IFTCOMBO Stratejisi //@version=5
strategy("IFTCOMBO Stratejisi", overlay=true, default_qty_type=strategy.percent_of_equity, default_qty_value=10)
// --- IFTCOMBO KODU BAŞLANGICI ---
// IFTCOMBO göstergesini strategy içinde tanımlayın
STO = input.bool(false, title='Show INVERSE FISHER TRANSFORM on STOCHASTIC Line?')
RSI = input.bool(false, title='Show INVERSE FISHER TRANSFORM on RSI Line?')
CCI = input.bool(true, title='Show INVERSE FISHER TRANSFORM on CCI Line?')
MFI = input.bool(false, title='Show INVERSE FISHER TRANSFORM on MFI Line?')
//Average means: average value of all 4 inverse fisher trasformed indicators
AVERAGE = input.bool(false, title='Show INVERSE FISHER TRANSFORM AVERAGE Line?')
ccilength = input.int(5, 'CCI Length')
wmalength = input.int(9, title='Smoothing Length')
v11 = 0.1 * (ta.cci(close, ccilength) / 4)
v21 = ta.wma(v11, wmalength)
INV1 = (math.exp(2 * v21) - 1) / (math.exp(2 * v21) + 1)
rsilength = input.int(5, 'RSI Length')
v12 = 0.1 * (ta.rsi(close, rsilength) - 50)
v22 = ta.wma(v12, wmalength)
INV2 = (math.exp(2 * v22) - 1) / (math.exp(2 * v22) + 1)
stochlength = input.int(5, 'STOCH Length')
v1 = 0.1 * (ta.stoch(close, high, low, stochlength) - 50)
v2 = ta.wma(v1, wmalength)
INVLine = (math.exp(2 * v2) - 1) / (math.exp(2 * v2) + 1)
//MFI
mfilength = input.int(5, 'MFI Length')
source = hlc3
up = math.sum(volume * (ta.change(source) <= 0 ? 0 : source), mfilength)
lo = math.sum(volume * (ta.change(source) >= 0 ? 0 : source), mfilength)
mfi = 100.0 - 100.0 / (1.0 + up / lo)
v13 = 0.1 * (mfi - 50)
v23 = ta.wma(v13, wmalength)
INV3 = (math.exp(2 * v23) - 1) / (math.exp(2 * v23) + 1)
AVINV = (INV1 + INV2 + INVLine + INV3) / 4
plot(AVERAGE and AVINV ? AVINV : na, color=color.new(color.purple, 0), linewidth=3, title='AVERAGE')
plot(STO and INVLine ? INVLine : na, color=color.new(color.blue, 0), linewidth=1, title='STOCH')
plot(CCI and INV1 ? INV1 : na, color=color.new(color.red, 0), linewidth=3, title='CCIv2')
plot(RSI and INV2 ? INV2 : na, color=color.new(color.black, 0), linewidth=2, title='RSI')
plot(MFI and INV3 ? INV3 : na, color=color.new(color.purple, 0), linewidth=2, title='MFI')
hline(0.5, color=color.red)
hline(-0.5, color=color.green)
// --- IFTCOMBO KODU SONU ---
// Stop loss yüzdesi
stopLossPercentage = input.float(2.0, title="Stop-Loss Yüzdesi", minval=0.1, step=0.1)
// Alım koşulu
longCondition = AVINV <= -0.97
// Satış koşulu
shortCondition = AVINV >= 0.97
// Alım emri
if longCondition
strategy.entry("Long", strategy.long)
strategy.exit("Long Stop Loss", "Long", loss=stopLossPercentage)
// Satış emri
if shortCondition
strategy.entry("Short", strategy.short)
strategy.exit("Short Stop Loss", "Short", loss=stopLossPercentage)
Portfolio Management
Employee Portfolio Generator [By MUQWISHI]▋ INTRODUCTION :
The “Employee Portfolio Generator” simplifies the process of building a long-term investment portfolio tailored for employees seeking to build wealth through investments rather than traditional bank savings. The tool empowers employees to set up recurring deposits at customizable intervals, enabling to make additional purchases in a list of preferred holdings, with the ability to define the purchasing investment weight for each security. The tool serves as a comprehensive solution for tracking portfolio performance, conducting research, and analyzing specific aspects of portfolio investments. The output includes an index value, a table of holdings, and chart plots, providing a deeper understanding of the portfolio's historical movements.
_______________________
▋ OVERVIEW:
● Scenario (The chart above can be taken as an example) :
Let say, in 2010, a newly employed individual committed to saving $1,000 each month. Rather than relying on a traditional savings account, chose to invest the majority of monthly savings in stable well-established stocks. Allocating 30% of monthly saving to AMEX:SPY and another 30% to NASDAQ:QQQ , recognizing these as reliable options for steady growth. Additionally, there was an admired toward innovative business models of NASDAQ:AAPL , NASDAQ:MSFT , NASDAQ:AMZN , and NASDAQ:EBAY , leading to invest 10% in each of those companies. By the end of 2024, after 15 years, the total monthly deposits amounted to $179,000, which would have been the result of traditional saving alone. However, by sticking into long term invest, the value of the portfolio assets grew, reaching nearly $900,000.
_______________________
▋ OUTPUTS:
The table can be displayed in three formats:
1. Portfolio Index Title: displays the index name at the top, and at the bottom, it shows the index value, along with the chart timeframe, e.g., daily change in points and percentage.
2. Specifications: displays the essential information on portfolio performance, including the investment date range, total deposits, free cash, returns, and assets.
3. Holdings: a list of the holding securities inside a table that contains the ticker, last price, entry price, return percentage of the portfolio's total deposits, and latest weighted percentage of the portfolio. Additionally, a tooltip appears when the user passes the cursor over a ticker's cell, showing brief information about the company, such as the company's name, exchange market, country, sector, and industry.
4. Indication of New Deposit: An indication of a new deposit added to the portfolio for additional purchasing.
5. Chart: The portfolio's historical movements can be visualized in a plot, displayed as a bar chart, candlestick chart, or line chart, depending on the preferred format, as shown below.
_______________________
▋ INDICATOR SETTINGS:
Section(1): Table Settings
(1) Naming the index.
(2) Table location on the chart and cell size.
(3) Sorting Holdings Table. By securities’ {Return(%) Portfolio, Weight(%) Portfolio, or Ticker Alphabetical} order.
(4) Choose the type of index: {Assets, Return, or Return (%)}, and the plot type for the portfolio index: {Candle, Bar, or Line}.
(5) Positive/Negative colors.
(6) Table Colors (Title, Cell, and Text).
(7) To show/hide any of selected indicator’s components.
Section(2): Recurring Deposit Settings
(1) From DateTime of starting the investment.
(2) To DateTime of ending the investment
(3) The amount of recurring deposit into portfolio and currency.
(4) The frequency of recurring deposits into the portfolio {Weekly, 2-Weeks, Monthly, Quarterly, Yearly}
(5) The Depositing Model:
● Fixed: The amount for recurring deposits remains constant throughout the entire investment period.
● Increased %: The recurring deposit amount increases at the selected frequency and percentage throughout the entire investment period.
(5B) If the user selects “ Depositing Model: Increased % ”, specify the growth model (linear or exponential) and define the rate of increase.
Section(3): Portfolio Holdings
(1) Enable a ticker in the investment portfolio.
(2) The selected deposit frequency weight for a ticker. For example, if the monthly deposit is $1,000 and the selected weight for XYZ stock is 30%, $300 will be used to purchase shares of XYZ stock.
(3) Select up to 6 tickers that the investor is interested in for long-term investment.
Please let me know if you have any questions
Ticker Tape with Multiple Inputs# Ticker Tape
A customizable multi-symbol price tracker that displays real-time price information in a scrolling ticker format, similar to financial news tickers.
This indicator is inspired from Tradingciew's default tickertape indicator with changes in the way inputs are given.
### Overview
This indicator allows you to monitor up to 15 different symbols simultaneously across any supported exchanges on TradingView. It displays essential price information including current price, price change, and percentage change in an easy-to-read format at the bottom of your chart.
### Features
• Monitor up to 15 different symbols simultaneously
• Support for any exchange available on TradingView
• Real-time price updates
• Color-coded price changes (green for increase, red for decrease)
• Smooth scrolling animation (can be disabled)
• Customizable scroll speed and position offset
### Input Parameters
#### Ticker Tape Controls
• Running: Enable/disable the scrolling animation
• Offset: Adjust the starting position of the ticker tape
#### Symbol Settings
• Exchange (1-15): Enter the exchange name (e.g., NSE, BINANCE, NYSE)
• Symbol (1-15): Enter the symbol name (e.g., BANKNIFTY, RELIANCE, BTCUSDT)
### Display Format
For each symbol, the ticker shows:
1. Symbol Name
2. Current Price
3. Price Change (Absolute and Percentage)
### Example Usage
Input Settings:
Exchange 1: NSE
Symbol 1: BANKNIFTY
Exchange 2: NSE
Symbol 2: RELIANCE
The ticker tape will display:
`NIFTY BANK 46750.00 +350.45 (0.75%) | RELIANCE 2456.85 -12.40 (-0.50%) |`
### Use Cases
1. Multi-Market Monitoring: Track different markets simultaneously without switching between charts
2. Portfolio Tracking: Monitor all your positions in real-time
### Tips for Best Use
1. Group related symbols together for easier monitoring
2. Use the offset parameter to position important symbols in your preferred viewing area
3. Disable scrolling if you prefer a static display
4. Leave exchange field empty for default exchange symbols
### Notes
• Price updates occur in real-time during market hours
• Color coding helps quickly identify price direction
• The indicator adapts to any chart timeframe
• Empty input pairs are automatically skipped
### Performance Considerations
The indicator is optimized for efficiency, but monitoring too many high-frequency symbols might impact chart performance. It's recommended to use only the symbols you actively need to monitor.
Version: 2.0 Stock_Cloud
Last Updated: December 2024
Standard Deviation of Returns: DivergencePurpose:
The "Standard Deviation of Returns: Divergence" indicator is designed to help traders identify potential trend reversals or continuation signals by analyzing divergences between price action and the statistical volatility of returns. Divergences can signal weakening momentum in the prevailing trend, offering insight into potential buying or selling opportunities.
Key Components
1. Returns Calculation:
* The indicator uses logarithmic returns (log(close / close )) to measure relative price changes in a normalized manner.
* Log returns are more effective than simple price differences when analyzing data across varying price levels, as they account for percentage-based changes.
2. Standard Deviation of Returns:
* The script computes the standard deviation of returns over a user-defined lookback period (ta.stdev(returns, lookback)).
* Standard deviation measures the dispersion of returns around their average, effectively quantifying market volatility.
* A higher standard deviation indicates increased volatility, while lower standard deviation reflects a calmer market.
3. Price Action:
* Detects higher highs (new peaks in price) and lower lows (new troughs in price) over the lookback period.
* Price trends are compared to the behavior of the standard deviation.
4. Divergence Detection:
A divergence occurs when price action (higher highs or lower lows) is not confirmed by a corresponding movement in standard deviation:
Bullish Divergence: Price makes a lower low, but the standard deviation does not, signaling potential upward momentum.
Bearish Divergence: Price makes a higher high, but the standard deviation does not, signaling potential downward momentum.
5. Visual Cues:
The script highlights divergence regions directly on the chart:
Green Background: Indicates a bullish divergence (potential buy signal).
Red Background: Indicates a bearish divergence (potential sell signal).
How It Works
Inputs:
* The user specifies the lookback period (lookback) for calculating the standard deviation and detecting divergences.
Calculation:
* Each bar’s returns are computed and used to calculate the standard deviation over the specified lookback period.
* The indicator evaluates price highs/lows and compares these with the highest and lowest values of the standard deviation within the same lookback period.
Highlight of Divergences:
When divergences are detected:
Bullish Divergence: The background of the chart is shaded green.
Bearish Divergence: The background of the chart is shaded red.
Trading Application
Bullish Divergence:
* Occurs when the market is oversold, or downward momentum is weakening.
* Suggests a potential reversal to an uptrend, signaling a buying opportunity.
Bearish Divergence:
* Occurs when the market is overbought, or upward momentum is weakening.
* Suggests a potential reversal to a downtrend, signaling a selling opportunity.
Contextual Use:
* Use this indicator in conjunction with other technical tools like RSI, MACD, or moving averages to confirm signals.
* Effective in volatile or ranging markets to help anticipate shifts in momentum.
Summary
The "Standard Deviation of Returns: Divergence" indicator is a robust tool for spotting divergences that can signal weakening market trends. It combines statistical volatility with price action analysis to highlight key areas of potential reversals. By integrating this tool into your trading strategy, you can gain additional confirmation for entries or exits while keeping a close watch on momentum shifts.
Disclaimer: This is not a financial advise; please consult your financial advisor for personalized advice.
US Recessions OverlayThe US Recessions Overlay indicator highlights the periods of US economic recessions directly on your TradingView chart. Using historical data from the Great Depression to the present, it provides a visual representation of recessions as transparent red backgrounds. This can help traders and analysts correlate market movements with historical economic downturns.
Features:
- Displays US recessions since the Great Depression (1929) as shaded areas.
- Automatically adjusts the background shading to match the date ranges of historical recessions.
- A simple and effective way to observe market behavior during recessionary periods.
- Fully customizable to include new recession periods or modify transparency levels.
How to Use:
Apply the indicator to any chart. Recession periods will appear as red-shaded backgrounds, providing a clear visual cue for market behavior during those times.
Use Case:
Ideal for traders, economists, and market historians who wish to study the impact of recessions on financial markets.
MES Position Sizing EstimatorDescription and Use:
Here is an indicator which aims to help all Micro-ES futures traders who struggle with risk management! I created this indicator designed as a general guideline to help short term traders (designed for 1 minute candles) determine how many contracts to trade on the MES for their desired profit target.
To use the indicator, simply go to MES on the 1 minute timeframe, apply the indicator, and enter your Holding Period (how long you want to have your position open for), Value Per Tick
(usually 1.25 for MES since one point is $5) and your target PnL for the trade in the inputs tab.
It will then show in a table the recommended position sizing, as well as the estimated price change for your holding period. Additionally, there are two plotted lines also showing the position sizing and estimated price change historically.
How the indicator works
On the technical level, I made calculations for this indicator using Python. I downloaded 82 days of 1 minute OHLC data from TradingView, and then ran regression (log-transformed linear regression specifically) to calculate how the average price change in MES futures scales with the amount of time a position is held for, and then ran these regressions for every hour of the day. I then copied the equations from those regressions into Pinescript, and used the assumption that:
position size = target PnL / (estimated price change for time * tick value)
Therefore, Choosing the number of contracts to trade position sizing for Micro E-mini S&P 500 Futures (MES) based on time of day, holding period, and tick value. This tool leverages historical volatility patterns and log-transformed linear regression models to provide precise recommendations tailored to your trading strategy.
If you want to check out how the regression code worked in python, it is all open source and available on my Github repository for it .
Notes:
The script assumes a log-normal distribution of price movements and is intended as an educational tool to aid in risk management.
It is not a standalone trading system and should be used in conjunction with other trading strategies and risk assessments.
Past performance is not indicative of future results, and traders should exercise caution and adjust their strategies based on personal risk tolerance.
This script is open-source and available for use and modification by the TradingView community. It aims to provide a valuable resource for traders seeking to enhance their risk management practices through data-driven insights.
DCA Valuation & Unrealized GainsThis Pine Script for TradingView calculates and visualizes the relationship between a Dollar Cost Average (DCA) price and the All-Time High (ATH) price for over 50 different cryptocurrencies. Here's what it does:
1. Inputs for DCA Prices:
- Users can manually input DCA prices for specific cryptocurrencies (e.g., BTC, ETH, BNB).
2. Dynamic ATH Calculation:
- Dynamically calculates the ATH price for the current asset using the highest price in the chart's loaded data and persists this value across bars.
3. Percentage Change from DCA to ATH:
- Computes the percentage gain from the DCA price to the ATH price.
4. Visualizations:
- Draws a line at the DCA price and the ATH price, both extended to the right.
- Adds an arrow pointing from the DCA price to the ATH, offset by 10 bars into the future.
- Displays labels for:
- The percentage gain from DCA to ATH.
- "No DCA Configured" if no valid DCA price is set for the asset.
5. Color Coding:
- Labels and arrows are color-coded to indicate positive or negative percentage changes:
- Green for gains.
- Red for losses.
6. Adaptability:
- The script dynamically adjusts to the current asset based on its ticker and uses the corresponding DCA price.
This functionality provides traders with clear insights into their investment's performance relative to its ATH, aiding in decision-making.
-----
To add a new asset to the script:
1. Define the DCA Input: Add a new input for the asset's DCA price using the `input.float` function. For example:
dcaPriceNEW = input.float(title="NEW DCA Price", defval=0.1, tooltip="Set the DCA price for NEW")
2. Add the Asset Logic: Include a conditional check for the new asset in the ticker matching logic:
if str.contains(currentAsset, "NEW") and dcaPriceNEW != 0
dcaPrice := dcaPriceNEW
Where NEW is the ticker symbol of the asset you're adding.
NOTE: SOLO had to be put before SOL because otherwise the indicator was pulling the DCA price from SOL even on the SOLO chart. If you have a similar issue, try that fix.
Adding an asset requires only these two changes. Once done, the script dynamically incorporates the new asset into its calculations and visualizations.
IU VaR (Value at Risk) Historical MethodThis Pine Script indicator calculates the **Value at Risk (VaR)** using the **Historical Method** to help traders understand potential losses during a given period( Chart Timeframe) with a specific level of confidence.
What is Value at Risk (VaR) ?
Value at Risk (VaR) is a measure used in finance to estimate the potential loss in value of an asset, portfolio, or investment over a specific time period, given normal market conditions, and at a certain confidence level.
Example:
Suppose you invest ₹1,00,000 in stocks. A VaR of 5% at a 95% confidence level means:
- There is a **95% chance** that you won’t lose more than **₹5,000** in a day.
- Conversely, there is a **5% chance** that your loss could exceed ₹5,000 in a day.
VaR is a helpful tool for understanding risk and making informed investment decisions!
How It Works:
1. The indicator calculates the percentage difference between consecutive bars.
2. The differences are sorted, and the VaR is determined based on the assurance level you specify.
3. A label displays the VaR value on the chart, indicating the potential maximum loss with the selected assurance level within one period eg - ( 1h, 4h , 1D, 1W, 1M etc as per your chart timeframe )
Key Features:
- Customizable Assurance Level:
Set the confidence level (e.g., 95%) to determine the probability of loss.
-Historical Approach:
Uses the past percentage changes in price to calculate the risk.
-Clear Insights:
Displays the calculated VaR value on the chart with an informative tooltip explaining the risk.
Use this tool to better understand your market exposure and manage risk!
Adaptive ATR Trailing Stops█ Introduction
This script is based on the average true range (ATR) and has been improved with the HHV or LLV. The script supports the trader to have his stoploss trailed. In this case, the stoploss is dynamic and can be adjusted with each candleclose.
█ What Does This Indicator Do?
The ATR SL Trailing Indicator helps you dynamically adjust your stop-loss levels based on market movements. It uses market volatility to calculate trailing stop-loss levels, ensuring you can secure profits or minimize losses. The indicator creates two lines:
A green/red line for long positions (when you’re betting on prices going up).
A green/red line for short positions (when you’re betting on prices going down).
█ Key Concepts: How Does the Indicator Work?
The Average True Range (ATR) measures market volatility, showing how much the price moves over a specific period.
A high ATR indicates a volatile market (large price swings), while a low ATR indicates a quiet market (smaller price changes).
Why is ATR important? ATR helps dynamically adjust the distance between your stop-loss and the current price. In volatile markets, the stop-loss is placed further away to avoid being triggered by short-term fluctuations. In quieter markets, the stop-loss is set closer to the price.
The HHV is the highest price over a specific period. For long positions, the indicator uses the highest price minus an ATR-based value to determine the stop-loss level.
Why is HHV important? HHV ensures the stop-loss for long positions only moves up when the price reaches new highs. Once the price starts falling, the stop-loss remains unchanged to lock in profits or minimize losses.
The LLV is the lowest price over a specific period. For short positions, the indicator uses the lowest price plus an ATR-based value to determine the stop-loss level.
Why is LLV important? LLV ensures the stop-loss for short positions only moves down when the price reaches new lows. Once the price starts rising, the stop-loss remains unchanged to lock in profits or minimize losses.
█ How Does the Indicator Work?
For Long Positions:
The indicator sets the stop-loss below the current price, based on:
Market volatility (ATR).
The highest price over a specific period (HHV).
The line turns green when the current price is above the stop-loss.
The line turns red when the price drops below the stop-loss, signaling you may need to exit the trade.
For Short Positions:
The indicator sets the stop-loss above the current price, based on:
*Market volatility (ATR).
*The lowest price over a specific period (LLV).
*The line turns green when the current price is below the stop-loss.
*The line turns red when the price moves above the stop-loss, signaling you may need to exit the trade.
█ Advantages of the ATR SL Trailing Indicator
*Dynamic and adaptive: Automatically adjusts stop-loss levels based on market volatility.
*Visual clarity: Green and red lines clearly indicate whether your position is safe or at risk.
*Effective risk management: Helps you lock in profits and minimize losses without the need for constant manual adjustments.
█ When Should You Use This Indicator?
*If you practice trend-based trading and want your stop-losses to automatically adapt to market movements.
*In volatile markets, to avoid being stopped out by short-term fluctuations.
*When you want to implement efficient risk management without manually adjusting your positions.
█ Inputs
The user can set the indicator for both longs and shorts. This is particularly important because the calculation is different. The HHV is used for longs and the LLV for shorts. The user can therefore set the period/length for the ATR on the one hand and the HHV/LLV on the other. He also has a multiplier, which can also be customized. The multiplier multiplies the price change of each individual candle.
█ Color Change
If the SL is trailed and the price breaks a line, the color changes. In this case, it would have executed the SL on an open trade.
ETF-Benchmark AnalyzerHave you ever wondered which ETF performs the best? Which one is the most volatile, or which one has the smallest drawdown?
This Pine Script™ "ETF-Benchmark Analyzer" compares the performance of an ETF (such as SPY, the S&P 500 ETF) against a benchmark, which can also be adjusted by the user. It provides several key financial metrics, such as:
Performance (%): Displays the total return over a specified lookback period (e.g., 1 year). It compares the performance of the ETF against the benchmark and shows the difference.
Alpha (%): Measures the excess return of the ETF over the expected return, which is calculated using the benchmark’s return. Positive alpha indicates that the ETF has outperformed the benchmark, while negative alpha suggests underperformance. This metric is important because it isolates performance that cannot be explained by exposure to the benchmark's movements.
Sharpe Ratio: A risk-adjusted measure of return. It is calculated by dividing the excess return of the ETF (above the risk-free rate) by its standard deviation (volatility). A higher Sharpe ratio indicates better risk-adjusted returns. The Sharpe ratio is calculated for both the ETF and the benchmark, and their difference is displayed as well.
Drawdown: The percentage decrease from the highest price to the lowest price over the lookback period. This is a critical measure of risk, as it shows the largest potential loss an investor might face during a specific period.
Beta: Measures the ETF’s sensitivity to movements in the benchmark. A beta of 1 means the ETF moves in line with the benchmark; greater than 1 means it is more volatile, while less than 1 means it is less volatile.
These metrics provide a holistic view of the ETF’s performance compared to the benchmark, allowing traders to assess the risk and return profile more effectively.
Scientific Sources
Sharpe Ratio: Sharpe, W. F. (1994). The Sharpe Ratio. Journal of Portfolio Management, 21(1), 49-58. This paper defines and develops the Sharpe ratio as a measure of risk-adjusted return.
Alpha and Beta: Jensen, M. C. (1968). The Performance of Mutual Funds in the Period 1945–1964. The Journal of Finance, 23(2), 389-416. This paper discusses the concepts of alpha and beta in the context of mutual fund performance.
Correlation Coefficient [Giang]### **Introduction to the "Correlation Coefficient" Indicator**
#### **Idea behind the Indicator**
The "Correlation Coefficient" indicator was developed to analyze the linear relationship between Bitcoin (**BTCUSD**) and other important economic indices or financial assets, such as:
- **SPX** (S&P 500 Index): Represents the U.S. stock market.
- **DXY** (Dollar Index): Reflects the strength of the USD against major currencies.
- **SPY** (ETF representing the S&P 500): A popular trading instrument.
- **GOLD** (Gold price): A traditional safe-haven asset.
The correlation between these assets can help traders understand how Bitcoin reacts to market movements of traditional financial instruments, providing opportunities for more effective trading decisions.
Additionally, the indicator allows users to **customize asset symbols for comparison**, not limited to the default indices (SPX, DXY, SPY, GOLD). This flexibility enables traders to tailor their analysis to specific goals and portfolios.
---
#### **Significance and Use of Correlation in Trading**
**Correlation** is a measure of the linear relationship between two data series. In the context of this indicator:
- **The correlation coefficient ranges from -1 to 1**:
- **1**: Perfect positive relationship (both increase or decrease together).
- **0**: No linear relationship.
- **-1**: Perfect negative relationship (one increases while the other decreases).
- **Use in trading**:
- Identify **strong relationships or unusual divergences** between Bitcoin and other assets.
- Help determine **market sentiment**: For example, if Bitcoin has a negative correlation with DXY, traders might expect Bitcoin to rise when the USD weakens.
- Provide a foundation for hedging strategies or investments based on inter-asset relationships.
---
#### **Components of the Indicator**
The "Correlation Coefficient" indicator consists of the following key components:
1. **Main Data (BTCUSD)**:
- The closing price of Bitcoin is used as the central asset for calculations.
2. **Comparison Data**:
- Users can select different asset symbols for comparison. By default, the indicator supports:
- **SPX**: Stock market index.
- **DXY**: Dollar Index.
- **SPY**: Popular ETF.
- **GOLD**: Gold price.
3. **Correlation Coefficients**:
- Calculated between BTC and each comparison index, based on a Weighted Moving Average (WMA) over a user-defined period.
4. **Graphical Representation**:
- Displays individual correlation coefficients with each comparison index, making it easier for traders to track and analyze.
---
#### **How to Analyze and Use the Indicator**
**1. Identify Key Correlations:**
- Observe the correlation lines between BTC and the indices to determine positive or negative relationships.
- Example:
- If the **Correlation Coefficient (BTC-DXY)** sharply declines to -1, this indicates that when USD strengthens, Bitcoin tends to weaken.
**2. Analyze the Strength of Correlations:**
- **Strong Correlations**: If the coefficient is close to 1 or -1, the relationship between the two assets is very clear.
- **Weak Correlations**: If the coefficient is near 0, Bitcoin may be influenced by other factors outside the compared index.
**3. Develop Trading Strategies:**
- Use correlations to predict Bitcoin's price movements:
- If BTC has an inverse relationship with **DXY**, traders might consider selling BTC when the USD strengthens.
- If BTC and **SPX** are strongly correlated, traders can monitor the stock market to predict Bitcoin's trend.
**4. Evaluate Changes Over Time:**
- Use different timeframes (daily, weekly) to track the correlation's fluctuations.
- Look for unusual signals, such as a breakdown or shift from positive to negative relationships.
---
#### **Conclusion**
The "Correlation Coefficient" indicator is a powerful tool that helps traders analyze the relationship between Bitcoin and major financial indices. The ability to customize asset symbols for comparison makes the indicator flexible and suitable for various trading strategies. When used correctly, this indicator not only provides insights into market sentiment but also supports the development of intelligent trading strategies and optimized profits.
Volatility and Tick Size DataThis indicator, titled "Tick Information & Standard Deviation Table," provides detailed insights into market microstructure, including tick size, point value, and standard deviation values calculated based on the True Range. It helps visualize essential trading parameters that influence transaction costs, risk management, and portfolio performance, including volatility measures that can guide investment strategies.
Why These Data Points Are Important for Portfolio Management
Tick Size and Point Value:
Tick size refers to the smallest possible price movement in a given asset. It defines the granularity of the price changes, affecting how precise the market price can be at any moment. Point value reflects the monetary value of a single price movement (one tick). These two data points are essential for understanding transaction costs and for evaluating how much capital is at risk per price movement. Smaller tick sizes may lead to more efficient pricing in high-frequency trading strategies (Hasbrouck, 2009).
Reference: Hasbrouck, J. (2009). Empirical Market Microstructure. Foundations and Trends® in Finance, 3(4), 169-272.
Standard Deviations and Volatility:
Standard deviation measures the variability or volatility of an asset's price over a set period. This data point is critical for portfolio management, as it helps to quantify risk and predict potential price movements. True Range and its standard deviations provide a more comprehensive measure of market volatility than just price fluctuations, as they include gaps and extreme price changes. Investors use volatility data to assess the potential risk and adjust portfolio allocations accordingly (Ang, 2006).
Reference: Ang, A. (2006). Asset Management: A Systematic Approach to Factor Investing. Oxford University Press.
Risk Management:
The ability to quantify risk through metrics like the 1st, 2nd, and 3rd standard deviations of the true range is essential for implementing risk controls within a portfolio. By incorporating volatility data, portfolio managers can adjust their strategies for different market conditions, potentially reducing exposure to high-risk environments. These volatility measures help in setting stop-loss levels, optimizing position sizes, and managing the portfolio’s overall risk-return profile (Black & Scholes, 1973).
Reference: Black, F., & Scholes, M. (1973). The Pricing of Options and Corporate Liabilities. Journal of Political Economy, 81(3), 637-654.
Portfolio Diversification and Hedging:
Understanding asset volatility and transaction costs is critical when constructing a diversified portfolio. By using the standard deviations from this indicator, investors can better identify assets that may provide diversification benefits, potentially reducing the overall portfolio risk. Moreover, the point values and tick sizes help assess the cost-effectiveness of various assets, enabling portfolio managers to implement more efficient hedging strategies (Markowitz, 1952).
Reference: Markowitz, H. (1952). Portfolio Selection. The Journal of Finance, 7(1), 77-91.
Conclusion
The Tick Information & Standard Deviation Table provides critical market data that informs the risk management, diversification, and pricing strategies used in portfolio management. By incorporating tick size, point value, and volatility metrics, investors can make more informed decisions, better manage risk, and optimize the returns on their portfolios. The data serves as an essential tool for aligning asset selection and portfolio allocations with the investor's risk tolerance and market conditions.
Confluence ChecklistHOW DOES IT WORK?
The "Confluence Checklist" indicator helps you to stick to your trading plan with your set rules. You have a total of 8 rules that can be set up manually. Using the checkbox, you can check during trading whether your rules are followed or not. You can change the colors of the table on the one hand, and the size and width of the table columns on the other.
█ WHAT MAKES IT UNIQUE?
It is the only checklist indicator on Tradingview that has an integrated checkbox. Thus, you can always check your trading plan.
█ HOW TO USE IT?
The best way to start is to create your personal trading plan based on your trading strategy. Then you can display the trading plan digitally in Tradingview. This way you don't have to write and check your rules on paper anymore. This is very important for scalping, because sometimes you only have a few seconds left for the execution. After creating the trading plan, you can integrate it into the checklist. Before placing an order, you can check the checklist to see if the trade is really valid.
Z The Good Stuff +I created this script to have a couple datapoints that I want to look at when going through charts to find trade ideas. Qullamaggie is one of my biggest inspirations and I built in a couple of his concepts with a touch to help me with sizing properly, all explained below:
Box 1: ADR %, Average Daily Range, gives and indication of how volatile the stock is. It uses the 20 day average % move of the current stock on the chart.
Box 2: LOD Distance, low of day distance is a quality of life element I created. It calculates the low for the current candle and color codes it red or green depending on if it's higher or lower than the daily ADR. The logic is that if a stock has an average speed, buying on a setup it is preferred if the stop distance (assuming a low of day stop) should be less than the ADR to improve the odds of more upside.
Box 3: Todays DV, this shows a rough estimate of how much money was traded on the particular day.
Box 4: ADV 20 days, similar to above this shows the 20 day $ traded average. The point to look at it is to have a better idea what position size is possible to not get stuck in something too illiquid.
Box 5: Market cap, just shows the market cap of the stock to know what size the company is.
Box 6: Number of shares, this is an additional quality of life aspect. If using low of day stops, this part calculates based on the users' inputted portfolio size and portfolio risk preference and then calculates how many stocks to buy to stay within the risk parameters. It is obviously not a sole decision making parameter nor does it guarantee any execution, but if a stock is showing an entry you want to take you can use the number of shares to help you know how many to buy. The preset is a portfolio of 10000 and a risk of 0.25%. This means that the number of shares to buy will be at the current price with lod stop that would result in a 0.25% portfolio loss. OF COURSE the actual loss depends on the execution and if the user places a stop loss order.
Hope you find it useful and feel free to give feedback! Cheers!
Position Sizing Calculator (Real-Time)█ SUMMARY
The following indicator is a Position Sizing Calculator based on Average True Range (ATR), originally developed by market technician J. Welles Wilder Jr., intended for real-time trading.
This script utilizes the user's account size, acceptable risk percentage, and a stop-loss distance based on ATR to dynamically calculate the appropriate position size for each trade in real time.
█ BACKGROUND
Developed for use on the 5-minute timeframe, this script provides traders with continuously updated, dynamic position sizes. It enables traders to instantly determine the exact number of shares and dollar amount to use for entering a trade within their acceptable risk tolerance whenever a trade opportunity arises.
This real-time position sizing tool helps traders make well-informed decisions when planning trade entries and calculating maximum stop-loss levels, ultimately enhancing risk management.
█ USER INPUTS
Trading Account Size: Total dollar value of the user's trading account.
Acceptable Risk (%): Maximum percentage of the trading account that the user is willing to risk per trade.
ATR Multiplier for Stop-Loss: Multiplier used to determine the distance of the stop-loss from the current price, based on the ATR value.
ATR Length: The length of the lookback period used to calculate the ATR value.
Portfolio [Afnan]🚀 Portfolio - Advanced Portfolio Management Indicator 📊
A game-changing portfolio management tool designed to help traders stay on top of their positions and manage risk efficiently. This indicator combines detailed tracking, real-time analytics, and visual clarity to ensure traders are well-equipped for the dynamic world of financial markets.
📈 Key Features 💡
Track up to 14 positions with ease
Real-time Profit & Loss (P&L) updates and risk metrics
Visual representation of entry, stop-loss (SL), and target levels
Alerts for stop-loss breaches and target achievements
Comprehensive portfolio summaries for quick analysis
Customizable options to suit individual trading styles
🔍 Main Components ⚙️
📊 1. Position Tracking
Detailed position data: entry, stop-loss, target levels, and more
Real-time risk-reward ratios
Insights into position size and exposure percentages
Continuous updates on P&L in real-time
📉 2. Visual Indicators
Clear visual markers for entry, SL, and target prices
Price labels with detailed percentage changes
Indicators that show the current position's market status
💼 3. Portfolio Summary
Aggregate account values and exposure
Summarized P&L metrics across all positions
Risk management insights for better decision-making
Daily performance tracking to evaluate strategies
⚠️ 4. Alert System
Instant notifications for stop-loss breaches
Alerts when target prices are hit
Alerts operate for the current chart symbol
⚡ Customization Options 🎨
Show or hide specific data columns
Adjust the table's position and size for better visibility
Personalize color schemes and text styles
Switch between full portfolio view and single symbol focus
📱 How to Use 📝
Input your positions in the indicator's settings
Enable or disable specific positions dynamically
Customize display preferences to your liking
Set up alerts for proactive risk management
Monitor all your trading activities in one comprehensive dashboard
📌 Important Notes ℹ️
Compatible with any trading symbol
Updates seamlessly during market hours
Alerts are specific to the currently active chart symbol
Maximum capacity: 14 simultaneous positions
Created by: @AfnanTAjuddin
⚠️ Disclaimer ⚠️
This indicator is a tool for informational purposes only. Ensure all calculations are verified and consult a financial professional before making investment decisions.
🎯 "Stay disciplined, trade smart, and let data guide your decisions." 📊
Linear Regression Channel Screener [Daveatt]Hello traders
First and foremost, I want to extend a huge thank you to @LonesomeTheBlue for his exceptional Linear Regression Channel indicator that served as the foundation for this screener.
Original work can be found here:
Overview
This project demonstrates how to transform any open-source indicator into a powerful multi-asset screener.
The principles shown here can be applied to virtually any indicator you find interesting.
How to Transform an Indicator into a Screener
Step 1: Identify the Core Logic
First, identify the main calculations of the indicator.
In our case, it's the Linear Regression
Channel calculation:
get_channel(src, len) =>
mid = math.sum(src, len) / len
slope = ta.linreg(src, len, 0) - ta.linreg(src, len, 1)
intercept = mid - slope * math.floor(len / 2) + (1 - len % 2) / 2 * slope
endy = intercept + slope * (len - 1)
dev = 0.0
for x = 0 to len - 1 by 1
dev := dev + math.pow(src - (slope * (len - x) + intercept), 2)
dev
dev := math.sqrt(dev / len)
Step 2: Use request.security()
Pass the function to request.security() to analyze multiple assets:
= request.security(sym, timeframe.period, get_channel(src, len))
Step 3: Scale to Multiple Assets
PineScript allows up to 40 request.security() calls, letting you monitor up to 40 assets simultaneously.
Features of This Screener
The screener provides real-time trend detection for each monitored asset, giving you instant insights into market movements.
It displays each asset's position relative to its middle regression line, helping you understand price momentum.
The data is presented in a clean, organized table with color-coded trends for easy interpretation.
At its core, the screener performs trend detection based on regression slope calculations, clearly indicating whether an asset is in a bullish or bearish trend.
Each asset's price is tracked relative to its middle regression line, providing additional context about trend strength.
The color-coded visual feedback makes it easy to spot changes at a glance.
Built-in alerts notify you instantly when any asset experiences a trend change, ensuring you never miss important market moves.
Customization Tips
You can easily expand the screener by adding more symbols to the symbols array, adapting it to your watchlist.
The regression parameters can be adjusted to match your preferred trading timeframes and sensitivity.
The alert system is already configured to notify you of trend changes, but you can customize the alert messages and conditions to your needs.
Limitations
While powerful, the screener is bound by PineScript's limitation of 40 security calls, capping the maximum number of monitored assets.
Using AI to Help With Conversion
An interesting tip:
You can use AI tools to help convert single-asset indicators to screeners.
Simply provide the original code and ask for assistance in transforming it into a screener format. While the AI output might need some syntax adjustments, it can handle much of the heavy lifting in the conversion process.
Prompt (example) : " Please make a pinescript version 5 screener out of this indicator below or in attachment to scan 20 instruments "
I prefer Claude AI (Opus model) over ChatGPT for pinescript.
Conclusion
This screener transformation technique opens up endless possibilities for market analysis.
By following these steps, you can convert any indicator into a powerful multi-asset scanner, enhancing your trading toolkit significantly.
Remember: The power of a screener lies not just in monitoring multiple assets, but in applying consistent analysis across your entire watchlist in real-time.
Feel free to fork and modify this screener for your own needs.
Happy trading! 🚀📈
Daveatt
Checklist By TAZFX with Trade ScoreTrading Checklist is a customizable indicator designed for traders who want to stay disciplined and stick to their trading rules. Using this indicator, you can easily create and display your own personalized checklist of trading rules directly on your TradingView chart.
1. Customizable Settings:
• Positioning : Place the table in one of nine positions on the chart (e.g., bottom left, top right).
• Header : Modify the banner text, size, and color.
• Row Content : Define text for each row and control visibility.
• Appearance : Adjust text and background colors.
2. Checklist Table:
•Displays up to 8 rows with checkboxes (✅/❌) and custom labels for trade evaluation.
•Useful for tracking whether specific trade conditions or rules are met.
3. Trade Score Calculation:
•The Trade Score is a percentage that shows how many of your checklist items are checked compared to the total visible items.
Sticky Note Pro: Customizable Trading ChecklistStay organized and disciplined with this customizable sticky note on your TradingView chart. Perfect for traders who want to keep essential trading reminders, checklists, or notes visible while analyzing the market.
### Features:
- **Customizable Templates**: Choose from a **Trading Checklist**, **Risk Management**, or **Custom** template.
- **Section Customization**: Tailor the titles and content for up to three sections:
- 📊 **Analysis**: Track trend direction and support/resistance levels.
- 💰 **Risk Management**: Ensure proper risk management with reminders for risk percentage and stop loss settings.
- 🧠 **Psychology**: Stay disciplined with reminders to stick to your plan and avoid overtrading.
- **Dynamic Content**: Add or hide sections based on your preference, with dynamic spacing and content formatting.
- **Visual Customization**: Change text and background colors, and adjust text size and line spacing for optimal visibility.
- **Chart Integration**: The sticky note is displayed on the top-right corner of your chart and updates with the most recent bar.
### Why Use This Indicator?
This tool helps you stay on track with your trading plan, offering reminders for analysis, risk management, and trading psychology, all in one convenient place. Customize it to fit your style, and never miss a key point during your trading sessions again.
Sharpe Ratio Z-ScoreThe "Sharpe Ratio Z-Score" indicator is a powerful tool designed to measure risk-adjusted returns in financial assets. This script helps investors evaluate the performance of a security relative to its risk, using a Z-score based modification of the Sharpe Ratio. The indicator is suitable for assessing market environments and understanding periods of underperformance or overperformance relative to historical standards.
Features:
Risk Assessment and Scaling: The indicator calculates a modified version of the Sharpe Ratio
over a user-defined period. By using scaling and mean offset adjustments, it allows for better
fitting to different market conditions.
Customizable Settings:
Period Length: The number of bars used to calculate the Sharpe Ratio.
Mean Adjustment: Offset value to adjust the average return of the calculated Sharpe ratio.
Scale Factor: A multiplier for emphasizing or reducing the calculated score's impact.
Line Color: Easily customize the plot's appearance.
Visual Cues:
Plots horizontal lines and fills specific regions to visually represent significant Z-score levels.
Highlighted zones include risk thresholds, such as overbought (positive Z-scores) and oversold
(negative Z-scores) areas, using intuitive color fills:
Green for areas below -0.5 (potential buy opportunities).
Red for areas above 0.5 (potential sell opportunities).
Yellow for neutral zones between -0.5 and 0.5.
Use Cases:
Risk-Adjusted Decision Making: Understand when returns are favorable compared to risk, especially during volatile market conditions.
Timing Reversion to Mean: Use highlighted zones to identify potential reversion-to-mean scenarios.
Trend Analysis: Identify times when an asset's performance is significantly deviating from its
average risk-adjusted return.
How It Works:
The script computes the daily returns over a set period, calculates the standard deviation of
those returns, and then applies a modified Sharpe Ratio approach. The Z-score transformation
helps to visualize how far an asset's risk-adjusted return deviates from its historical average.
This "Sharpe Ratio Z-Score" indicator is well-suited for investors seeking to combine quantitative metrics with visual cues, enhancing decision-making for long and short positions while maintaining a risk-adjusted perspective.
Sharpe Ratio With Upper/Lower BandsSharpe Ratio with Upper/Lower Bands is an advanced indicator designed to measure and visualize risk-adjusted returns. The Sharpe Ratio evaluates the performance of an asset or portfolio relative to its risk, helping traders and investors gauge efficiency.
This indicator enhances the traditional Sharpe Ratio by adding dynamic upper and lower bands based on its historical mean and standard deviation. These bands provide clear visual thresholds for overperformance and underperformance, allowing users to identify when the Sharpe Ratio deviates significantly from its typical range.
It’s a valuable tool for spotting extreme risk-adjusted performance levels, optimizing entry and exit points, and maintaining a balanced risk-reward strategy.
Conditional Value at Risk (CVaR)This Pine Script implements the Conditional Value at Risk (CVaR), a risk metric that evaluates the potential losses in a financial portfolio beyond a certain confidence level, incorporating both the Value at Risk (VaR) and the expected loss given that the VaR threshold has been breached.
Key Features:
Input Parameters:
length: Defines the observation period in days (default is 252, typically used to represent the number of trading days in a year).
confidence: Specifies the confidence interval for calculating VaR and CVaR, with values between 0.5 and 0.99 (default is 0.95, indicating a 95% confidence level).
Logarithmic Returns Calculation: The script computes the logarithmic returns based on the daily closing prices, a common method to measure financial asset returns, given by:
Log Return=ln(PtPt−1)
Log Return=ln(Pt−1Pt)
where PtPt is the price at time tt, and Pt−1Pt−1 is the price at the previous time point.
VaR Calculation: Value at Risk (VaR) is estimated as the percentile of the returns array corresponding to the given confidence interval. This represents the maximum loss expected over a given time horizon under normal market conditions at the specified confidence level.
CVaR Calculation: The Conditional VaR (CVaR) is calculated as the average of the returns that fall below the VaR threshold. This represents the expected loss given that the loss has exceeded the VaR threshold.
Visualization: The script plots two key risk measures:
VaR: The maximum potential loss at the specified confidence level.
CVaR: The average of the losses beyond the VaR threshold.
The script also includes a neutral line at zero to help visualize the losses and their magnitude.
Source and Scientific Background:
The concept of Value at Risk (VaR) was popularized by J.P. Morgan in the 1990s, and it has since become a widely-used tool for risk management (Jorion, 2007). Conditional Value at Risk (CVaR), also known as Expected Shortfall, addresses the limitation of VaR by considering the severity of losses beyond the VaR threshold (Rockafellar & Uryasev, 2002). CVaR provides a more comprehensive risk measure, especially in extreme tail risk scenarios.
References:
Jorion, P. (2007). Value at Risk: The New Benchmark for Managing Financial Risk. McGraw-Hill Education.
Rockafellar, R.T., & Uryasev, S. (2002). Conditional Value-at-Risk for General Loss Distributions. Journal of Banking & Finance, 26(7), 1443–1471.