Ido strategy RSI Oversold with MACD Buy Signal Indicator
This indicator combines the Relative Strength Index (RSI) and the Moving Average Convergence Divergence (MACD) to help identify potential buy signals based on oversold conditions and trend reversals. This script is designed for traders looking to identify entry points when an asset is likely undervalued (oversold) and showing bullish momentum.
How It Works
RSI Oversold Detection: The RSI measures the speed and change of price movements. This indicator flags when the RSI falls below 30, signaling that the asset may be oversold. The user can customize the RSI lookback period and the timeframe within which oversold conditions are considered relevant.
MACD Crossover: The MACD line crossing above the Signal line often indicates a shift to bullish momentum. In this script, a buy signal is generated when a MACD bullish crossover occurs after an RSI oversold condition has been met within a user-defined lookback window.
Buy Signal: A green triangle appears below the price chart each time both conditions are met—when the RSI has recently been in oversold territory and the MACD line crosses above the Signal line. This signal suggests that the asset may be positioned for a potential upward trend, providing a visual cue for entry points.
Customizable Settings
RSI Settings: Adjust the RSI source and period length.
MACD Settings: Customize the fast, slow, and signal lengths of the MACD to suit different market conditions.
Lookback Period: Define how many bars back to check for an RSI oversold condition before confirming a MACD crossover.
Visual Elements
Oversold Background Color: The background on the price chart is shaded red whenever the RSI is below 30.
Buy Signal: A green triangle is displayed on the chart to indicate a potential entry point when both conditions are met.
Alerts
This indicator includes optional alerts, allowing traders to receive notifications whenever the conditions for a buy signal are met, making it easier to monitor multiple assets and stay informed of trading opportunities.
This indicator is ideal for traders using a combination of momentum and trend reversal strategies, especially in volatile markets where oversold conditions often precede a trend change.
Educational
Cumulative Volume Delta Custom AlertDescription
This script calculates and visualizes the Cumulative Volume Delta (CVD) on multiple timeframes, enabling traders to monitor volume-based price action dynamics. The CVD is calculated based on up and down volume approximations and displayed as a candle plot, with color-coded alerts when significant changes occur.
Key Features:
Multi-Timeframe Analysis: The script uses a customizable anchor period and a lower timeframe for scanning, allowing it to capture more granular volume movements.
Volume-Based Trend Detection: Plots CVD candles with color indicators (teal for increasing volume delta, red for decreasing), helping traders to visually track volume trends.
Dynamic Alerts for Volume Shifts:
Triggers an alert when there is a significant (over 25%) change in CVD between consecutive periods.
The alert marker color adapts based on the current CVD value:
Blue when the current CVD is positive.
Yellow when the current CVD is negative.
Markers are placed above bars for volume increases and below for volume decreases, simplifying visual analysis.
Customizable Background Highlight: Adds a background highlight to emphasize significant CVD changes.
Use Cases:
Momentum Detection: Traders can use alerts on large volume delta changes to identify potential trend reversals or continuation points.
Volume-Driven Analysis: CVD helps distinguish buy and sell pressure across different timeframes, ideal for volume-based strategies.
How to Use
Add the script to your TradingView chart.
Configure the anchor and lower timeframes in the input settings.
Set up alerts to receive notifications when a 25% change in CVD occurs, with color-coded markers for easy identification.
Silen's EMA AreasAre you tired of reading candles? 🧨 Do you want to bring more meaning to your chart? 🧹
Then this is the script for you!
This script does:
- Add several meaningfully pre-configured EMA lines to your chart - up to EMA 300
- Colors the areas between EMA lines in 3d colors - green and red
- The Smaller the EMA, the firmer the color
- Highlights the EMA 300 in a golden color
What is the meaning of this?
Let me introduce a new word to you: EMA FOLDING .
Yes, you heard right. With this indicator you can see in 3D how EMA lines are folding above and below each other, indicating severe mood swings in the chart.
This helps you keep track of what your instrument is actually doing while it enables you to cancel out the noise and messyness of ordinary candles which can be quite random and hard to read.
Once an EMA is fully positive or negatively folded (all ema lines are green and above each other from largest EMA to smallest EMA and vice versa for negatively folded) you can be sure that you are in a Trend or certain mood (for higher timeframes, from 15mins on).
I don't ever want to read any chart without having this indicator on. Whenever I present charts to anybody I use this indicator - and the feedback is insanely positive. People tend to read and understand charts much better with this indicator than just staring at candles.
Why is this indicator different to other EMA indicators and should thereby not be deleted by the TradingView Team due to redundance with other EMA indicators?
- This is not a simple indicator for EMAs
- Rather, this is an indicator to better and easier read the whole chart
- You can detect mood swings very easily which is very hard to do with a normal EMA indicator
- I haven't found any EMA indicator on TradingView that does this job so i sincerely believe it is extremely unique
- I sincerely believe it can help people get a much better understanding of charts without actualy getting into details of EMA's or even needing to know what an EMA is.
This indicator isn't intended for trading purposes, rather it is intended to give you a better and easier understanding of the chart. Of course - you can also use it for your trading but like I said, that is not the primary intended purpose.
This indicator comes pre-configured with quite optimal values (in my opinion) but of course can be fully customized. 🧮
Test it for yourself!
Dual Momentum StrategyThis Pine Script™ strategy implements the "Dual Momentum" approach developed by Gary Antonacci, as presented in his book Dual Momentum Investing: An Innovative Strategy for Higher Returns with Lower Risk (McGraw Hill Professional, 2014). Dual momentum investing combines relative momentum and absolute momentum to maximize returns while minimizing risk. Relative momentum involves selecting the asset with the highest recent performance between two options (a risky asset and a safe asset), while absolute momentum considers whether the chosen asset has a positive return over a specified lookback period.
In this strategy:
Risky Asset (SPY): Represents a stock index fund, typically more volatile but with higher potential returns.
Safe Asset (TLT): Represents a bond index fund, which generally has lower volatility and acts as a hedge during market downturns.
Monthly Momentum Calculation: The momentum for each asset is calculated based on its price change over the last 12 months. Only assets with a positive momentum (absolute momentum) are considered for investment.
Decision Rules:
Invest in the risky asset if its momentum is positive and greater than that of the safe asset.
If the risky asset’s momentum is negative or lower than the safe asset's, the strategy shifts the allocation to the safe asset.
Scientific Reference
Antonacci's work on dual momentum investing has shown the strategy's ability to outperform traditional buy-and-hold methods while reducing downside risk. This approach has been reviewed and discussed in both academic and investment publications, highlighting its strong risk-adjusted returns (Antonacci, 2014).
Reference: Antonacci, G. (2014). Dual Momentum Investing: An Innovative Strategy for Higher Returns with Lower Risk. McGraw Hill Professional.
XRP Comparative RSI Indicator - Final VersionXRP Comparative RSI Indicator - Final Version
The XRP Comparative RSI Indicator offers a dynamic analysis of XRP’s market positioning through relative strength index (RSI) comparisons across various cryptocurrencies and major market indicators. This indicator allows traders and analysts to gauge XRP’s momentum and potential turning points within different market conditions.
Key Features:
• Normalized RSIs: Each RSI value is normalized between 0.00 and 1.00, allowing seamless comparison across multiple assets.
• Grouped Analysis: Three RSI groups provide specific insights:
• Group 1 (XRP-Specific): Measures XRPUSD, XRP Dominance (XRP.D), and XRP/BTC, focusing on XRP’s performance across different trading pairs.
• Group 2 (Market Influence - Bitcoin): Measures BTCUSD, BTC Dominance (BTC.D), and XRP/BTC, capturing the influence of Bitcoin on XRP.
• Group 3 (Liquidity Impact): Measures USDT Dominance (USDT.D), BTCUSD, and ETHUSD, evaluating the liquidity impact from key assets and stablecoins.
• Individual Asset RSIs: Track the normalized RSI for each specific pair or asset, including XRPUSD, BTCUSD, ETHUSD, XRP/BTC, BTC Dominance, ETH Dominance, and the S&P 500.
• Clear Color Coding: Each asset’s RSI is plotted with a unique color scheme, consistent with the first indicator, for easy recognition.
This indicator is ideal for identifying relative strengths, potential entry and exit signals, and understanding how XRP’s momentum aligns or diverges from broader market trends.
Previous Day High/Low ±0.5%The simple script was written for the educational purposes, to check if the simple system can help to hedge your strategic portfolio. Mainly works with Indexes (tested on IRUS). You can optimize strategy by changing the % in the pine code. Working mainly on D timeframe.
Current script gives you the lines on the graph, you should check if the current day close price is above the high line - buy, if below - close your buy, or reverce your position to sell, if you go in short.
XRP Comparative Price Action Indicator - Final VersionXRP Comparative Price Action Indicator - Final Version
The XRP Comparative Price Action Indicator provides a comprehensive visual analysis of XRP’s price movements relative to key cryptocurrencies and market indices. This indicator normalises price data across various assets, allowing traders and investors to assess XRP’s performance against its peers and major market influences at a glance.
Key Features:
• Normalised Price Data: Prices are scaled between 0.00 and 1.00,
enabling straightforward comparisons between different assets.
• Key Comparisons: Includes normalised prices for:
• XRP/USD (Bitstamp)
• XRP Dominance (CryptoCap)
• XRP/BTC (Bitstamp)
• BTC/USD (Bitstamp)
• BTC Dominance (CryptoCap)
• USDT Dominance (CryptoCap)
• S&P 500 (SPY)
• DXY (Dollar Index)
• ETH/USD (Bitstamp)
• ETH Dominance (CryptoCap)
• XRP/ETH (Binance)
• Visual Clarity: Each asset is plotted with distinct colors for easy identification,
with thicker lines enhancing visibility on the chart.
• Reference Lines: Optional horizontal lines indicate the minimum (0) and maximum (1) normalised values, providing clear reference points for analysis.
This indicator is ideal for traders looking to understand XRP’s relative performance, gauge market sentiment, and make informed trading decisions based on comparative price action.
Gradient color Candlesthis is a simple candle colouring script that sets the colour of the candles to a gradient and the length of the gradient can be set by a user defined number of bars
Trade 1 + StatergyThe Relative Strength Index (RSI) is a momentum oscillator used in technical analysis that measures the speed and change of price movements of a security within a range of 0 to 100. It is most commonly set to a 14-period timeframe and helps traders identify overbought or oversold conditions, suggesting potential reversal points in the market. Divergence occurs when the price trend and the RSI trend move in opposite directions. A bullish divergence signals potential upward movement when prices are making new lows while the RSI makes higher lows. Conversely, a bearish divergence suggests a possible downward trend when prices are making new highs but the RSI is making lower highs. These signals are crucial for traders looking to capture shifts in momentum and adjust their trading strategies accordingly.
use full to
5 min
10 min
15 min decition
SMA- Ashish SinghSMA
This script implements a Simple Moving Average (SMA) crossover strategy using three SMAs: 200-day, 50-day, and 20-day, with buy and sell signals triggered based on specific conditions involving these moving averages. The indicator is overlaid on the price chart, providing visual cues for potential buy and sell opportunities based on moving average crossovers.
Key Features:
Moving Averages:
The 200-day, 50-day, and 20-day SMAs are calculated and plotted on the price chart. These are key levels that traders use to assess trends.
The 200-day SMA represents the long-term trend, the 50-day SMA is used for medium-term trends, and the 20-day SMA is for short-term analysis.
Buy Signal:
A buy signal is triggered when the price is below all three moving averages (200 SMA, 50 SMA, 20 SMA) and the SMAs are in a specific downward trend (200 SMA > 50 SMA > 20 SMA). This is an indication of a potential upward reversal.
The buy signal is marked with a green triangle below the price bar.
Sell Signal:
A sell signal is triggered when the price is above all three moving averages and the SMAs are in a specific upward trend (200 SMA < 50 SMA < 20 SMA). This signals a potential downward reversal.
The sell signal is marked with a red triangle above the price bar.
Trade Information:
After a buy signal, the buy price, bar index, and timestamp are recorded. When a sell signal occurs, the percentage gain or loss is calculated along with the number of days between the buy and sell signals.
The script automatically displays a label on the chart showing the gain or loss percentage along with the number of days the trade lasted. Green labels represent gains, and red labels represent losses.
User-friendly Visuals:
The buy and sell signals are plotted as small triangles directly on the chart for easy identification.
Detailed trade information is provided with well-formatted labels to highlight the profit or loss after each trade.
How It Works:
This strategy helps traders to identify trend reversals by leveraging long-term and short-term moving averages.
A single buy or sell signal is triggered based on price movement relative to the SMAs and their order.
The tool is designed to help traders quickly spot buying and selling opportunities with clear visual indicators and gain/loss metrics.
This indicator is ideal for traders looking to implement a systematic SMA-based strategy with well-defined buy/sell points and automatic performance tracking for each trade.
Disclaimer: The information provided here is for educational and informational purposes only. It is not intended as financial advice or as a recommendation to buy or sell any stocks. Please conduct your own research or consult a financial advisor before making any investment decisions. ProfitLens does not guarantee the accuracy, completeness, or reliability of any information presented.
S&P 100 Option Expiration Week StrategyThe Option Expiration Week Strategy aims to capitalize on increased volatility and trading volume that often occur during the week leading up to the expiration of options on stocks in the S&P 100 index. This period, known as the option expiration week, culminates on the third Friday of each month when stock options typically expire in the U.S. During this week, investors in this strategy take a long position in S&P 100 stocks or an equivalent ETF from the Monday preceding the third Friday, holding until Friday. The strategy capitalizes on potential upward price pressures caused by increased option-related trading activity, rebalancing, and hedging practices.
The phenomenon leveraged by this strategy is well-documented in finance literature. Studies demonstrate that options expiration dates have a significant impact on stock returns, trading volume, and volatility. This effect is driven by various market dynamics, including portfolio rebalancing, delta hedging by option market makers, and the unwinding of positions by institutional investors (Stoll & Whaley, 1987; Ni, Pearson, & Poteshman, 2005). These market activities intensify near option expiration, causing price adjustments that may create short-term profitable opportunities for those aware of these patterns (Roll, Schwartz, & Subrahmanyam, 2009).
The paper by Johnson and So (2013), Returns and Option Activity over the Option-Expiration Week for S&P 100 Stocks, provides empirical evidence supporting this strategy. The study analyzes the impact of option expiration on S&P 100 stocks, showing that these stocks tend to exhibit abnormal returns and increased volume during the expiration week. The authors attribute these patterns to intensified option trading activity, where demand for hedging and arbitrage around options expiration causes temporary price adjustments.
Scientific Explanation
Research has found that option expiration weeks are marked by predictable increases in stock returns and volatility, largely due to the role of options market makers and institutional investors. Option market makers often use delta hedging to manage exposure, which requires frequent buying or selling of the underlying stock to maintain a hedged position. As expiration approaches, their activity can amplify price fluctuations. Additionally, institutional investors often roll over or unwind positions during expiration weeks, creating further demand for underlying stocks (Stoll & Whaley, 1987). This increased demand around expiration week typically leads to temporary stock price increases, offering profitable opportunities for short-term strategies.
Key Research and Bibliography
Johnson, T. C., & So, E. C. (2013). Returns and Option Activity over the Option-Expiration Week for S&P 100 Stocks. Journal of Banking and Finance, 37(11), 4226-4240.
This study specifically examines the S&P 100 stocks and demonstrates that option expiration weeks are associated with abnormal returns and trading volume due to increased activity in the options market.
Stoll, H. R., & Whaley, R. E. (1987). Program Trading and Expiration-Day Effects. Financial Analysts Journal, 43(2), 16-28.
Stoll and Whaley analyze how program trading and portfolio insurance strategies around expiration days impact stock prices, leading to temporary volatility and increased trading volume.
Ni, S. X., Pearson, N. D., & Poteshman, A. M. (2005). Stock Price Clustering on Option Expiration Dates. Journal of Financial Economics, 78(1), 49-87.
This paper investigates how option expiration dates affect stock price clustering and volume, driven by delta hedging and other option-related trading activities.
Roll, R., Schwartz, E., & Subrahmanyam, A. (2009). Options Trading Activity and Firm Valuation. Journal of Financial Markets, 12(3), 519-534.
The authors explore how options trading activity influences firm valuation, finding that higher options volume around expiration dates can lead to temporary price movements in underlying stocks.
Cao, C., & Wei, J. (2010). Option Market Liquidity and Stock Return Volatility. Journal of Financial and Quantitative Analysis, 45(2), 481-507.
This study examines the relationship between options market liquidity and stock return volatility, finding that increased liquidity needs during expiration weeks can heighten volatility, impacting stock returns.
Summary
The Option Expiration Week Strategy utilizes well-researched financial market phenomena related to option expiration. By positioning long in S&P 100 stocks or ETFs during this period, traders can potentially capture abnormal returns driven by option market dynamics. The literature suggests that options-related activities—such as delta hedging, position rollovers, and portfolio adjustments—intensify demand for underlying assets, creating short-term profit opportunities around these key dates.
Payday Anomaly StrategyThe "Payday Effect" refers to a predictable anomaly in financial markets where stock returns exhibit significant fluctuations around specific pay periods. Typically, these are associated with the beginning, middle, or end of the month when many investors receive wages and salaries. This influx of funds, often directed automatically into retirement accounts or investment portfolios (such as 401(k) plans in the United States), temporarily increases the demand for equities. This phenomenon has been linked to a cycle where stock prices rise disproportionately on and around payday periods due to increased buy-side liquidity.
Academic research on the payday effect suggests that this pattern is tied to systematic cash flows into financial markets, primarily driven by employee retirement and savings plans. The regularity of these cash infusions creates a calendar-based pattern that can be exploited in trading strategies. Studies show that returns on days around typical payroll dates tend to be above average, and this pattern remains observable across various time periods and regions.
The rationale behind the payday effect is rooted in the behavioral tendencies of investors, specifically the automatic reinvestment mechanisms used in retirement funds, which align with monthly or semi-monthly salary payments. This regular injection of funds can cause market microstructure effects where stock prices temporarily increase, only to stabilize or reverse after the funds have been invested. Consequently, the payday effect provides traders with a potentially profitable opportunity by predicting these inflows.
Scientific Bibliography on the Payday Effect
Ma, A., & Pratt, W. R. (2017). Payday Anomaly: The Market Impact of Semi-Monthly Pay Periods. Social Science Research Network (SSRN).
This study provides a comprehensive analysis of the payday effect, exploring how returns tend to peak around payroll periods due to semi-monthly cash flows. The paper discusses how systematic inflows impact returns, leading to predictable stock performance patterns on specific days of the month.
Lakonishok, J., & Smidt, S. (1988). Are Seasonal Anomalies Real? A Ninety-Year Perspective. The Review of Financial Studies, 1(4), 403-425.
This foundational study explores calendar anomalies, including the payday effect. By examining data over nearly a century, the authors establish a framework for understanding seasonal and monthly patterns in stock returns, which provides historical support for the payday effect.
Owen, S., & Rabinovitch, R. (1983). On the Predictability of Common Stock Returns: A Step Beyond the Random Walk Hypothesis. Journal of Business Finance & Accounting, 10(3), 379-396.
This paper investigates predictability in stock returns beyond random fluctuations. It considers payday effects among various calendar anomalies, arguing that certain dates yield predictable returns due to regular cash inflows.
Loughran, T., & Schultz, P. (2005). Liquidity: Urban versus Rural Firms. Journal of Financial Economics, 78(2), 341-374.
While primarily focused on liquidity, this study provides insight into how cash flows, such as those from semi-monthly paychecks, influence liquidity levels and consequently impact stock prices around predictable pay dates.
Ariel, R. A. (1990). High Stock Returns Before Holidays: Existence and Evidence on Possible Causes. The Journal of Finance, 45(5), 1611-1626.
Ariel’s work highlights stock return patterns tied to certain dates, including paydays. Although the study focuses on pre-holiday returns, it suggests broader implications of predictable investment timing, reinforcing the calendar-based effects seen with payday anomalies.
Summary
Research on the payday effect highlights a repeating pattern in stock market returns driven by scheduled payroll investments. This cyclical increase in stock demand aligns with behavioral finance insights and market microstructure theories, offering a valuable basis for trading strategies focused on the beginning, middle, and end of each month.
Pine Execution MapPine Script Execution Map
Overview:
This is an educational script for Pine Script developers. The script includes data structure, functions/methods, and process to capture and print Pine Script execution map of functions called while pine script execution.
Map of execution is produced for last/latest candle execution.
The script also has example code to call execution map methods and generate Pine Execution map.
Use cases:
Pine script developers can get view of how the functions are called
This can also be used while debugging the code and know which functions are called vs what developer expect code to do
One can use this while using any of the open source published script and understand how public script is organized and how functions of the script are called.
Code components:
User defined type
type EMAP
string group
string sub_group
int level
array emap = array.new()
method called internally by other methods to generate level of function being executed
method id(string tag) =>
if(str.startswith(tag, "MAIN"))
exe_level.set(0, 0)
else if(str.startswith(tag, "END"))
exe_level.set(0, exe_level.get(0) - 1)
else
exe_level.set(0, exe_level.get(0) + 1)
exe_level.get(0)
Method called from main/global scope to record execution of main scope code. There should be only one call to this method at the start of global scope.
method main(string tag) =>
this = EMAP.new()
this.group := "MAIN"
this.sub_group := tag
this.level := "MAIN".id()
emap.push(this)
Method called from main/global scope to record end of execution of main scope code. There should be only one call to this method at the end of global scope.
method end_main(string tag) =>
this = EMAP.new()
this.group := "END_MAIN"
this.sub_group := tag
this.level := 0
emap.push(this)
Method called from start of each function to record execution of function code
method call(string tag) =>
this = EMAP.new()
this.group := "SUB"
this.sub_group := tag
this.level := "SUB".id()
emap.push(this)
Method called from end of each function to record end of execution of function code
method end_call(string tag) =>
this = EMAP.new()
this.group := "END_SUB"
this.sub_group := tag
this.level := "END_SUB".id()
emap.push(this)
Pine code which generates execution map and show it as a label tooltip.
if(barstate.islast)
for rec in emap
if(not str.startswith(rec.group, "END"))
lvl_tab = str.repeat("", rec.level+1, "\t")
txt = str.format("=> {0} {1}> {2}", lvl_tab, rec.level, rec.sub_group)
debug.log(txt)
debug.lastr()
Snapshot 1:
This is the output of the script and can be viewed by hovering mouse pointer over the blue color diamond shaped label
Snapshot 2:
How to read the Pine execution map
Customizable BTC Seasonality StrategyThis strategy leverages intraday seasonality effects in Bitcoin, specifically targeting hours of statistically significant returns during periods when traditional financial markets are closed. Padysak and Vojtko (2022) demonstrate that Bitcoin exhibits higher-than-average returns from 21:00 UTC to 23:00 UTC, a period in which all major global exchanges, such as the New York Stock Exchange (NYSE), Tokyo Stock Exchange, and London Stock Exchange, are closed. The absence of competing trading activity from traditional markets during these hours appears to contribute to these statistically significant returns.
The strategy proceeds as follows:
Entry Time: A long position in Bitcoin is opened at a user-specified time, which defaults to 21:00 UTC, aligning with the beginning of the identified high-return window.
Holding Period: The position is held for two hours, capturing the positive returns typically observed during this period.
Exit Time: The position is closed at a user-defined time, defaulting to 23:00 UTC, allowing the strategy to exit as the favorable period concludes.
This simple seasonality strategy aims to achieve a 33% annualized return with a notably reduced volatility of 20.93% and maximum drawdown of -22.45%. The results suggest that investing only during these high-return hours is more stable and less risky than a passive holding strategy (Padysak & Vojtko, 2022).
References
Padysak, M., & Vojtko, R. (2022). Seasonality, Trend-following, and Mean reversion in Bitcoin.
Immediate Rebalance ICT [TradingFinder] No Imbalances - MTF Gaps🔵 Introduction
The concept of "Immediate Rebalance" in technical analysis is a powerful and advanced strategy within the ICT (Inner Circle Trader) framework, widely used to identify key market levels.
Unlike the "Fair Value Gap," which leaves a price gap requiring a retracement for a fill, an Immediate Rebalance fills the gap immediately, representing an instant balance that strengthens the prevailing market trend. This structure allows traders to quickly spot critical price zones, capitalizing on strong trend continuations without the need for price retracement.
The "Immediate Rebalance ICT" indicator leverages this concept, providing traders with automated identification of critical supply and demand zones, order blocks, liquidity voids, and key buy-side and sell-side liquidity levels.
Through features like crucial liquidity points and immediate rebalancing areas, this tool enables traders to perform precise real-time market analysis and seize profitable opportunities.
🔵 How to Use
The Immediate Rebalance indicator assists traders in identifying reliable trading signals by detecting and analyzing Immediate Rebalance zones. By focusing on supply and demand areas, the indicator pinpoints optimal entry and exit positions.
Here’s how to use the indicator in both bearish (Supply Immediate Rebalance) and bullish (Demand Immediate Rebalance) structures :
🟣 Bullish Structure (Demand Immediate Rebalance)
In a bullish scenario, the indicator detects a Demand Immediate Rebalance formed by two consecutive bullish candles with overlapping wicks. This structure signifies an immediate demand zone, where price instantly balances within the zone, reducing the likelihood of a revisit and indicating potential upside momentum.
Zone Identification : Look for two consecutive bullish candles with overlapping wicks, forming a demand zone. This structure, due to its rapid balance, usually does not require a revisit and supports further upward movement.
Entry and Exit Levels : If price revisits this zone, percentage markers, particularly 50% and 75%, act as supportive levels, creating ideal entry points for long positions.
Example : In the second image, an example of a Demand Immediate Rebalance is shown, where overlapping bullish candle shadows indicate immediate balance, supporting the continuation of the bullish trend.
🟣 Bearish Structure (Supply Immediate Rebalance)
In a bearish setup, the indicator identifies a Supply Immediate Rebalance when two consecutive bearish candles with overlapping wicks appear. This formation signals an immediate supply zone, suggesting a high probability of trend continuation to the downside, with minimal expectation for price to retrace back to this area.
Zone Identificatio n: Look for two consecutive bearish candles with overlapping shadows. This structure forms a supply area where price is expected to continue its downtrend without revisiting the zone.
Entry and Exit Level s: Should price revisit this zone, percentage-based levels (e.g., 50% and 75%) serve as potential resistance points, optimizing entry for short positions, especially if the downtrend is expected to persist.
Example : The attached chart illustrates a Supply Immediate Rebalance, where overlapping candle shadows define this area, reassuring traders of a continued downward trend with a low likelihood of price returning to this zone.
🔵 Settings
ImmR Filter : This filter allows users to adjust the detection of Immediate Rebalance zones in four modes, from "Very Aggressive" to "Very Defensive," based on zone width. The chosen mode controls the sensitivity of Immediate Rebalance detection, allowing users to fine-tune the indicator to their trading style.
Multi Time Frame : Enabling this option allows users to set the indicator to a specific timeframe (1 minute, 5 minutes, 15 minutes, 30 minutes, 1 hour, 4 hours, daily, weekly, or monthly), broadening the perspective for identifying Immediate Rebalance zones across multiple timeframes.
🔵 Conclusion
The Immediate Rebalance indicator, based on rapid balancing zones within supply and demand areas, serves as a powerful tool for market analysis and improving trade decision-making.
By accurately identifying zones where price achieves instant balance without gaps, the indicator highlights areas likely to support strong trend continuations, exempt from common retracements.
The indicator’s use of percentage levels enables traders to pinpoint optimal entry and exit points more effectively, with levels like 50% and 75% acting as support within demand zones and resistance within supply zones. This empowers traders to ride strong trends without the worry of abrupt reversals.
Overall, the Immediate Rebalance is a reliable tool for both professional and beginner traders seeking precise methods to recognize supply and demand zones, capitalizing on consistent trends.
By choosing appropriate settings and focusing on the zones highlighted by this indicator, traders can enter trades with greater confidence and improve their risk management.
Patrick [TFO]This Patrick indicator was made for the 1 year anniversary of my Spongebob indicator, which was an experiment in using the polyline features of Pine Script to draw complex subjects. This indicator was made with the same methodology, with some helper functions to make things a bit easier on myself. It's sole purpose is to display a picture of Patrick Star on your chart, particularly the "I have $3" meme.
The initial Spongebob indicator included more than 1300 lines of code, as there were several more shapes to account for compared to Patrick, however it was done rather inefficiently. I essentially used an anchor point for each "layer" or shape (eye, nose, mouth, etc.), and drew from that point. This resulted in a ton of trial and error as I had to be very careful about the anchor points for each and every layer, and then draw around that point. In this indicator, however, I gave myself a frame to work with by specifying fixed bounds that you'll see in the code: LEFT, RIGHT, TOP, and BOTTOM.
var y_size = 4
atr = ta.atr(50)
LEFT = bar_index + 10
RIGHT = LEFT + 200
TOP = open + atr * y_size
BOTTOM = open - atr * y_size
You may notice that the top and bottom scale with the atr, or Average True Range to account for varying price fluctuations on different assets.
With these limits established, I could write some simple functions to translate my coordinates, using a range of 0-100 to describe how far the X coordinates should be from left to right, where left is 0 and right is 100; and likewise how far the Y coordinates should be from bottom to top, where bottom is 0 and top is 100.
X(float P) =>
result = LEFT + math.floor((RIGHT - LEFT)*P/100)
Y(float P) =>
result = BOTTOM + (TOP - BOTTOM)*P/100
With these functions, I could then start drawing points much simpler, with respect to the overall frame of the picture. If I wanted a point in the dead center of the frame, I would choose X(50), Y(50) for example.
At this point, the process just became tediously drawing each layer of my reference picture, including but not limited to Patrick's body, arm, mouth, eyes, eyebrows, etc. I've attached the reference picture here (left), without the text enabled.
As tedious as this was to create, it was done much more efficiently than Spongebob, and the ideas used here will make it much easier to draw more complex subjects in the future.
Multi-Timeframe RangeThe Multi-Timeframe Range Indicator is designed for traders looking to monitor key price levels across various timeframes (Daily, Weekly, Monthly, Quarterly, and Yearly) directly on their charts. This indicator draws boxes and mid-lines for each timeframe’s high, low, and midpoint, enabling users to visualize price ranges and assess potential areas of support and resistance more effectively.
Features:
Dynamic Range Boxes: Displays the high, low, and midpoint levels for each specified timeframe, with customizable colors for easy differentiation.
Visual Cues for Monday’s Levels: Highlights Monday’s high, low, and midpoint levels each week to support intraday trading setups and weekly trend analysis.
Multi-Timeframe Flexibility: Easily toggle between timeframes to view ranges from daily to yearly, making this indicator suitable for both short-term and long-term traders.
Ideal Use Cases:
Identify key support and resistance zones based on multiple timeframes.
Assess weekly and monthly trends using the Monday range levels.
Gain insights into market structure across various timeframes.
Daily ATR 2 and 10 Percent Value
This indicator shows three values: the main ATR value, a 2% value,
and a 10% Value of the Daily ATR.
After you have added the indicator to your chart, follow these steps
to be able to see the values and labels on the right.
1. Right-click on the price level bar or click the gear icon at the bottom
2. Click on LABELS
3. Select with a check mark the following two
INDICATORS AND FINANCIAL NAME LABELS
and INDICATORS AND FINANCIAL VALUE LABELS
4. Look for D-ATR % Value, click on the gear icon and verify these settings
- D-ATR Lenght = 14
- ATR Lenght = 14
- Smoothing = RMA
- Timeframe = 1 Day
5. Select Wait for timeframe closes
6. Click on Defaults, Save as default, and click ok.
You can move the indicator to the top of your chart if preferred, by clicking
on Move pane up.
Slightly modifications from other indicators.
Weekly High/Low Day BreakdownThe "Weekly High/Low Day Breakdown" is a tool designed to help identify patterns in market behaviour by analysing the days of the week when weekly highs and lows occur. This indicator calculates the frequency and percentage of weekly highs and lows for each day from Monday to Sunday within the visible range of your chart.
Features:
Weekly Analysis: Calculates weekly highs and lows based on daily open high and low prices from Monday to Sunday.
Day-Specific Breakdown: Tracks which day of the week each weekly high and low occurred.
Visible Range Focus: Only considers data within the current visible range of your chart for precise analysis.
Interactive Table Display: Presents the results in an easy-to-read table directly on your chart.
How It Works:
Data Collection: Fetches daily high, low, day of the week, and time data regardless of your chart's timeframe. Uses these daily figures to determine the weekly high and low for each week.
Weekly Tracking: Monitors the day of the week when the weekly high and low prices occur. Resets tracking at the end of each week (Sunday).
Visible Range Analysis: Only includes weeks that fall entirely within the visible time range of your chart. Ensures that the analysis is relevant to the period you are focusing on.
Percentage Calculation: Counts the occurrences of weekly highs and lows for each day. Calculates the percentage based on the total number of weeks in the visible range.
Result Display: Generates a table with days of the week as columns and "Weekly High" and "Weekly Low" as rows. Displays the percentage values, indicating how often highs and lows occur on each day.
How to Use:
Add the Indicator: Apply the "Weekly High/Low Day Breakdown" indicator to your TradingView chart.
Adjust Visible Range: Zoom in or out to set the desired visible time range for your analysis.
Interpret the Table:
Columns: Represent days from Monday to Sunday.
"Weekly High" Row: Shows the percentage of times the weekly high occurred on each day. "Weekly Low" Row: Shows the percentage of times the weekly low occurred on each day.
Colors: Blue text indicates high percentages, red text indicates low percentages.
Example Interpretation:
If the table shows a 30% value under "Tuesday" for "Weekly High," it means that in 30% of the weeks within the visible range, the highest price of the week occurred on a Tuesday.
Similarly, a 40% value under "Friday" for "Weekly Low" indicates that 40% of the weekly lows happened on a Friday.
VWAP it GOODWhy: Instead of having 5 individual VWAP indicators, I found it beneficial for one view with a clean display. This VWAP indicator combines the daily, weekly, monthly, quarterly and annual into one color coded view. These colors and styles can be modified by the user.
A user can turn any timeframe off, but this is how I personally like to trade since it helps me better understand potential bounce or pullback areas.
Do your own research for what is best for you.
Custom Time Frame BackgroundThis indicator allows you to highlight custom time frames on your chart with alternating background colors. It's particularly useful for visualizing specific intervals that are not standard on TradingView, such as 4-hour, 6-hour, or any other custom duration you choose. Features:
Customizable time frames: Set any combination of minutes, hours, and days
Fallback to daily/weekly coloring if no custom time frame is set
User-defined colors for alternating backgrounds
How to use:
Add the indicator to your chart
In the settings, input your desired custom time frame:
Set 'Custom Minutes' for intervals less than an hour
Use 'Custom Hours' for hourly intervals
Use 'Custom Days' for daily intervals
Adjust 'Color 1' and 'Color 2' to your preferred background colors
Examples:
For a 4-hour time frame: Set Custom Hours to 4
For a 6-hour time frame: Set Custom Hours to 6
For a 2-day time frame: Set Custom Days to 2
If all inputs are set to 0, the indicator will default to daily coloring for intraday charts and weekly coloring for higher timeframes. This indicator helps traders visually segment their charts into custom intervals, making it easier to identify patterns and trends over specific time periods.
Z-Score Weighted Trend System I [InvestorUnknown]The Z-Score Weighted Trend System I is an advanced and experimental trading indicator designed to utilize a combination of slow and fast indicators for a comprehensive analysis of market trends. The system is designed to identify stable trends using slower indicators while capturing rapid market shifts through dynamically weighted fast indicators. The core of this indicator is the dynamic weighting mechanism that utilizes the Z-score of price , allowing the system to respond effectively to significant market movements.
Dynamic Z-Score-Based Weighting System
The Z-Score Weighted Trend System I utilizes the Z-score of price to assign weights dynamically to fast indicators. This mechanism is designed to capture rapid market shifts at potential turning points, providing timely entry and exit signals.
Traders can choose from two primary weighting mechanisms:
Threshold-Based Weighting: The fast indicators are given weight only when the absolute Z-score exceeds a user-defined threshold. Below this threshold, fast indicators have no impact on the final signal.
Continuous Weighting: By setting the threshold to zero, fast indicators always contribute to the final signal, regardless of Z-score levels. However, this increases the likelihood of false signals during ranging or low-volatility markets
// Calculate weight for Fast Indicators based on Z-Score (Slow Indicator weight is kept to 1 for simplicity)
f_zscore_weights(series float z, simple float weight_thre) =>
float fast_weight = na
float slow_weight = na
if weight_thre > 0
if math.abs(z) <= weight_thre
fast_weight := 0
slow_weight := 1
else
fast_weight := 0 + math.sqrt(math.abs(z))
slow_weight := 1
else
fast_weight := 0 + math.sqrt(math.abs(z))
slow_weight := 1
Choice of Z-Score Normalization
Traders have the flexibility to select different Z-score processing methods to better suit their trading preferences:
Raw Z-Score or Moving Average: Traders can opt for either the raw Z-score or a moving average of the Z-score to smooth out fluctuations.
Normalized Z-Score (ranging from -1 to 1) or Z-Score Percentile: The normalized Z-score is simply the raw Z-score divided by 3, while the Z-score percentile utilizes a normal distribution for transformation.
f_zscore_perc(series float zscore_src, simple int zscore_len, simple string zscore_a, simple string zscore_b, simple string ma_type, simple int ma_len) =>
z = (zscore_src - ta.sma(zscore_src, zscore_len)) / ta.stdev(zscore_src, zscore_len)
zscore = switch zscore_a
"Z-Score" => z
"Z-Score MA" => ma_type == "EMA" ? (ta.ema(z, ma_len)) : (ta.sma(z, ma_len))
output = switch zscore_b
"Normalized Z-Score" => (zscore / 3) > 1 ? 1 : (zscore / 3) < -1 ? -1 : (zscore / 3)
"Z-Score Percentile" => (f_percentileFromZScore(zscore) - 0.5) * 2
output
Slow and Fast Indicators
The indicator uses a combination of slow and fast indicators:
Slow Indicators (constant weight) for stable trend identification: DMI (Directional Movement Index), CCI (Commodity Channel Index), Aroon
Fast Indicators (dynamic weight) to identify rapid trend shifts: ZLEMA (Zero-Lag Exponential Moving Average), IIRF (Infinite Impulse Response Filter)
Each indicator is calculated using for-loop methods to provide a smoothed and averaged view of price data over varying lengths, ensuring stability for slow indicators and responsiveness for fast indicators.
Signal Calculation
The final trading signal is determined by a weighted combination of both slow and fast indicators. The slow indicators provide a stable view of the trend, while the fast indicators offer agile responses to rapid market movements. The signal calculation takes into account the dynamic weighting of fast indicators based on the Z-score:
// Calculate Signal (as weighted average)
float sig = math.round(((DMI*slow_w) + (CCI*slow_w) + (Aroon*slow_w) + (ZLEMA*fast_w) + (IIRF*fast_w)) / (3*slow_w + 2*fast_w), 2)
Backtest Mode and Performance Metrics
The indicator features a detailed backtesting mode, allowing traders to compare the effectiveness of their selected settings against a traditional Buy & Hold strategy. The backtesting provides:
Equity calculation based on signals generated by the indicator.
Performance metrics comparing Buy & Hold metrics with the system’s signals, including: Mean, positive, and negative return percentages, Standard deviations, Sharpe, Sortino, and Omega Ratios
// Calculate Performance Metrics
f_PerformanceMetrics(series float base, int Lookback, simple float startDate, bool Annualize = true) =>
// Initialize variables for positive and negative returns
pos_sum = 0.0
neg_sum = 0.0
pos_count = 0
neg_count = 0
returns_sum = 0.0
returns_squared_sum = 0.0
pos_returns_squared_sum = 0.0
neg_returns_squared_sum = 0.0
// Loop through the past 'Lookback' bars to calculate sums and counts
if (time >= startDate)
for i = 0 to Lookback - 1
r = (base - base ) / base
returns_sum += r
returns_squared_sum += r * r
if r > 0
pos_sum += r
pos_count += 1
pos_returns_squared_sum += r * r
if r < 0
neg_sum += r
neg_count += 1
neg_returns_squared_sum += r * r
float export_array = array.new_float(12)
// Calculate means
mean_all = math.round((returns_sum / Lookback), 4)
mean_pos = math.round((pos_count != 0 ? pos_sum / pos_count : na), 4)
mean_neg = math.round((neg_count != 0 ? neg_sum / neg_count : na), 4)
// Calculate standard deviations
stddev_all = math.round((math.sqrt((returns_squared_sum - (returns_sum * returns_sum) / Lookback) / Lookback)) * 100, 2)
stddev_pos = math.round((pos_count != 0 ? math.sqrt((pos_returns_squared_sum - (pos_sum * pos_sum) / pos_count) / pos_count) : na) * 100, 2)
stddev_neg = math.round((neg_count != 0 ? math.sqrt((neg_returns_squared_sum - (neg_sum * neg_sum) / neg_count) / neg_count) : na) * 100, 2)
// Calculate probabilities
prob_pos = math.round((pos_count / Lookback) * 100, 2)
prob_neg = math.round((neg_count / Lookback) * 100, 2)
prob_neu = math.round(((Lookback - pos_count - neg_count) / Lookback) * 100, 2)
// Calculate ratios
sharpe_ratio = math.round((mean_all / stddev_all * (Annualize ? math.sqrt(Lookback) : 1))* 100, 2)
sortino_ratio = math.round((mean_all / stddev_neg * (Annualize ? math.sqrt(Lookback) : 1))* 100, 2)
omega_ratio = math.round(pos_sum / math.abs(neg_sum), 2)
// Set values in the array
array.set(export_array, 0, mean_all), array.set(export_array, 1, mean_pos), array.set(export_array, 2, mean_neg),
array.set(export_array, 3, stddev_all), array.set(export_array, 4, stddev_pos), array.set(export_array, 5, stddev_neg),
array.set(export_array, 6, prob_pos), array.set(export_array, 7, prob_neu), array.set(export_array, 8, prob_neg),
array.set(export_array, 9, sharpe_ratio), array.set(export_array, 10, sortino_ratio), array.set(export_array, 11, omega_ratio)
// Export the array
export_array
//}
Calibration Mode
A Calibration Mode is included for traders to focus on individual indicators, helping them fine-tune their settings without the influence of other components. In Calibration Mode, the user can visualize each indicator separately, making it easier to adjust parameters.
Alerts
The indicator includes alerts for long and short signals when the indicator changes direction, allowing traders to set automated notifications for key market events.
// Alert Conditions
alertcondition(long_alert, "LONG (Z-Score Weighted Trend System)", "Z-Score Weighted Trend System flipped ⬆LONG⬆")
alertcondition(short_alert, "SHORT (Z-Score Weighted Trend System)", "Z-Score Weighted Trend System flipped ⬇Short⬇")
Important Note:
The default settings of this indicator are not optimized for any particular market condition. They are generic starting points for experimentation. Traders are encouraged to use the calibration tools and backtesting features to adjust the system to their specific trading needs.
The results generated from the backtest are purely historical and are not indicative of future results. Market conditions can change, and the performance of this system may differ under different circumstances. Traders and investors should exercise caution and conduct their own research before using this indicator for any trading decisions.
30D Vs 90D Historical VolatilityVolatility equals risk for an underlying asset's price meaning bullish volatility is bearish for prices while bearish volatility is bullish. This compares 30-Day Historical Volatility to 90-Day Historical Volatility.
When the 30-Day crosses under the 90-day, this is typically when asset prices enter a bullish trend.
Conversely, When the 30-Day crosses above the 90-Day, this is when asset prices enter a bearish trend.
Peaks in volatility are bullish divergences while troughs are bearish divergences.
