123 Reversal Trading StrategyThe 123 Reversal Trading Strategy is a technical analysis approach that seeks to identify potential reversal points in the market by analyzing price patterns. This Pine Script™ code implements a version of this strategy, and here’s a detailed description:
Strategy Overview
Objective: The strategy aims to identify bullish reversal patterns using the 123 pattern and manage trades with a specified holding period and a 20-day moving average as an additional exit condition.
Key Components:
Holding Period: The number of days to hold a trade is adjustable, with the default set to 7 days.
Moving Average: A 200-day simple moving average (SMA) is used to determine an exitcondition based on the price crossing this average.
Pattern Recognition:
Condition 1: The low of the current day must be lower than the low of the previous day.
Condition 2: The low of the previous day must be lower than the low from three days ago.
Condition 3: The low two days ago must be lower than the low from four days ago.
Condition 4: The high two days ago must be lower than the high three days ago.
Entry Condition: All four conditions must be met for a buy signal.
Exit Condition: The position is closed either after the specified holding period or when the price reaches or exceeds the 200-day moving average.
Relevant Literature
Graham, B., & Dodd, D. L. (1934). Security Analysis. This classic work introduces fundamental analysis and technical analysis principles which are foundational to understanding patterns like the 123 reversal.
Murphy, J. J. (1999). Technical Analysis of the Financial Markets. Murphy provides an extensive overview of technical indicators and chart patterns, including reversal patterns similar to the 123 pattern.
Elder, A. (1993). Trading for a Living. Elder discusses various trading strategies and technical analysis techniques that complement the understanding of reversal patterns and their application in trading.
Risks and Considerations
Pattern Reliability: The 123 reversal pattern, like many technical patterns, is not foolproof. It can generate false signals, especially in volatile or trending markets. This may lead to losses if the pattern does not play out as expected.
Market Conditions: The strategy may perform differently under various market conditions. In strongly trending markets, reversal patterns might not be as reliable.
Lagging Indicators: The use of the 200-day moving average as an exit condition can be considered a lagging indicator. This means it reacts to price movements with a delay, which might result in late exits and missed profit opportunities.
Holding Period: The fixed holding period of 7 days may not be optimal for all market conditions or stocks. It is essential to adjust the holding period based on market dynamics and individual stock behavior.
Overfitting: The parameters used (like the number of days and moving average length) are set based on historical data. Overfitting can occur if these parameters are tailored too specifically to past data, leading to reduced performance in future scenarios.
Conclusion
The 123 Reversal Trading Strategy is designed to identify potential market reversals using specific conditions related to price lows and highs. While it offers a structured approach to trading, it is essential to be aware of its limitations and potential risks. As with any trading strategy, it should be tested thoroughly in various market conditions and adjusted according to the individual trading style and risk tolerance.
ค้นหาในสคริปต์สำหรับ "200元+股票大盘"
Bollinger Bands Enhanced StrategyOverview
The common practice of using Bollinger bands is to use it for building mean reversion or squeeze momentum strategies. In the current script Bollinger Bands Enhanced Strategy we are trying to combine the strengths of both strategies types. It utilizes Bollinger Bands indicator to buy the local dip and activates trailing profit system after reaching the user given number of Average True Ranges (ATR). Also it uses 200 period EMA to filter trades only in the direction of a trend. Strategy can execute only long trades.
Unique Features
Trailing Profit System: Strategy uses user given number of ATR to activate trailing take profit. If price has already reached the trailing profit activation level, scrip will close long trade if price closes below Bollinger Bands middle line.
Configurable Trading Periods: Users can tailor the strategy to specific market windows, adapting to different market conditions.
Major Trend Filter: Strategy utilizes 100 period EMA to take trades only in the direction of a trend.
Flexible Risk Management: Users can choose number of ATR as a stop loss (by default = 1.75) for trades. This is flexible approach because ATR is recalculated on every candle, therefore stop-loss readjusted to the current volatility.
Methodology
First of all, script checks if currently price is above the 200-period exponential moving average EMA. EMA is used to establish the current trend. Script will take long trades on if this filtering system showing us the uptrend. Then the strategy executes the long trade if candle’s low below the lower Bollinger band. To calculate the middle Bollinger line, we use the standard 20-period simple moving average (SMA), lower band is calculated by the substruction from middle line the standard deviation multiplied by user given value (by default = 2).
When long trade executed, script places stop-loss at the price level below the entry price by user defined number of ATR (by default = 1.75). This stop-loss level recalculates at every candle while trade is open according to the current candle ATR value. Also strategy set the trailing profit activation level at the price above the position average price by user given number of ATR (by default = 2.25). It is also recalculated every candle according to ATR value. When price hit this level script plotted the triangle with the label “Strong Uptrend” and start trail the price at the middle Bollinger line. It also started to be plotted as a green line.
When price close below this trailing level script closes the long trade and search for the next trade opportunity.
Risk Management
The strategy employs a combined and flexible approach to risk management:
It allows positions to ride the trend as long as the price continues to move favorably, aiming to capture significant price movements. It features a user-defined ATR stop loss parameter to mitigate risks based on individual risk tolerance. By default, this stop-loss is set to a 1.75*ATR drop from the entry point, but it can be adjusted according to the trader's preferences.
There is no fixed take profit, but strategy allows user to define user the ATR trailing profit activation parameter. By default, this stop-loss is set to a 2.25*ATR growth from the entry point, but it can be adjusted according to the trader's preferences.
Justification of Methodology
This strategy leverages Bollinger bangs indicator to open long trades in the local dips. If price reached the lower band there is a high probability of bounce. Here is an issue: during the strong downtrend price can constantly goes down without any significant correction. That’s why we decided to use 200-period EMA as a trend filter to increase the probability of opening long trades during major uptrend only.
Usually, Bollinger Bands indicator is using for mean reversion or breakout strategies. Both of them have the disadvantages. The mean reversion buys the dip, but closes on the return to some mean value. Therefore, it usually misses the major trend moves. The breakout strategies usually have the issue with too high buy price because to have the breakout confirmation price shall break some price level. Therefore, in such strategies traders need to set the large stop-loss, which decreases potential reward to risk ratio.
In this strategy we are trying to combine the best features of both types of strategies. Script utilizes ate ATR to setup the stop-loss and trailing profit activation levels. ATR takes into account the current volatility. Therefore, when we setup stop-loss with the user-given number of ATR we increase the probability to decrease the number of false stop outs. The trailing profit concept is trying to add the beat feature from breakout strategies and increase probability to stay in trade while uptrend is developing. When price hit the trailing profit activation level, script started to trail the price with middle line if Bollinger bands indicator. Only when candle closes below the middle line script closes the long trade.
Backtest Results
Operating window: Date range of backtests is 2020.10.01 - 2024.07.01. 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: 30%
Maximum Single Position Loss: -9.78%
Maximum Single Profit: +25.62%
Net Profit: +6778.11 USDT (+67.78%)
Total Trades: 111 (48.65% win rate)
Profit Factor: 2.065
Maximum Accumulated Loss: 853.56 USDT (-6.60%)
Average Profit per Trade: 61.06 USDT (+1.62%)
Average Trade Duration: 76 hours
These results are obtained with realistic parameters representing trading conditions observed at major exchanges such as Binance and with realistic trading portfolio usage parameters.
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
EMA Cross Fibonacci Entry with RetracementThe EMA Cross Fibonacci Entry with Retracement is a trading strategy that combines two popular technical analysis tools: Exponential Moving Averages (EMAs) and Fibonacci retracement levels. Here's a brief overview of how this strategy typically works:
### Exponential Moving Averages (EMAs)
1. **EMAs Calculation**: EMAs give more weight to recent price data, making them more responsive to price changes. Commonly used periods for EMAs in this strategy are the 50-period and 200-period EMAs.
2. **EMA Cross**: The strategy looks for a "golden cross" (short-term EMA crosses above the long-term EMA) as a potential buy signal, and a "death cross" (short-term EMA crosses below the long-term EMA) as a potential sell signal.
### Fibonacci Retracement Levels
1. **Fibonacci Retracement**: This tool is used to identify potential support and resistance levels based on the Fibonacci sequence. The key retracement levels are 23.6%, 38.2%, 50%, 61.8%, and 78.6%.
2. **Drawing Retracement Levels**: Traders draw Fibonacci retracement levels from a significant peak to a significant trough (or vice versa) to identify potential retracement levels where the price might reverse.
### Combining EMA Cross with Fibonacci Retracement
1. **Identify EMA Cross**: First, traders look for an EMA cross. For example, a golden cross where a shorter EMA (e.g., 50 EMA) crosses above a longer EMA (e.g., 200 EMA) suggests a bullish trend.
2. **Wait for Retracement**: After identifying a cross, traders wait for the price to retrace to a Fibonacci level. The key levels to watch are 38.2%, 50%, and 61.8%.
3. **Entry Point**: The entry point is when the price retraces to a Fibonacci level and shows signs of reversal (e.g., bullish candlestick patterns, support at Fibonacci levels). This is typically when traders enter a long position.
4. **Confirmation with EMA**: Ensure that the EMAs support the trend. For a buy entry, the short-term EMA should remain above the long-term EMA.
### Example of a Bullish Entry
1. **Golden Cross**: 50 EMA crosses above 200 EMA.
2. **Retracement**: Price retraces to the 38.2% Fibonacci level.
3. **Entry Signal**: At the 38.2% level, a bullish candlestick pattern (e.g., hammer) forms, indicating potential support.
4. **Entry Point**: Enter a long position at the close of the bullish candlestick.
### Risk Management
1. **Stop Loss**: Place a stop loss below the next Fibonacci retracement level or below the recent swing low to limit potential losses.
2. **Take Profit**: Set a take profit target based on a risk-reward ratio, previous resistance levels, or further Fibonacci extensions.
### Conclusion
The EMA Cross Fibonacci Entry with Retracement strategy is a systematic approach to identifying entry points in a trending market. By combining the responsiveness of EMAs with the predictive power of Fibonacci retracement levels, traders aim to enter trades at optimal points, increasing their chances of success while managing risk effectively.
Golden Area### Golden Area Indicator Description
The "Golden Area" indicator is a technical analysis tool designed to assist traders by identifying potential buy and sell signals based on moving averages and support/resistance levels within a specific time frame. This indicator can be applied directly to price charts.
#### How It Works
1. **Inputs:**
- **MA50 Length:** The period length for the 50-period Simple Moving Average (SMA).
- **MA200 Length:** The period length for the 200-period Simple Moving Average (SMA).
2. **Calculations:**
- **MA50 (50-period SMA):** Calculated by averaging the closing prices over the past 50 periods.
- **MA200 (200-period SMA):** Calculated by averaging the closing prices over the past 200 periods.
- **Support Level:** The lowest price over the last 50 periods.
- **Resistance Level:** The highest price over the last 50 periods.
3. **Time Filter:**
- **Start Time:** The indicator becomes active at 12:30 IST (07:00 UTC).
- **End Time:** The indicator deactivates at 10:30 IST the next day (05:00 UTC).
- A background color change (yellow) highlights the active time range on the chart.
4. **Signals:**
- **Buy Signal:** Triggered when the current time matches the start time and the closing price is below the support level.
- **Sell Signal:** Triggered when the current time matches the start time and the closing price is above the resistance level.
5. **Plots:**
- **MA50:** Plotted as a blue line on the chart.
- **MA200:** Plotted as a red line on the chart.
- **Buy Signals:** Indicated by a green 'B' below the bars.
- **Sell Signals:** Indicated by a red 'S' above the bars.
This indicator provides visual cues for potential trading opportunities within the specified time frame, aiding traders in making informed decisions.
Multi-Timeframe Trend Cloud (EMA13/21) with Alerts Purpose:
This indicator combines trend analysis across multiple timeframes with alerts to help identify general trends and market shifts. It visualizes trends by creating EMA clouds (The area between EMA13 & EMA21), detects confirmed EMA crossovers, and can alert users when the price re-enters the cloud or interacts with the EMA200.
Input Parameters:
Lower Timeframe (LTF): Allows you to select the lower timeframe for trend analysis (e.g., 1 hour, 4 hours).
Higher Timeframe (HTF): Allows you to select the higher timeframe for the main trend reference (e.g., 4 hours, 1 day).
Fill Colors: You can customize the colors used to fill the areas between the EMA lines in both the higher and lower timeframes. Note: The LTF cloud defaults to transparent white so if you have a light background change the color in style settings.
The EMA's default to transparent but can be turned on in the style settings.
Calculating & Plotting EMAs:
The indicator calculates two Exponential Moving Averages (EMAs) on both the LTF and HTF: a faster 13-period EMA and a slower 21-period EMA.
Additionally, it calculates a 200-period EMA on the HTF.
These EMAs are plotted on your chart, providing a visual representation of the trend.
Identifying Trend States:
The script uses the relationships between the price and the 13-period and 21-period EMAs on the Higher Time Frame (HTF) to identify four distinct trend states, each depicted by a specific color to create the "Trend Cloud":
Strong Bull Trend: The 13-period EMA is above the 21-period EMA, and the price is above both EMAs. --- "Color 0" in HTF Trend style settings.
Broken Bullish Trend: The 13-period EMA is above the 21-period EMA but price has broken below both EMA's. --- "Color 3" in HTF Trend style settings.
Strong Bear Trend: The 13-period EMA is below the 21-period EMA, and the price is below both EMAs. --- "Color 2" in HTF Trend style settings.
Broken Bearish Trend: The 13-period EMA is below the 21-period EMA but price has broken above both EMA's. --- "Color 1" in HTF Trend style settings.
Important Note: The 200-period EMA is plotted for reference but is not directly used in determining the current trend state within this script.
Confirmed Crossover Signals:
The indicator plots upward or downward triangles to signal confirmed crossovers of the EMA13 and EMA21 on the HTF. A crossover is considered "confirmed" when it's followed by a candle closing on the same side of the crossing point, adding an extra layer of confidence to the signal.
Cloud Re-entry Alerts:
Receive alerts whenever the price re-enters the HTF cloud - aka "Trend".
EMA200 Retest Alerts:
Get alerts when the price touches the 200 EMA on the HTF. These alerts can be valuable for identifying potential trend reversals or trend continuation scenarios.
Benefits:
Clear Visual Representation: Easily visualize trends on both the lower and higher timeframes.
Confirmed Signals: Filter out false signals by focusing on confirmed crossovers.
Timely Alerts: Get instant notifications for important price actions, allowing you to react quickly to market opportunities.
Customizable: Tailor the indicator's appearance and alert settings to your preferences.
How to Use:
Add the indicator to your chart.
Select your desired LTF and HTF in the Inputs tab.
Customize the fill colors (and optional EMA line colors) in the Style tab.
Enable the alerts you want to receive in the Alerts tab.
Note: This indicator is a great tool for trend analysis, but it should be used with other forms of analysis and risk management techniques to make informed trading decisions.
Bank Nifty Market Breadth (OsMA)This indicator is the market breadth for Bank Nifty (NSE:BANKNIFTY) index calculated on the histogram values of MACD indicator. Each row in this indicator is a representation of the histogram values of the individual stock that make up Bank Nifty. Components are listed in order of its weightage to Bank nifty index (Highest -> Lowest).
When you see Bank Nifty is on an uptrend on daily timeframe for the past 10 days, you can see what underlying stocks support that uptrend. The brighter the plot colour, the higher the momentum and vice versa. Looking at the individual rows that make up Bank Nifty, you can have an understanding if there is still enough momentum in the underlying stocks to go higher or are there many red plots showing up indicating a possible pullback or trend reversal.
The plot colours are shown as a percentage of the current histogram value taken from MACD from the highest histogram value of the previous 200 bars shown on the current timeframe. Look back value of 200 bars was chosen as it provided a better representation of the current value from its peak over the recent past(previous 200 bars), on all timeframes. Histogram value do grow/fall along with the underlying stock price, so choosing the chart's all-time high/low value as peak was not ideal. Labels on the right show the current histogram value.
Base Code taken from @fengyu05's S&P 500 Market Breadth indicator.
SMA Comparison with Buy and Sell Signals ShrutIndicator Name: SMA Comparison with Buy and Sell Signals
Overlay: Enabled (Indicator is displayed on the main price chart)
Description:
The "SMA Comparison with Buy and Sell Signals" indicator is designed to identify potential buying and selling opportunities in a financial instrument by comparing three Simple Moving Averages (SMAs) – the 20-day SMA, 50-day SMA, and 200-day SMA.
Features:
Simple Moving Averages (SMAs):
Calculates and displays three SMAs based on the closing price: SMA-20, SMA-50, and SMA-200.
Buy and Sell Conditions:
Buy Condition : Triggered when the 200-day SMA is greater than the 50-day SMA, the 50-day SMA is greater than the 20-day SMA, and the current closing price is lower than the 20-day SMA.
Sell Condition: Triggered when the 200-day SMA is less than the 50-day SMA, the 50-day SMA is less than the 20-day SMA, and the current closing price is higher than the 20-day SMA.
Signal Generation:
Generates buy and sell signals on the chart based on the identified conditions.
Implements a 15-day cooldown period between consecutive buy or sell signals to prevent frequent signals in volatile market conditions.
Signal Display:
Displays buy signals as green triangle shapes below the price bars.
Displays sell signals as red triangle shapes above the price bars.
Usage:
Buy Signals: Considered when the green triangle shapes (buy signals) appear below the price bars, indicating a potential buying opportunity based on the defined SMA conditions.
Sell Signals: Considered when the red triangle shapes (sell signals) appear above the price bars, indicating a potential selling opportunity based on the defined SMA conditions.
Notes:
This indicator is customizable and can be adjusted by modifying the conditions based on specific trading strategies and preferences.
Traders should consider additional analysis and risk management strategies before making trading decisions based solely on the indicator signals.
Temporary imbalances 2.0 This indicator attempts to calculate potential points of imbalance and equilibrium based on VWAPs and modified moving averages. The idea is to determine if there has been a change in volume and perform the calculation from that point It uses the standard deviation to determine the significant imbalance threshold. Candles with bullish imbalances are highlighted in green, while candles with bearish imbalances are highlighted in red.
"It also features a set of VWAPs and modified moving averages that you can enable or disable."
When you activate the 'Show Anchor VWAP' option, it will add five modified VWAPs.
Practical Significance:
The Anchored VWAP is a volume-weighted average price that serves as a dynamic reference to assess the average price during specific moments of market imbalance.
During a bullish imbalance, the anchor_vwap reflects the VWAP at that moment, emphasizing price behavior during that specific period.
Similarly, in a bearish imbalance, the anchor_vwap provides the associated VWAP for that condition, highlighting price movements during the imbalance phase.
How to Use:
The anchor_vwap can be employed to contextualize the volume-weighted average price during critical moments associated with significant changes in market imbalance.
By analyzing price behavior during and after periods of imbalance, the Anchored VWAP can help better understand market dynamics and identify potential areas of support or resistance.
Show VWAP Percent Imbalance"
Definition: Represents the Volume Weighted Average Price (VWAP) adjusted by the volume-weighted average of the price multiplied by volume, with a focus on conditions where the percentage volume variation surpasses a predefined threshold.
Calculation: Utilizes the simple moving average weighted of the product of the volume-weighted average price and volume only when the percentage volume variation exceeds a specific threshold.
Interpretation: Provides insight into the volume-weighted price trend during conditions where the percentage volume variation exceeds a predefined limit.
The "showDeltaVWAP" is a toggleable setting that you can turn on or off. When activated, it displays special lines on the chart. Let's understand what these lines represent:
Delta Anchor VWAP:
A green line (Delta Anchor VWAP) represents a measure of market volume imbalance.
Delta2 Anchor VWAP:
A red line (Delta2 Anchor VWAP) shows another perspective of volume imbalance.
VWAP Delta Volume:
A light blue line (VWAP Delta Volume) displays a volume-weighted average of price.
VWAP Delta Volume2:
An orange line (VWAP Delta Volume2) shows another view of the volume-weighted average of price.
Delta3 Anchor VWAP:
A light blue line (Delta3 Anchor VWAP) represents a combination of the previous measures.
Delta4 Anchor VWAP:
A purple line (Delta4 Anchor VWAP) is another combination, providing an overall view.
These lines are based on different conditions and calculations related to trading volume. When you activate "showDeltaVWAP," these lines appear on the chart, aiding in better understanding market behavior.
"Show Faster Volatility" is an option that you can enable or disable. When activated (set to true), it displays special lines on the chart called "Faster Volatility VWAP," "Faster Volatility VWAP2," and "Faster Volatility VWAP3." Let's understand what these lines represent:
Faster Volatility VWAP:
A purple line (Faster Volatility VWAP) is a Volume Weighted Average Price (VWAP) that is calculated more quickly based on short-term price reversal patterns.
Faster Volatility VWAP2:
A light gray line (Faster Volatility VWAP2) is another Volume Weighted Average Price (VWAP) that is calculated even more quickly based on even shorter-term price reversal patterns.
Faster Volatility VWAP3:
A purple line (Faster Volatility VWAP3) is another Volume Weighted Average Price (VWAP) calculated rapidly based on even shorter-term price reversal patterns.
These lines are designed to indicate moments of possible exhaustion of volatility in the market, suggesting that there may be a subsequent increase in volatility. When you activate "Show Faster Volatility," these lines are displayed on the chart.
"Show Average VWAPs Imbalance" displays weighted averages of different Volume Weighted Average Prices (VWAPs) in relation to specific market conditions. Here's an explanation of each component:
Standard VWAP:
The blue line represents the standard VWAP, a volume-weighted average of asset prices over a specific period.
VWAP with Added Imbalance (avg_vwap2):
The pink line is a weighted average that adds an imbalance value to the standard VWAP. This component highlights periods of market imbalance.
VWAP with Balance (avg_vwap3):
The lilac line is a weighted average that adds balance based on the imbalance between uptrend and downtrend, reflecting changes in volume. This provides insights into supply and demand dynamics.
Overall Average of VWAPs (avg_vwaptl):
The violet line is a weighted average that incorporates both standard and adjusted VWAPs, offering an overview of market behavior under different considered conditions.
Visual Customization (Show Average VWAPs Imbalance):
Users have the option to show or hide these average lines on the chart, allowing for a clear visualization of market trends.
"Show Min Variation VWAP" is associated with the calculation and display of a smoothed version of the Volume Weighted Average Price (VWAP), taking into account the minimum price variation over a specific period.
"How Imbalance Anchor VWAP Calculated as the smoothed relationship between liquidity difference and maximum VWAP equilibrium" is associated with the calculation and display of a smoothed version of the Imbalance Anchor VWAP. Here is a detailed explanation:
Calculations and Smoothing:
The variable "smoothed_difference" represents the exponential moving average (EMA) of the difference between two variables related to liquidity.
"smoothed_difference2" is the division of "smoothed_difference" by the maximum variation of the VWAP Equilibrium.
"smoothed_difference3" involves additional manipulation of "smoothed_difference" and "vwap_delta3."
"smoothed_difference4" incorporates the previous results, adjusted by the value of the VWAP.
Visual Customization:
The user has the option to enable or disable the display on the chart.
The line is colored in a shade of green.
It provides a smoothed representation of the Imbalance Anchor VWAP.
The line is colored in a shade of blue, and the calculation involves the summation of moving averages (20, 50, 200). Afterward, there is division by 3. Additionally, there is the summation of moving averages (766, 866, 966), divided by 3. The final step is to add these results together and divide by 2. media name is Imbalance Value2
Show VWAP Equilibrium (Max Variation) Calculated as the difference between two VWAPs derived from the highest and lowest price changes
Show Equilibrium VWAP Calculated as the sum of VWAP and (sma200 - sma20)
calculate the difference between the media of 200 to 20
Show Equilibrium VWAP Calculated as the sum of VWAP and (766+866+966)/3 - (sma200 - sma20)
Show Equilibrium VWAP Standard Deviation Calculated as the Exponential Moving Average (EMA) of the Standard Deviation of SMA (sma200 + sma20 + sma8)/3
Show Equilibrium VWAP Delta Calculated as the ratio of the smoothed VWAP Delta Result componentes
Show Standard Deviation Equilibrium VWAP Delta: Calculated as the Standard Deviation between the Average of VWAP Delta Result Components and Their Smoothed Versions
This average attempts to calculate the equilibrium."
vwap_equilibrium:
Definition: Represents the Volume Weighted Average Price (VWAP) adjusted by the volume-weighted average of the price (hl2) multiplied by volume, focusing on periods of volume equilibrium.
Calculation: Utilizes the simple moving average weighted (sma) of the product of the volume-weighted average price and volume only when there is no volume imbalance.
Interpretation: This indicator provides a view of the volume-weighted price trend during moments when the market is in equilibrium, meaning there is no noticeable imbalance in volume conditions. The calculation of VWAP is adjusted to reflect market characteristics during periods of stability.
vwap_percent_condition:
Definition: Represents the Volume Weighted Average Price (VWAP) adjusted by the volume-weighted average of the price multiplied by volume, with a focus on conditions where the percentage volume variation surpasses a predefined threshold.
Calculation: Utilizes the simple moving average weighted of the product of the volume-weighted average price and volume only when the percentage volume variation exceeds a specific threshold.
Interpretation: Provides insight into the volume-weighted price trend during conditions where the percentage volume variation exceeds a predefined limit.
The objective of these two VWAPs is to calculate possible equilibrium points between buyers and sellers.
The indicator works for all timeframes This indicator can be adjusted according to the preferences and characteristics of the specific asset or market. It provides clear visual information and can be used as a complementary tool for technical analysis in trading strategies.
Interesting
Interesting
lookback period 7 , 12, 20,70,200, 500,766,866,966
imbalance threshold 2.4, 3.3 ,4.2
The objective of this indicator is to identify and highlight various points of imbalance and equilibrium.
SMA Cross with a Price FilterA moving average strategy generates an entry (buy) signal when the price goes above the moving average, and an exit (sell) signal when the price goes below the moving average. But it gives lots of whipsaws and noise depends on the moving average we use. A fast moving average gives more whipsaws and a slow moving average gives less whipsaws. To reduce the noise/whipsaws, we can add a filter on a fast/slow moving average. It will improve entry/exit performance significantly specially for those who don't want to watch the market actively.
I created this indicator with a price filter. This means the price of an underlying asset must be at least a specific percentage above its moving average to generate a buy signal and a specific percentage below its moving average to generate a sell signal. This price filter can also be a confirmation after the price crosses above/below its SMA. I couldn't find any indicator yet based on this idea. So I wrote this indicator and publishing it so it helps those who are interested.
I use 200 SMA and 3% price filter as default and using SPY as an example. So,
ENTRY signal when the closing price of SPY is 3% above its 200 SMA.
EXIT signal when the closing price of SPY is 3% below its 200 SMA.
Enjoy and let me know if it works.
** This chart only generates entry (buy) and exit (sell) signals. Please, do your own diligence to make any investment or trading decisions.
Machine Learning: Optimal RSI [YinYangAlgorithms]This Indicator, will rate multiple different lengths of RSIs to determine which RSI to RSI MA cross produced the highest profit within the lookback span. This ‘Optimal RSI’ is then passed back, and if toggled will then be thrown into a Machine Learning calculation. You have the option to Filter RSI and RSI MA’s within the Machine Learning calculation. What this does is, only other Optimal RSI’s which are in the same bullish or bearish direction (is the RSI above or below the RSI MA) will be added to the calculation.
You can either (by default) use a Simple Average; which is essentially just a Mean of all the Optimal RSI’s with a length of Machine Learning. Or, you can opt to use a k-Nearest Neighbour (KNN) calculation which takes a Fast and Slow Speed. We essentially turn the Optimal RSI into a MA with different lengths and then compare the distance between the two within our KNN Function.
RSI may very well be one of the most used Indicators for identifying crucial Overbought and Oversold locations. Not only that but when it crosses its Moving Average (MA) line it may also indicate good locations to Buy and Sell. Many traders simply use the RSI with the standard length (14), however, does that mean this is the best length?
By using the length of the top performing RSI and then applying some Machine Learning logic to it, we hope to create what may be a more accurate, smooth, optimal, RSI.
Tutorial:
This is a pretty zoomed out Perspective of what the Indicator looks like with its default settings (except with Bollinger Bands and Signals disabled). If you look at the Tables above, you’ll notice, currently the Top Performing RSI Length is 13 with an Optimal Profit % of: 1.00054973. On its default settings, what it does is Scan X amount of RSI Lengths and checks for when the RSI and RSI MA cross each other. It then records the profitability of each cross to identify which length produced the overall highest crossing profitability. Whichever length produces the highest profit is then the RSI length that is used in the plots, until another length takes its place. This may result in what we deem to be the ‘Optimal RSI’ as it is an adaptive RSI which changes based on performance.
In our next example, we changed the ‘Optimal RSI Type’ from ‘All Crossings’ to ‘Extremity Crossings’. If you compare the last two examples to each other, you’ll notice some similarities, but overall they’re quite different. The reason why is, the Optimal RSI is calculated differently. When using ‘All Crossings’ everytime the RSI and RSI MA cross, we evaluate it for profit (short and long). However, with ‘Extremity Crossings’, we only evaluate it when the RSI crosses over the RSI MA and RSI <= 40 or RSI crosses under the RSI MA and RSI >= 60. We conclude the crossing when it crosses back on its opposite of the extremity, and that is how it finds its Optimal RSI.
The way we determine the Optimal RSI is crucial to calculating which length is currently optimal.
In this next example we have zoomed in a bit, and have the full default settings on. Now we have signals (which you can set alerts for), for when the RSI and RSI MA cross (green is bullish and red is bearish). We also have our Optimal RSI Bollinger Bands enabled here too. These bands allow you to see where there may be Support and Resistance within the RSI at levels that aren’t static; such as 30 and 70. The length the RSI Bollinger Bands use is the Optimal RSI Length, allowing it to likewise change in correlation to the Optimal RSI.
In the example above, we’ve zoomed out as far as the Optimal RSI Bollinger Bands go. You’ll notice, the Bollinger Bands may act as Support and Resistance locations within and outside of the RSI Mid zone (30-70). In the next example we will highlight these areas so they may be easier to see.
Circled above, you may see how many times the Optimal RSI faced Support and Resistance locations on the Bollinger Bands. These Bollinger Bands may give a second location for Support and Resistance. The key Support and Resistance may still be the 30/50/70, however the Bollinger Bands allows us to have a more adaptive, moving form of Support and Resistance. This helps to show where it may ‘bounce’ if it surpasses any of the static levels (30/50/70).
Due to the fact that this Indicator may take a long time to execute and it can throw errors for such, we have added a Setting called: Adjust Optimal RSI Lookback and RSI Count. This settings will automatically modify the Optimal RSI Lookback Length and the RSI Count based on the Time Frame you are on and the Bar Indexes that are within. For instance, if we switch to the 1 Hour Time Frame, it will adjust the length from 200->90 and RSI Count from 30->20. If this wasn’t adjusted, the Indicator would Timeout.
You may however, change the Setting ‘Adjust Optimal RSI Lookback and RSI Count’ to ‘Manual’ from ‘Auto’. This will give you control over the ‘Optimal RSI Lookback Length’ and ‘RSI Count’ within the Settings. Please note, it will likely take some “fine tuning” to find working settings without the Indicator timing out, but there are definitely times you can find better settings than our ‘Auto’ will create; especially on higher Time Frames. The Minimum our ‘Auto’ will create is:
Optimal RSI Lookback Length: 90
RSI Count: 20
The Maximum it will create is:
Optimal RSI Lookback Length: 200
RSI Count: 30
If there isn’t much bar index history, for instance, if you’re on the 1 Day and the pair is BTC/USDT you’ll get < 4000 Bar Indexes worth of data. For this reason it is possible to manually increase the settings to say:
Optimal RSI Lookback Length: 500
RSI Count: 50
But, please note, if you make it too high, it may also lead to inaccuracies.
We will conclude our Tutorial here, hopefully this has given you some insight as to how calculating our Optimal RSI and then using it within Machine Learning may create a more adaptive RSI.
Settings:
Optimal RSI:
Show Crossing Signals: Display signals where the RSI and RSI Cross.
Show Tables: Display Information Tables to show information like, Optimal RSI Length, Best Profit, New Optimal RSI Lookback Length and New RSI Count.
Show Bollinger Bands: Show RSI Bollinger Bands. These bands work like the TDI Indicator, except its length changes as it uses the current RSI Optimal Length.
Optimal RSI Type: This is how we calculate our Optimal RSI. Do we use all RSI and RSI MA Crossings or just when it crosses within the Extremities.
Adjust Optimal RSI Lookback and RSI Count: Auto means the script will automatically adjust the Optimal RSI Lookback Length and RSI Count based on the current Time Frame and Bar Index's on chart. This will attempt to stop the script from 'Taking too long to Execute'. Manual means you have full control of the Optimal RSI Lookback Length and RSI Count.
Optimal RSI Lookback Length: How far back are we looking to see which RSI length is optimal? Please note the more bars the lower this needs to be. For instance with BTC/USDT you can use 500 here on 1D but only 200 for 15 Minutes; otherwise it will timeout.
RSI Count: How many lengths are we checking? For instance, if our 'RSI Minimum Length' is 4 and this is 30, the valid RSI lengths we check is 4-34.
RSI Minimum Length: What is the RSI length we start our scans at? We are capped with RSI Count otherwise it will cause the Indicator to timeout, so we don't want to waste any processing power on irrelevant lengths.
RSI MA Length: What length are we using to calculate the optimal RSI cross' and likewise plot our RSI MA with?
Extremity Crossings RSI Backup Length: When there is no Optimal RSI (if using Extremity Crossings), which RSI should we use instead?
Machine Learning:
Use Rational Quadratics: Rationalizing our Close may be beneficial for usage within ML calculations.
Filter RSI and RSI MA: Should we filter the RSI's before usage in ML calculations? Essentially should we only use RSI data that are of the same type as our Optimal RSI? For instance if our Optimal RSI is Bullish (RSI > RSI MA), should we only use ML RSI's that are likewise bullish?
Machine Learning Type: Are we using a Simple ML Average, KNN Mean Average, KNN Exponential Average or None?
KNN Distance Type: We need to check if distance is within the KNN Min/Max distance, which distance checks are we using.
Machine Learning Length: How far back is our Machine Learning going to keep data for.
k-Nearest Neighbour (KNN) Length: How many k-Nearest Neighbours will we account for?
Fast ML Data Length: What is our Fast ML Length? This is used with our Slow Length to create our KNN Distance.
Slow ML Data Length: What is our Slow ML Length? This is used with our Fast Length to create our KNN Distance.
If you have any questions, comments, ideas or concerns please don't hesitate to contact us.
HAPPY TRADING!
Relative Daily Change% by SUMIT
"Relative Daily Change%" Indicator (RDC)
The "Relative Daily Change%" indicator compares a stock's average daily price change percentage over the last 200 days with a chosen index.
It plots a colored curve. If the stock's change% is higher than the index, the curve is green, indicating it's doing better. Red means the stock is under-performing.
This indicator is designed to compare the performance of a stock with specific index (as selected) for last 200 candles.
I use this during a breakout to see whether the stock is performing well with comparison to it`s index. As I marked in the chart there was a range zone (red box), we got a breakout with good volume and it is also sustaining above 50 and 200 EMA, the RDC color is also in green so as per my indicator it is performing well. This is how I do fine-tuning of my analysis for a breakout strategy.
You can select Index from the list available in input
**Line Color Green = Avg Change% per day of the stock is more than the Selected Index
**Line Color White = Avg Change% per day of the stock is less than the Selected Index
If you want details of stocks for all index you can ask for it.
Disclaimer : **This is for educational purpose only. It is not any kind of trade recommendation/tips.
Major and Minor Trend Indicator by Nikhil34aScript Description:
This script is designed to provide a visual indication of the major and minor trends of an asset, along with potential buy and sell signals. It calculates two Simple Moving Averages (SMA): a longer-term 200-period SMA (Major SMA) and a shorter-term 20-period SMA (Minor SMA). The script determines whether the asset's closing price is above or below these moving averages to identify the major and minor trends. It also detects potential buying and selling opportunities based on the intersection of the asset's price with the SMA lines.
Usefulness:
This script can be useful for traders and investors who follow trend-based strategies and want to monitor the major and minor trends of an asset. By visually displaying the trends and potential buy and sell signals, it helps traders make informed decisions about entering or exiting positions.
Simple Explanation on BTC Chart:
In the context of a BTC chart, let's consider the following scenario:
BTC is currently trading above the 200-period SMA (Simple Moving Average), which is located at 29,059.
BTC is trading below the 20-period SMA, positioned at 30,178.
The current price of BTC is 29,916.
Based on this information, we can conclude that:
The major trend is bullish since BTC is trading above the 200-period SMA.
The minor trend is bearish as BTC is trading below the 20-period SMA.
The intersection of the price with the moving averages indicates a potential selling opportunity.
Traders using this script would observe that BTC is in a bullish major trend, a bearish minor trend, and there is a possibility of a sell signal. They may consider these factors when making trading decisions, such as adjusting their positions or taking profits.
Remember to conduct your own analysis and consider additional factors before making any trading decisions.
SmartVPSGTitle: Identifying Volume Spikes, Price Movements and Gap Ups: A TradingView Script
Introduction:
In the world of trading, identifying volume spikes and price movements can provide valuable insights into market trends and potential trading opportunities. In this article, we'll explore a TradingView script that helps traders visualize volume spikes, price up moves with volume spikes, and gap-up days on their charts.
Detecting Price Up Moves:
The script starts by calculating price up moves. It compares the current day's closing price with the previous day's closing price and checks if it has increased by 3% or more. This helps traders spot significant upward price movements.
Detecting Volume Spurts:
Next, the script focuses on detecting volume spikes, which are often associated with increased market activity and potential trading opportunities. It compares the current day's volume with the highest volume of the previous nine sessions. If the current volume exceeds all the volumes of the previous nine sessions, it is considered a volume spurt.
Example:
Let's consider a hypothetical scenario where we have the following volume data for a stock:
Day 1: 100,000
Day 2: 80,000
Day 3: 120,000
Day 4: 150,000
Day 5: 200,000
Day 6: 90,000
Day 7: 110,000
Day 8: 130,000
Day 9: 140,000
Day 10: 250,000 (current day)
To determine if there is a volume spurt on Day 10, the script compares the current day's volume (250,000) with the highest volume of the previous nine sessions. In this case, the highest volume among the previous nine sessions is 200,000 (on Day 5). Since the current day's volume (250,000) exceeds the highest volume of the previous nine sessions (200,000), it is considered a volume spurt.
Identifying Gap-Up Days:
Gap-up days occur when the market opens significantly higher than the previous day's close. To identify these days, the script compares the current day's low price with the previous day's high price. If the low price is greater than the previous day's high, it is marked as a gap-up day.
Visualizing the Findings:
To provide a clear visual representation of the identified patterns, the script uses different shapes and colors. First, it plots small red dots above the candles whenever a volume spurt is detected. These dots help traders quickly identify periods of increased volume activity.
For price up moves with volume spikes, the script utilizes blue triangular shapes below the candles. This allows traders to pinpoint instances where both price and volume are showing positive signs, indicating potential bullish movements.
Additionally, the script incorporates green candles to represent gap-up days. These candles help traders recognize days when the market opens with a significant upward gap, suggesting a potential shift in market sentiment.
Conclusion:
The TradingView script discussed in this article provides traders with a visual representation of volume spikes , price up moves with volume spikes , and gap-up days . By incorporating these visual cues into their analysis, traders can gain valuable insights into market trends and potential trading opportunities.
Remember, this script should be used for educational and informational purposes only and does not serve as financial advice or recommendations. Traders are encouraged to customize and modify the script according to their specific trading strategies and risk tolerance.
Share this script with other traders on TradingView to enhance their chart analysis and trading decisions.
PS: This TradingView script is designed to work specifically on the daily timeframe (daily candles). It calculates and identifies volume spurts based on the volume data of the daily timeframe. Since it is designed for the daily timeframe, it may not produce accurate results or work as intended on other timeframes.
Bollinger Bands Lab - by InFinitoVariation of the Moving Average Lab that includes Bollinger Bands functionality for any manually created Moving Average. It includes:
- Standard Deviations for any MA
- Fixed Symmetrical Deviations for any MA that remain at a constant % away from the MA
- The same Moving Average creation settings from the Moving Average Lab
"The Moving Average Lab allows to create any possible combination of up to 3 given MAs. It is meant to help you find the perfect MA that fits your style, strategy and market type.
This script allows to average, weight, double and triple multiple types and lengths of Moving Averages
Currently supported MA types are:
SMA
EMA
VWMA
WMA
SMMA (RMA)
HMA
LSMA
DEMA
TEMA
Features:
- Double or Triple any type of Moving Average using the same logic used for calculating DEMAs and TEMAs
- Average 2 or 3 different types and lengths of Moving Average
- Weight each MA manually
- Average up to 3 personalized MAs
- Average different Moving Averages with different length each "
The preview screenshot shows:
- The combination of:
- 200 LSMA - Weight: 1
- 200 HMA - Weight: 2
- 200 VWMA - Weight: 1 - Double
- The regular Bollinger Band setting, 2 standard deviations
- Two fixed symmetrical deviations at 15% and 20% away from the XMA
David Varadi Intermediate OscillatorThe David Varadi Intermediate Oscillator (DVI) is a composite momentum oscillator designed to generate trading signals based on two key factors: the magnitude of returns over different time windows and the stretch, which measures the relative number of up versus down days. By combining these factors, the DVI aims to provide a reliable and objective assessment of market trends and momentum.
Methodology:
To calculate the DVI, a specific formula is applied. The magnitude component involves averaging smoothed returns over various lengths, weighted according to user-defined parameters. This calculation helps determine the magnitude of price changes. The stretch component follows a similar process, averaging smoothed returns over different lengths to gauge market momentum. Users have the flexibility to adjust the weights and lengths to suit their trading preferences and styles.
Utility:
The DVI offers versatility in its applications. It can be used for both momentum trading and trend analysis due to its smooth and consistent signals. Unlike some other oscillators, the DVI provides longer and uncorrelated signals, allowing traders to effectively combine trend-following and mean-reversion strategies. For example, the DVI is adept at identifying overbought levels above the 200-day moving average, serving as a useful tool for determining exit points during price strength and even potential shorting opportunities. Traders can develop simple trading systems based on the DVI, buying above the 200-day moving average and selling when the DVI exceeds a specified threshold. Conversely, they can consider short positions below the 200-day moving average and cover when the DVI falls below a specific threshold. The DVI's objective approach to analyzing market momentum makes it a valuable resource for traders seeking to identify trading opportunities.
Key Features:
Bar coloring: based on Trend, Extremeties or Reversions
Reversions: Potential reversal points marked with triangles above\below oscillator
Extremity Hues: Highlighting oxcillator reaching traditional OB\OS levels
Example Charts:
Scalping Strategy (5min)This indicator is designed for scalping strategies on a 5-minute timeframe. It generates signals based on two RSI crossovers and incorporates moving averages to identify trends. Additionally, a Bollinger Band is included to eliminate the need for an additional Bollinger Band on the chart.
Please note that this indicator does not guarantee 100% accurate signals and may produce false signals. It is recommended to use this indicator in conjunction with other indicators such as Stochastic, MACD, SuperTrend, or any other suitable indicators to enhance the accuracy of trading decisions.
1) Signal Generation: The indicator generates buy and sell signals based on two RSI crossovers. A buy signal is generated when the fast RSI crosses above the slow RSI, indicating potential bullish momentum. Conversely, a sell signal is generated when the fast RSI crosses below the slow RSI, suggesting potential bearish momentum.
2) To adjust the indicator to your specific chart and trading preferences, you have the flexibility to modify the RSI and moving average (MA) values. By changing the RSI values (slow RSI length and fast RSI length), you can fine-tune the sensitivity of the RSI crossovers to suit different timeframes and market conditions. Similarly, adjusting the MA values (slow MA period and fast MA period) allows you to adapt the indicator to the desired trend identification and short-term trend confirmation.
3) Pay attention to trades that are confirmed by the short-term moving average (MA) aligning with the desired direction. For buy signals, ensure that the short MA is tending upward, indicating a potential uptrend. For sell signals, confirm that the short MA is trending downward, suggesting a potential downtrend.
4) Moving Averages: The indicator uses a 200-period moving average (MA) to identify the overall trend and a short-term MA for additional confirmation.
5) Bollinger Band: The included Bollinger Band is not directly used in the indicator's calculations. However, it is provided for convenience so that users don't need to add another Bollinger Band to their chart separately.
6) Exercise caution when the short MA is below the 200-period MA but showing signs of attempting an upward move. These situations may indicate a potential reversal or consolidation, and it is advisable to avoid taking trades solely based on the 200-period MA crossover in such cases.
Remember that these guidelines are intended to provide additional insights and should be used in combination with your trading judgment and analysis.
Fetch ATR + MA StrategyA trend following indicator that allows traders/investors to enter trades for the long term, as it is mainly tested on the daily chart. The indicator fires off buy and sell signals. The sell signals can be turned off as trader can decide to use this indicator for long term buy signals. The buy signals are indicated by the green diamonds, and the red diamonds show the points on then chart where the asset can be sold.
The indicator uses a couple indicators in order to generate the buy signals:
- ADX
- ATR
- Moving Average of ATR
- 50 SMA
- 200 SMA
The buy signal is generated at the cross overs of the 50 and 200 SMA's while the ATR is lower than then Moving Average of the ATR. The buy signal is fired when these conditions are met and if the ADX is lower than 30.
The thought process is as follows:
When the ATR is lower than its moving average, the price should be in a low volatilty environment. An ADX between 25 and 50 signals a Strong trend. Every value below 25 is an absent or weak trend. So entering a trade when the volatilty is still low but increasing, you'll be entering a trade at the start of a new uptrend. This mechanism also filters out lots of false signals of the simple cross overs.
The sell signals are fired every time the 50 SMA drops below the 200 SMA.
ASR_Top/BottomThis is top and bottom finder indicator which is using RSI , Mavilimw(Kivanc's) , BB and WT
it is better use this indicator on Daily Weekly and 4H chart
There is 2 signal Buy and Sell , you can use different strategies such as ( when you see Buy signal you can buy 25% of your portfolio and when you see SELL signal sell 25% of portfolio )
rule is basic :
Buy - when RSI and WT is in overbought zone and Last bar touched upper BB line and backed inside it would triggered buy
SELL- when RSI and WT is in oversold zone and last bar touched lower band of BB and up inside it would be triggered sell
Strategies :
it is better use this indicator also with RSI divergence
let say you see BUY signal and after that you see rsi positive divergence it is confirmed that we are in bottom or when Sell signal happened and rsi negative divergence happened it confirms that we are in top
also you can use 200 ma , if you see sell in below 200 ma it is exit opportunity or if you see buy above 200 ma it is buy opportunity
I would appreciate your comments and opinions or experiences when you are using this indicator
EMA bridge and dashboard with color coding.
Summary:
This is a custom moving average indicator script that calculates and plots different Exponential Moving Averages (EMAs) based on user-defined input values. The script also displays MACD and RSI, and provides a table that displays the current trend of the market in a color-coded format.
Explanation:
- The script starts by defining the name of the indicator and the different inputs that the user can customize.
- The inputs include bridge values for three different EMAs (high, close, and low), and four other EMAs (5, 50, 100, and 200).
- The script assigns values to these inputs using the `ta.ema()` function.
- Additionally, the script calculates EMAs for higher timeframes (3m, 5m, 15m, and 30m).
- The script then plots the EMAs on the chart using different colors and line widths.
- The script defines conditions for going long or short based on the crossover of two EMAs.
- It plots triangles above or below bars to indicate the crossover events.
- The script also calculates and displays the RSI and MACD of the asset.
- Finally, the script creates a table that displays the current trend of the market in a color-coded format. The table can be positioned on the top, middle, or bottom of the chart and on the left, center, or right side of the chart.
Parameters:
- i_ema_h: Bridge value for high EMA (default=34)
- i_ema_c: Bridge value for close EMA (default=34)
- i_ema_l: Bridge value for low EMA (default=34)
- i_ema_5: Value for 5-period EMA (default=5)
- i_ema_50: Value for 50-period EMA (default=50)
- i_ema_100: Value for 100-period EMA (default=100)
- i_ema_200: Value for 200-period EMA (default=200)
- i_f_ema: Value for fast EMA used in MACD calculation (default=9)
- i_s_ema: Value for slow EMA used in MACD calculation (default=21)
- fastInput: Value for fast length used in MACD calculation (default=7)
- slowInput: Value for slow length used in MACD calculation (default=14)
- tableYposInput: Vertical position of the table (options: top, middle, bottom; default=middle)
- tableXposInput: Horizontal position of the table (options: left, center, right; default=right)
- bullColorInput: Color of the table cell for a bullish trend (default=green)
- bearColorInput: Color of the table cell for a bearish trend (default=red)
- neutColorInput: Color of the table cell for a neutral trend (default=white)
- neutColorLabelInput: Color of the label for neutral trend in the table (default=fuchsia)
Usage:
To use this script, simply copy and paste it into the Pine Editor on TradingView. You can then customize the input values to your liking or leave them at their default values. Once you have added the script to your chart, you can view the EMAs, MACD, RSI, and trend table on the chart. The trend table provides a quick way to assess the current trend of the market at a glance.
Lorentzian Classification Strategy Based in the model of Machine learning: Lorentzian Classification by @jdehorty, you will be able to get into trending moves and get interesting entries in the market with this strategy. I also put some new features for better backtesting results!
Backtesting context: 2022-07-19 to 2023-04-14 of US500 1H by PEPPERSTONE. Commissions: 0.03% for each entry, 0.03% for each exit. Risk per trade: 2.5% of the total account
For this strategy, 3 indicators are used:
Machine learning: Lorentzian Classification by @jdehorty
One Ema of 200 periods for identifying the trend
Supertrend indicator as a filter for some exits
Atr stop loss from Gatherio
Trade conditions:
For longs:
Close price is above 200 Ema
Lorentzian Classification indicates a buying signal
This gives us our long signal. Stop loss will be determined by atr stop loss (white point), break even(blue point) by a risk/reward ratio of 1:1 and take profit of 3:1 where half position will be closed. This will be showed as buy.
The other half will be closed when the model indicates a selling signal or Supertrend indicator gives a bearish signal. This will be showed as cl buy.
For shorts:
Close price is under 200 Ema
Lorentzian Classification indicates a selling signal
This gives us our short signal. Stop loss will be determined by atr stop loss (white point), break even(blue point) by a risk/reward ratio of 1:1 and take profit of 3:1 where half position will be closed. This will be showed as sell.
The other half will be closed when the model indicates a buying signal or Supertrend indicator gives a bullish signal. This will be showed as cl sell.
Risk management
To calculate the amount of the position you will use just a small percent of your initial capital for the strategy and you will use the atr stop loss or last swing for this.
Example: You have 1000 usd and you just want to risk 2,5% of your account, there is a buy signal at price of 4,000 usd. The stop loss price from atr stop loss or last swing is 3,900. You calculate the distance in percent between 4,000 and 3,900. In this case, that distance would be of 2.50%. Then, you calculate your position by this way: (initial or current capital * risk per trade of your account) / (stop loss distance).
Using these values on the formula: (1000*2,5%)/(2,5%) = 1000usd. It means, you have to use 1000 usd for risking 2.5% of your account.
We will use this risk management for applying compound interest.
> In settings, with position amount calculator, you can enter the amount in usd of your account and the amount in percentage for risking per trade of the account. You will see this value in green color in the upper left corner that shows the amount in usd to use for risking the specific percentage of your account.
> You can also choose a fixed amount, so you will have to activate fixed amount in risk management for trades and set the fixed amount for backtesting.
Script functions
Inside of settings, you will find some utilities for display atr stop loss, break evens, positions, signals, indicators, a table of some stats from backtesting, etc.
You will find the settings for risk management at the end of the script if you want to change something or trying new values for other assets for backtesting.
If you want to change the initial capital for backtest the strategy, go to properties, and also enter the commisions of your exchange and slippage for more realistic results.
In risk managment you can find an option called "Use leverage ?", activate this if you want to backtest using leverage, which means that in case of not having enough money for risking the % determined by you of your account using your initial capital, you will use leverage for using the enough amount for risking that % of your acount in a buy position. Otherwise, the amount will be limited by your initial/current capital
I also added a function for backtesting if you had added or withdrawn money frequently:
Adding money: You can choose how often you want to add money (Monthly, yearly, daily or weekly). Then a fixed amount of money and activate or deactivate this function
Withdraw money: You can choose if you want to withdraw a fixed amount or a percentage of earnings. Then you can choose a fixed amount of money, the period of time and activate or deactivate this function. Also, the percentage of earnings if you choosed this option.
Some other assets where strategy has worked
BTCUSD 4H, 1D
ETHUSD 4H, 1D
BNBUSD 4H
SPX 1D
BANKNIFTY 4H, 15 min
Some things to consider
USE UNDER YOUR OWN RISK. PAST RESULTS DO NOT REPRESENT THE FUTURE.
DEPENDING OF % ACCOUNT RISK PER TRADE, YOU COULD REQUIRE LEVERAGE FOR OPEN SOME POSITIONS, SO PLEASE, BE CAREFULL AND USE CORRECTLY THE RISK MANAGEMENT
Do not forget to change commissions and other parameters related with back testing results!. If you have problems loading the script reduce max bars back number in general settings
Strategies for trending markets use to have more looses than wins and it takes a long time to get profits, so do not forget to be patient and consistent !
Please, visit the post from @jdehorty called Machine Learning: Lorentzian Classification for a better understanding of his script!
Any support and boosts will be well received. If you have any question, do not doubt to ask!
God's Little FingerThe "God's Little Finger" indicator uses several technical analysis tools to provide information about the direction of the market and generate buy/sell signals. These tools include a 200-period exponential moving average (EMA), Moving Average Convergence Divergence (MACD), Bollinger Bands, and the Relative Strength Index (RSI).
EMA is used to determine if prices are trending. MACD measures the speed and momentum of the trend. Bollinger Bands are used to determine if prices are staying within a range and to measure the strength of the trend. RSI shows overbought/oversold levels and can be used to determine if the trend will continue.
The indicator generates buy/sell signals based on market conditions. A buy signal is generated when the MACD line is below zero, the price is below the lower boundary of the Bollinger Bands, the price is above the 200-period EMA, and the RSI is in oversold levels (usually below 40). A sell signal is generated when the MACD line is above zero, the price is above the upper boundary of the Bollinger Bands, the price is below the 200-period EMA, and the RSI is in overbought levels (usually above 60).
However, it should be noted that indicators can be used to predict market conditions, but they do not guarantee results and any changes or unexpected events in the market can affect predictions. Therefore, they should always be used in conjunction with other analysis methods and risk management strategies.
Mean Reversion and TrendfollowingTitle: Mean Reversion and Trendfollowing
Introduction:
This script presents a hybrid trading strategy that combines mean reversion and trend following techniques. The strategy aims to capitalize on short-term price corrections during a downtrend (mean reversion) as well as ride the momentum of a trending market (trend following). It uses a 200-period Simple Moving Average (SMA) and a 2-period Relative Strength Index (RSI) to generate buy and sell signals.
Key Features:
Combines mean reversion and trend following techniques
Utilizes 200-period SMA and 2-period RSI
Customizable starting date
Allows for enabling/disabling mean reversion or trend following modes
Adjustable position sizing for trend following and mean reversion
Script Description:
The script implements a trading strategy that combines mean reversion and trend following techniques. Users can enable or disable either of these techniques through the input options. The strategy uses a 200-period Simple Moving Average (SMA) and a 2-period Relative Strength Index (RSI) to generate buy and sell signals.
The mean reversion mode is active when the price is below the SMA200, while the trend following mode is active when the price is above the SMA200. The script generates buy signals when the RSI is below 20 (oversold) in mean reversion mode or when the price is above the SMA200 in trend following mode. The script generates sell signals when the RSI is above 80 (overbought) in mean reversion mode or when the price falls below 95% of the SMA200 in trend following mode.
Users can adjust the position sizing for both trend following and mean reversion modes using the input options.
To use this script on TradingView, follow these steps:
Open TradingView and load your preferred chart.
Click on the 'Pine Editor' tab located at the bottom of the screen.
Paste the provided script into the Pine Editor.
Click 'Add to Chart' to apply the strategy to your chart.
Please note that the past performance of any trading system or methodology is not necessarily indicative of future results. Always use proper risk management and consult a financial advisor before making any investment decisions.
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The following is a summary of the underlying whitepaper (onlinelibrary.wiley.com) for this strategy:
This paper proposes a theory of securities market under- and overreactions based on two psychological biases: investor overconfidence about the precision of private information and biased self-attribution, which causes asymmetric shifts in investors' confidence as a function of their investment outcomes. The authors show that overconfidence implies negative long-lag autocorrelations, excess volatility, and public-event-based return predictability. Biased self-attribution adds positive short-lag autocorrelations (momentum), short-run earnings "drift," and negative correlation between future returns and long-term past stock market and accounting performance.
The paper explains that there is empirical evidence challenging the traditional view that securities are rationally priced to reflect all publicly available information. Some of these anomalies include event-based return predictability, short-term momentum, long-term reversal, high volatility of asset prices relative to fundamentals, and short-run post-earnings announcement stock price "drift."
The authors argue that investor overconfidence can lead to stock prices overreacting to private information signals and underreacting to public signals. This overreaction-correction pattern is consistent with long-run negative autocorrelation in stock returns, excess volatility, and further implications for volatility conditional on the type of signal. The market's tendency to over- or underreact to different types of information allows the authors to address the pattern that average announcement date returns in virtually all event studies are of the same sign as the average post-event abnormal returns.
Biased self-attribution implies short-run momentum and long-term reversals in security prices. The dynamic analysis based on biased self-attribution can also lead to a lag-dependent response to corporate events. Cash flow or earnings surprises at first tend to reinforce confidence, causing a same-direction average stock price trend. Later reversal of overreaction can lead to an opposing stock price trend.
The paper concludes by summarizing the findings, relating the analysis to the literature on exogenous noise trading, and discussing issues related to the survival of overconfident traders in financial markets.
Strategy Myth-Busting #13 - MultiEMA+BXTrender - [SP/MYN]#13 on the Myth-Busting bench, we are automating the "I Found The Highest Win Rate 15 Minute Scalping Trading Strategy Ever" strategy from "TradeIQ" who claims to have backtested this manually and achieved 410% profit over 100 trades within 6 months on Natural Gas with 79 Wins / 21 Losses with an astounding 3.96% Max Drawdown.
It was quite challenging emulating the same subjective EMA pullback logic along with the dependent sequencing of events necessary to enter a trade and we might improve on this to make it better in the future. Super kudos to @spdoinkal who helped with this strategy. If you have ideas on how this could be improved on, would love to hear about them.
As is, we were unable to substantiate similar results to what was manually backtested by TradeIQ, we do however see potential here. Given some optimizations and improvements to the the entry logic accommodating for a wider more variable margin after pullbacks reestablish above/below the fast EMA we think the performance of this strategy could certainly be improved upon. So not sure if we have totally myth busted this completely at this point in time.
This strategy uses a combination of 2 open-source public indicators:
3 EMA's (Trading View Internal)
B-Xtrender by Puppytherapy
Three separate (21), (89) and (200) EMA's are used as a means to confirm and keep entry out of ranged markets. When the 3 EMA's are all clumped up together with no distance it's indicative of a flat or ranged market. This is then used in conjunction with B-XTrender as a means to detect the trend direction. B-XTrender which is a trend following indicator originally published in the IFTA Journal by Bharat Jhunjhunwala. It uses both a short and long term lengths along with a compound EMA used as a means to smooth and sample trend direction.
Trading Rules
15 min candles but other lower time-frames
Stop Loss on previous swing high/low
No Take Profit, Exit on new red/green circles from BX-Trender
Long
EMA Green (21) on top, White (89)in middle and red (200) on bottom and there is distance between EMA's need to be spaced, otherwise in a ranged market
Price action must pull back into 89 EMA (White line) either close or touching it.
Once pullback occurs wait for BX Trender to issue a new green circle and BX Trend line must be green and above 0
Price action must also pull up back above the (Green Line) EMA 21
Short
EMA Red (200) on top, White (89) in middle and Green (21) on bottom and there is distance between EMA's need to be spaced, otherwise in a ranged market
Price action must pull back into 89 EMA (White line) either close or touching it.
Once pullback occurs wait for BX Trender to issue a new red circle and BX Trend line must be red and below 0
Price action must also pull up back below the (green Line) EMA 21
If you know of or have a strategy you want to see myth-busted or just have an idea for one, please feel free to message me.
