bearish trend points with symbols, making it easier to identify potential trading opportunities. This powerful tool helps traders make informed decisions by combining volume, price action, and trend analysis.
✨ Key Features :
📊 Volume-Weighted RSI : Combines RSI with volume for better accuracy.
🔄 Range Detection : Identifies consolidation phases.
🎨 Customizable MAs : Choose from various moving averages.
🔔 Alert Capabilities : Set notifications for trend points.
🚀 How to Use :
🛠 Add Indicator : Add the indicator to favorites, and customize the settings to suite your trading style.
📊 Analyze Market : Watch RSI and range score for trends.
🔔 Set Alerts : Get notified of bullish/bearish points.
✨ How It Works :
The Volume Weighted Relative Strength Index (VWRSI) combines traditional RSI with volume weighting to offer a more comprehensive view of market momentum. It calculates the RSI using the closing price, then weights it by volume to enhance the accuracy of the trend analysis. The indicator also includes a custom range detection feature that evaluates consolidation strength by dynamically scoring the RSI over a specified period. This scoring helps identify phases of strong trends and consolidations. Visual elements like color-coded trend fills and symbols for bullish and bearish points make it easier to spot key market movements and potential trading opportunities.
Stay ahead with VWRSI by AlgoAlpha! 📈💡
Donchian Trend Ranges [AlgoAlpha]🚀🔗 Donchian Trend Ranges 🔗🚀
Elevate your trading game with the Donchian Trend Ranges indicator from AlgoAlpha! 🌟📈 This advanced tool helps you visualize market trends and potential reversal points using Donchian channels, volatility measures, and average true range (ATR).
Key Features
⚙️ Customizable Parameters: Adjust the lookback period and range multiplier to fit your trading style.
🎨 Color-Coded Trends: Easily distinguish between uptrends and downtrends with customizable colors.
📊 Dynamic Channels: Visualize multiple dynamic channels based on Donchian ranges and volatility.
☁️ Trend Clouds: See market strength and weakness with upper and lower trend clouds.
🔔 Signal Alerts: Get notified of potential trend shifts and take profit points.
How to Use
🛠 Add the Indicator: Add the indicator to favorites. Customize settings such as the lookback period and range multiplier to match your trading needs.
🔍 Analyze Trends: The indicator calculates the highest and lowest prices over a specified period to create dynamic channels. It then uses standard deviation and ATR to adjust these channels for market volatility, plotting upper and lower ranges. Green bars indicate an up trend and red bars for a down trend.
🔔 Set Alerts: Enable notifications for bullish and bearish trend shifts, as well as weak and strong take profit points, ensuring you never miss an opportunity.
How it Works
The Donchian Trend Ranges indicator calculates the highest and lowest prices over a specified period to create a basis line. It creates a range around the basis based on standard deviations and the clouds' width is determined by a 14 period ATR. The basis line and bar colors changes based on whether the closing price is above or below it, indicating trends. Clouds around these lines represent market reversal zones that can be used as entry levels when used in confluence with momentum indicators, visual signals ("X" and "◆") marking strong and weak take profit points are also printed when the prices revert from the clouds towards the basis. Integrated alerts notify you of significant events like trend shifts and take profit signals, keeping you informed without constant monitoring.
Unleash the power of the Donchian Trend Ranges in your trading strategy! 🌐📈✨
TanHef Ranks ScreenerTanHef Ranks Screener: A Numeric Compass to Market Tops and Bottoms
█ Simple Explanation:
The TanHef Ranks Screener illustrates the ‘TanHef Ranks’ indicator, designed to signal 'buy low and sell high' opportunities through numerical rankings. Larger numbers represent stronger signals, with negative numbers indicating potential ‘buy’ opportunities and positive numbers suggesting possible ‘sell’ moments.
█ TanHef Ranks Indicator:
View the TanHef Ranks Indicator description prior to using the screener.
█ Ticker Input Method:
Add tickers to the screener using a text area list in a CSV-styled (comma-separated values) list and/or through individual ticker inputs. The text area supports various delimiters, including commas, spaces, semicolons, apostrophes, and new lines. To ensure the expected exchange is used, the exchange prefix should be included when using a text area list.
█ Pair Configuration:
Quickly set up specific trading pairs by comparing tickers to the chart’s symbol or a specified input. This feature is useful for identifying opportunities in obscure trading pairs.
█ Total Combined Average Rank:
Compute the average rank of all tickers to highlighting overall market opportunities. When combined with the 'Pair Configuration' settings, it allows for identifying specific opportunities where one ticker may present a better trading opportunity relative to others.
█ Screener Display Settings:
Customize color-coded rank thresholds, text details, toggle visibility of numerical rankings, and other display settings. Hover over tickers for tooltips with full ticker names and rankings, ideal for small fonts or screens.
█ Alerts:
Set up alerts for individual ticker ranks or total average ranks. To avoid inconsistent or excessive alerts within a short period of time due to TradingView's alert frequency limits, it is recommended to use alerts set to occur at bar close to guarantee alerts. For immediate alerts, consider configuring them directly within the ‘TanHef Ranks’ indicator for better reliability. For the most up-to-date suggestions, hover the tooltips within the indicator’s alert settings.
█ Additional Clarity:
All the settings and functionality are described in detail within the tooltips beside each setting in the indicator’s settings. Hover over each tooltip for comprehensive explanations and guidance on how to configure and use the screener effectively.
█ How To Access:
Follow the Author's Instructions below to get access.
Net Buying/Selling Flows Toolkit [AlgoAlpha]🌟📊 Introducing the Net Buying/Selling Flows Toolkit by AlgoAlpha 📈🚀
🔍 Explore the intricate dynamics of market movements with the Net Buying/Selling Flows Toolkit designed for precision and effectiveness in visualizing money inflows and outflows and their impact on asset prices.
🔀 Multiple Display Modes : Choose from "Flow Comparison", "Net Flow", or "Sum of Flows" to view the data in the most relevant way for your analysis.
📏 Adjustable Unit Display : Easily manage the magnitude of the values displayed with options like "1 Billion", "1 Million", "1 Thousand", or "None".
🔧 Lookback Period Customization : Tailor the sum calculation window with a configurable lookback period, applicable in "Sum of Flows" mode.
📊 Deviation Thresholds : Set up lower and upper deviation thresholds to identify significant changes in flow data.
🔄 Reversal Signals and Deviation Bands : Enable signals for potential reversals and visualize deviation bands for comparative analysis.
🎨 Color-coded Visualization : Distinct colors for upward and downward movements make it easy to distinguish between buying and selling pressures.
🚀 Quick Guide to Using the Net Buying/Selling Flows Toolkit :
🔍 Add the Indicator : Add the indicator to you favorites. Customize the settings to fit your trading requirements.
👁️🗨️ Data Analysis : Compare the trend of Buying and Selling to help indicate whether bulls or bears are in control of the market. Utilize the different display modes to present the data in different form to suite your analysis style.
🔔 Set Alerts : Activate alerts for reversal conditions to keep abreast of significant market movements without having to monitor the charts constantly.
🌐 How It Works :
The toolkit processes volume data on a lower timeframe to distinguish between buying and selling pressures based on intra-bar price closing higher or lower than it opened. It aggregates these transactions and finds the net selling and buying that took place during that bar, offering a clearer view of market fundamentals. The indicator then plots this data visually with multiple modes including comparisons between buying/selling and the net flow of the asset. Deviation thresholds help in identifying significant changes, allowing traders to spot potential buying or selling opportunities based on the money flow dynamics. The "Sum of Flows" mode is unique from other trend following indicators as it does not determine trend based on price action, but rather based on the net buying/selling. Therefore in some cases the "Sum of Flows" mode can be a leading indicator showing bullish/bearish net flows even before the prices move significantly.
Embark on a more informed trading journey with this dynamic and insightful tool, tailor-made for those who demand precision and clarity in their trading strategies. 🌟📉📈
Simple Volatility MomentumOverview:
The Simple Volatility Momentum indicator calculates the mean and standard deviation of the changes of price (returns) using various types of moving averages (Incremental, Rolling, and Exponential). With quantifying the dispersion of price data around the mean, statistical insights are provided on the volatility and the movements of price and returns. The indicator also ranks the mean absolute value of the changes of price over a specified time period which helps you assess the strength of the "trend" and "momentum" regardless of the direction of returns.
Simple Volatility Momentum
This indicator can be used for mean reversion strategies and "momentum" or trend based strategies.
The indicator calculates the average return as the momentum metric and then gets the moving average of the average return and standard deviations from average return average. On the options you can determine if you want to use 1 or 2 standard deviation bands or have both of them enabled.
Settings:
Source: By default it's at close.
M Length: This is the length of the "momentum".
Rank Length: This is the length of the rank calculation of absolute value of the average return
MA Type: This is the different type of calculations for the mean and standard deviation. By default its at incremental.
Smoothing factor: (Only used if you choose the exponential MA type.)
The absolute value of the average return helps you see the strength of the "momentum" and trend. If there is a low ranking of the absolute value of the average return then you can eventually expect it to increase which means that the average return is trending, leading to trending price moves. If the Mean ABS rank value is at or near the maximum value 100 and the average return is at -2 standard deviation from the mean, you can see it as the negative momentum or trend being "finished". Similarly, if the Mean ABS value is near or at the maximum value 100 and the average return is at +2 standard deviation from the mean, you can view the uptrend, as "finished" and the Mean ABS rank can't really go higher than 100.
Moving Average Calculations type:
Incremental: Incremental moving averages use an incremental approach to update the moving average by adding the newest data point and subtracting the oldest one.
Exponential: The exponential moving average gives more weight to recent data points while still considering older ones. This is achieved by applying a smooth factor to the previous EMA value and the current data point. EMA's react more quickly to recent changes in the data compared to simple moving averages, making them useful for short term trends and momentum in financial markets.
Rolling: The moving average is calculated by taking the average of a fixed number of data points within a defined window. As new data becomes available, the window moves forward and the average is recalculated. Rolling Moving Averages are useful for smoothing out short-fluctuations and identifying trends over time.
Important thing to note about indicators involving bands and "momentum" or "trend" or prices:
For the explanation we will assume that stock returns follow a normal distribution and price follows a log normal distribution. Please note that in the live market this assumption isn't always true. Many people incorrectly use standard deviations on prices and trade them as mean reversion strategies or overbought or oversold levels which is not what standard deviations are meant for. Assuming you have applied the log transformation on the standard deviation bands (if your input is raw price then you should use a log transformation to remove the skewness of price), and you have a range of 2 standard deviations from the mean, under the empirical rule with enough occurrences 95% of the values will be within the 2 standard deviation range. This doesn't mean that if price falls to the bottom of the 2 standard deviation bound, there is a 95% chance it will revert back to mean, this is incorrect and not how standard deviations or mean reversion works.
"MOMENTUM"
In finance "momentum" refers to the rate of change of a time series data point. It shows the persistence or tendency for a data series to continue moving in its current direction. In finance, "momentum" based strategies capitalize on the observed tendency of assets that have performed well (or poorly) in the recent past to continue performing well (or poorly) in the near future. This persistence is often observed in various financial instruments including stocks, currencies and commodities.
"Momentum" is commonly calculated with the average return, and relies on the assumption that assets with positive "momentum" or a positive average return will likely continue to perform well in the short to medium term, while assets with a negative average return are expected to continue underperforming. This average return or expected value is derived from historical observations and statistical analysis of previous price movements. However, real markets are subject to levels of efficiencies, market fluctuations, randomness, and may not always produce consistent returns over time involving momentum based strategies.
Mean Reversion:
In finance, the average return is an important parameter in mean reversion strategies. Using statistical methodologies, mean reversion strategies aim to exploit the deviations from the historical average return by identifying instances where current prices and their changes diverge from their expected levels based on past performance. This approach involves statistical analysis and predictive modelling techniques to check where and when the average rate of change is likely to revert towards the mean. It's important to know that mean reversion is a temporary state and will not always be present in a specific timeseries.
Using the average return over price offers several advantages in finance and trading since it is less sensitive to extreme price movements or outliers compared to raw price data. Price itself contains a distribution that is usually positively-skewed and has no upper bound. Mean reversion typically occurs in distributions where extreme values are followed by a tendency for the variable to return towards its mean over time, however the probability distribution of price has no tendency for values to revert towards any specific level. Instead, values may continue to increase without a bound. Returns themself contain more stationary behavior than price levels. Mean reversion strategies rely on the assumption that deviations from the mean will eventually revert back to the mean. Returns, being more likely to exhibit stationary, are better suited for mean reversion based strategies.
The distribution of returns are often more symmetrically distributed around their mean compared to price distributions. This symmetry makes it easier to identify deviations from the mean and assess the likelihood of mean reversion occurrence. Returns are also less sensitive to trends and long-term price movements compared to price levels. Mean reversion strategies aim to exploit deviations from mean, which can be obscured when analyzing raw price data since raw price is almost always trending. Returns can filter out the trend component of price movements, making it easier to identify opportunities.
Stationary Process: Implication that properties like mean and variance remain relatively constant over time.
Crypto Realized Profits/Losses Extremes [AlgoAlpha]🌟🚀 Introducing the Crypto Realized Profits/Losses Extremes Indicator by AlgoAlpha 🚀🌟
Unlock the potential of cryptocurrency markets with our cutting-edge On-Chain Pine Script™ indicator, designed to highlight extreme realized profit and loss zones! 🎯📈
Key Features:
✨ Realized Profits/Losses Calculation: Uses real-time data from the blockchain to monitor profit and loss realization events.
📊 Multi-Crypto Compatibility: The Indicator is compatible on other Crypto tickers besides Bitcoin.
⚙️ Customizable Sensitivity: Adjust the look-back period, normalization period, and deviation thresholds to tailor the indicator to your trading style.
🎨 Visual Enhancements: Choose from a variety of colors for up and down trends, and toggle extreme profit/loss overlay for easy viewing.
🔔 Integrated Alerts: Set up alerts for high and extreme profit or loss conditions, helping you stay ahead of significant market movements.
🔍 How to Use:
🛠 Add the Indicator: Add the indicator to favorites. Customize settings like period lengths and deviation thresholds according to your needs.
📊 Market Analysis: Monitor the main oscillator and the bands to understand current profit and loss extremes in the market. When the oscillator is at the upper band, this means that the market is doing really well and traders/investors will be likely to take profit and cause a reversal. The opposite is true when the oscillator reaches the lower band. The main oscillator can also be used for trend analysis.
🔔 Set Alerts: Configure alerts to notify you when the market enters a zone of high profit or loss, or during trend changes, enabling timely decisions without constant monitoring.
How It Works:
The indicator calculates a normalized area under the RSI curve applied on on-chain data regarding the number of wallets in profit. It employs a custom "src" variable that aggregates data from the blockchain about profit and loss addresses, adapting to intraday or longer timeframes as needed. The main oscillator plots this normalized area, while the upper and lower bands are plotted based on a deviation metric to identify extreme conditions. Colored fills between these bands visually denote these zones. For interaction, the indicator plots bubbles for extreme profits or losses and provides optional bar coloring to reflect the current market trend.
🚀💹 Enjoy a comprehensive, customizable, and visually engaging tool that helps you stay ahead in the fast-paced crypto market!
Candlestick Reversal and Trend Signals [AlgoAlpha]🚀 Unleash your charting capabilities with the Candlestick Reversal and Trend Signals indicator by AlgoAlpha, your go-to tool for spotting pivotal market movements! This script enhances your trading experience by identifying key candlestick patterns and trend changes, perfect for traders aiming for precision in their technical analysis.
🛠 Key Features:
- 🔄 Multi-Timeframe Analysis : Leverages a timeframe multiplier to analyze levels on higher timeframes, enhancing the depth and applicability of insights.
- 🧩 Diverse Pattern Detection : Capable of detecting a wide array of patterns including Bull/Bear Engulfings, Dojis, Haramis, Piercing Lines, Dark Cloud Covers, and Morning/Evening Stars, each contributing to a robust trading strategy.
- 🔍 Dynamic Trend Filters : Utilizes three exponential moving averages (EMAs) and volume filters to decisively confirm trend directions and strength, providing a clearer picture of market dynamics.
- ⚙️ Customizable Settings : Features adjustable settings for filter period, signal thresholds, and appearance, allowing for a tailored analysis experience to fit individual trading styles.
- 📉 Swing Levels Identification : Marks significant high and low swing points on the chart, highlighting potential pivot points and trend reversals for strategic trading decisions.
📈 Quick Guide to Using the Candlestick Reversal and Trend Signals Indicator
1. 🛠 Add the Indicator : Add the indicator to your favorites. Adjust the settings to match your analysis needs.
2. 📊 Analysis : Keep an eye out for the specific symbols plotted on your chart that indicate various candlestick patterns. Use these signals to enhance your market analysis.
3. 🔔 Set Alerts : Enable alerts for the patterns you are most interested in to get notified of potential trading opportunities without needing to monitor the charts constantly.
Embark on your enhanced trading journey with this powerful tool! 🚀✨ Happy trading!
🧐 How It Works:
The Candlestick Reversal and Trend Signals indicator operates by integrating several candlestick patterns and trend analysis features to assist in making informed trading decisions. Initially, it gathers user-defined settings like the period for filtering, signal thresholds, and the desired patterns to detect. It analyzes candlestick formations such as Bull/Bear Engulfings, Dojis, Haramis, and more, by comparing the current candlestick's attributes (such as body length and direction) with previous data to identify potential market reversals or confirmations. The indicator enhances its accuracy through additional filters like volume ratios and exponential moving averages (EMAs) that help validate the strength and direction of trends. By marking these patterns and trends visually on the chart, it provides clear signals that aid traders in identifying significant market movements efficiently. The script is then complemented with the 3 EMA indicator for trend detection and swing levels for added confluence.
Unmitigated Liquidity Imbalances [AlgoAlpha]🎉 Introducing the Unmitigated Liquidity Imbalance Indicator by AlgoAlpha! 🎉
Dive into the depths of market analytics with our "Unmitigated Liquidity Imbalance" indicator. This tool harnesses unique algorithms to detect liquidity imbalances between bulls and bears, helping traders spot trends and potential entry and exit points with greater accuracy. 📈🚀
🔍 Key Features:
🌟 Advanced Analysis : Analyses candle direction and length to forecast market peaks and valleys.
🎨 Customizable Visuals : Tailor the chart with your choice of bullish green or bearish red to reflect different market conditions.
🔄 Real-Time Updates : Continuously updates to reflect live market changes.
🔔 Configurable Alerts : Set up alerts for key trading signals such as bullish and bearish reversals, as well as trend shifts.
📐 How to Use:
🛠 Add the Indicator : Add the indicator to your favourites and customize the settings to suite your needs.
📊 Market Analysis : Monitor the oscillator threshold; readings above 0.5 suggest bullish sentiment, while below 0.5 indicate bearish conditions. And reversal signals are displayed to show potential entry points.
🔔 Set Alerts : Enable notifications for reversal conditions or trend changes to seize trading opportunities without constant chart watching.
🧠 How It Works:
The core mechanism of the indicator is based on detecting changes in candlestick size and direction to identify bullish and bearish liquidity levels from the peak & valley indicator's logic. By comparing the length of a current candle to the previous one and checking the change in direction, it pinpoints moments where market sentiment could be shifting, indicating if the liquidity at that point is bullish or bearish. The script then looks at what percentage of the past few unmitigated levels are bullish or bearish based on a customizable lookback and determines the liquidity imbalance which can then be interpreted as trend.
Empower your trading with the Unmitigated Liquidity Imbalance indicator and navigate the markets with confidence and precision. 🌟💹
Happy trading, and may your charts be ever in your favour! 🥳✨
💎 Related Indicator
Smart Money Liquidity Heatmap [AlgoAlpha]🌟📈 Introducing the Smart Money Liquidity Heatmap by AlgoAlpha! 🗺️🚀
Dive into the depths of market liquidity with our innovative Pine Script™ indicator designed to illuminate the trading actions of smart money! This meticulously crafted tool provides an enhanced visualization of liquidity flow, highlighting the dynamics between smart and retail investors directly on your chart! 🌐🔍
🙌 Key Features of the Smart Money Liquidity Heatmap:
🖼️ Visual Clarity: Uses vibrant heatmap colors to represent liquidity concentrations, making it easier to spot significant trading zones.
🔧 Customizable Settings: Adjust index periods, volume flow periods, and more to tailor the heatmap to your trading strategy.
📊 Dynamic Ratios: Computes the ratio of smart money to retail trading activity, providing insights into who is driving market movements.
👓 Transparency Options: Modify color intensity for better visibility against various chart backgrounds.
🛠 How to Use the Smart Money Liquidity Heatmap:
1️⃣ Add the Indicator:
Add the indicator to favourites. Customize settings to align with your trading preferences, including periods for index calculation and volume flow.
2️⃣ Market Analysis:
Monitor the heatmap for high liquidity zones signalled by the heatmap. These are potential areas where smart money is actively engaging, providing crucial insights into market dynamics.
Basic Logic Behind the Indicator:
The Smart Money Liquidity Heatmap utilizes the Smart Money Interest Index Indicator and operates by differentiating between the trading behaviors of informed (smart money) and less-informed (retail) traders. It calculates the differences between specific volume indices—Positive Volume Index (PVI) for retail investors and Negative Volume Index (NVI) for institutional players—and their respective moving averages, highlighting these differences using the Relative Strength Index (RSI) over user-specified periods. This calculation generates a ratio that is then normalized and compared against a threshold to identify areas of high institutional trading interest, visually representing these zones on your chart as vibrant heatmaps. This enables traders to visually identify where significant trading activities among smart money are occurring, potentially signalling important buying or selling opportunities.
🎉 Elevate your trading experience with precision, insight, and clarity by integrating the Smart Money Liquidity Heatmap into your toolkit today!
Rolling Point of Control (POC) [AlgoAlpha]Enhance your trading decisions with the Rolling Point of Control (POC) Indicator designed by AlgoAlpha! This powerful tool displays a dynamic Point of Control based on volume or price profiles directly on your chart, providing a vivid depiction of dominant price levels according to historical data. 🌟📈
🚀 Key Features:
Profile Type Selection: Choose between Volume Profile and Price Profile to best suit your analysis needs.
Adjustable Lookback Period: Modify the lookback period to consider more or less historical data for your profile.
Customizable Resolution and Scale: Tailor the resolution and horizontal scale of the profile for precision and clarity.
Trend Analysis Tools: Enable trend analysis with the option to display a weighted moving average of the POC.
Color-Coded Feedback: Utilize color gradients to quickly identify bullish and bearish conditions relative to the POC.
Interactive Visuals: Dynamic rendering of profiles and alerts for crossing events enhances visual feedback and responsiveness.
Multiple Customization Options: Smooth the POC line, toggle profile and fill visibility, and choose custom colors for various elements.
🖥️ How to Use:
🛠 Add the Indicator:
Add the indicator to favorites and customize settings like profile type, lookback period, and resolution to fit your trading style.
📊 Market Analysis:
Monitor the POC line for significant price levels. Use the histogram to understand price distributions and locate major market pivots.
🔔 Alerts Setup:
Enable alerts for price crossing over or under the POC, as well as for trend changes, to stay ahead of market movements without constant chart monitoring.
🛠️ How It Works:
The Rolling POC indicator dynamically calculates the Point of Control either based on volume or price within a user-defined lookback period. It plots a histogram (profile) that highlights the level at which the most trading activity has occurred, helping to identify key support and resistance levels.
Basic Logic Overview:
- Data Compilation: Gathers high, low, and volume (if volume profile selected) data within the lookback period.
- Histogram Calculation: Divides the price range into bins (as specified by resolution), counting hits in each bin to find the most frequented price level.
- POC Identification: The price level with the highest concentration of hits (or volume) is marked as the POC.
- Trend MA (Optional): If enabled, the indicator plots a moving average of the POC for trend analysis.
By integrating the Rolling Point of Control into your charting toolkit, you can significantly enhance your market analysis and potentially increase the accuracy of your trading decisions. Whether you're day trading or looking at longer time frames, this indicator offers a detailed, customizable perspective on market dynamics. 🌍💹
