ETF / Stocks / Crypto - DCA Strategy v1Simple "benchmark" strategy for ETFs, Stocks and Crypto! Super-easy to implement for beginners, a DCA (dollar-cost-averaging) strategy means that you buy a fixed amount of an ETF / Stock / Crypto every several months. For instance, to DCA the S&P 500 (SPY), you could purchase $10,000 USD every 12 months, irrespective of the market price. Assuming the macro-economic conditions of the underlying country remain favourable, DCA strategies will result in capital gains over a period of many years, e.g. 10 years. DCA is the safest strategy that beginners can employ to make money in the markets, and all other types of strategies should be "benchmarked" against DCA; if your strategy cannot outperform DCA, then your strategy is useless.
Recommended Chart Settings:
Asset Class: ETF / Stocks / Crypto
Time Frame: H1 (Hourly) / D1 (Daily) / W1 (Weekly) / M1 (Monthly)
Necessary ETF Macro Conditions:
1. Country must have healthy demographics, good ratio of young > old
2. Country population must be increasing
3. Country must be experiencing price-inflation
Necessary Stock Conditions:
1. Growing revenue
2. Growing net income
3. Consistent net margins
4. Higher gross/net profit margin compared to its peers in the industry
5. Growing share holders equity
6. Current ratios > 1
7. Debt to equity ratio (compare to peers)
8. Debt servicing ratio < 30%
9. Wide economic moat
10. Products and services used daily, and will stay relevant for at least 1 decade
Necessary Crypto Conditions:
1. Honest founders
2. Competent technical co-founders
3. Fair or non-existent pre-mine
4. Solid marketing and PR
5. Legitimate use-cases / adoption
Default Robot Settings:
Contribution (USD): $10,000
Frequency (Months): 12
*Robot buys $10,000 worth of ETF, Stock, Crypto, regardless of the market price, every 12 months since its founding time.*
*Equity curve can be seen from the bottom panel*
Risk Warning:
This strategy is low-risk, however it assumes you have a long time horizon of at least 5 to 10 years. The longer your holding-period, the better your returns. The only thing the user has to keep-in-mind are the macro-economic conditions as stated above. If unsure, please stick to ETFs rather than buying individual stocks or cryptocurrencies.
Cari dalam skrip untuk "半导体设备ETF"
Stock ETF Tracker 2.0The Stock Sector ETF tracker with Indicators is a versatile tool designed to track the performance of sector-specific ETFs relative to the current asset. It automatically identifies the sector of the underlying symbol and displays the corresponding ETF’s price action alongside key technical indicators. This helps traders analyze sector trends and correlations in real time.
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Key Features
Automatic Sector Detection:
Fetches the sector of the current asset (e.g., "Technology" for AAPL).
Maps the sector to a user-defined ETF (default: SPDR sector ETFs) .
Technical Indicators:
Simple Moving Average (SMA): Tracks the ETF’s trend.
Bollinger Bands: Highlights volatility and potential reversals.
Donchian High (52-Week High): Identifies long-term resistance levels.
SPY Regime Filter: Red background color if SP500 is below 200 day SMA.
Customizable Inputs:
Adjust indicator parameters (length, visibility).
Override default ETFs for specific sectors.
Informative Table:
Displays the current sector and ETF symbol in the bottom-right corner.
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Input Settings
SMA Settings
SMA Length: Period for calculating the Simple Moving Average (default: 200).
Show SMA: Toggle visibility of the SMA line.
Bollinger Bands Settings
BB Length: Period for Bollinger Bands calculation (default: 20).
BB Multiplier: Standard deviation multiplier (default: 2.0).
Show Bollinger Bands: Toggle visibility of the bands.
Donchian High (52-Week High)
Daily High Length: Days used to calculate the high (default: 252, approx. 1 year).
Show High: Toggle visibility of the 52-week high line.
Sector Selections
Customize ETFs for each sector (e.g., replace XLU with another utilities ETF).
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Example Use Cases
Trend Analysis: Compare a stock’s price action to its sector ETF’s SMA for trend confirmation.
Volatility Signals: Use Bollinger Bands to spot ETF price squeezes or breakouts.
Sector Strength: Monitor if the ETF is approaching its 52-week high to gauge sector momentum.
Enjoy tracking sector trends with ease! 🚀
Stock Sector ETF with IndicatorsThe Stock Sector ETF with Indicators is a versatile tool designed to track the performance of sector-specific ETFs relative to the current asset. It automatically identifies the sector of the underlying symbol and displays the corresponding ETF’s price action alongside key technical indicators. This helps traders analyze sector trends and correlations in real time.
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Key Features
Automatic Sector Detection:
Fetches the sector of the current asset (e.g., "Technology" for AAPL).
Maps the sector to a user-defined ETF (default: SPDR sector ETFs) .
Technical Indicators:
Simple Moving Average (SMA): Tracks the ETF’s trend.
Bollinger Bands: Highlights volatility and potential reversals.
Donchian High (52-Week High): Identifies long-term resistance levels.
Customizable Inputs:
Adjust indicator parameters (length, visibility).
Override default ETFs for specific sectors.
Informative Table:
Displays the current sector and ETF symbol in the bottom-right corner.
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Input Settings
SMA Settings
SMA Length: Period for calculating the Simple Moving Average (default: 200).
Show SMA: Toggle visibility of the SMA line.
Bollinger Bands Settings
BB Length: Period for Bollinger Bands calculation (default: 20).
BB Multiplier: Standard deviation multiplier (default: 2.0).
Show Bollinger Bands: Toggle visibility of the bands.
Donchian High (52-Week High)
Daily High Length: Days used to calculate the high (default: 252, approx. 1 year).
Show High: Toggle visibility of the 52-week high line.
Sector Selections
Customize ETFs for each sector (e.g., replace XLU with another utilities ETF).
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Example Use Cases
Trend Analysis: Compare a stock’s price action to its sector ETF’s SMA for trend confirmation.
Volatility Signals: Use Bollinger Bands to spot ETF price squeezes or breakouts.
Sector Strength: Monitor if the ETF is approaching its 52-week high to gauge sector momentum.
Enjoy tracking sector trends with ease! 🚀
[MAD] BTC ETF Volume In/OutflowThe " BTC ETF Volume In/Outflows" indicator is designed to analyze and visualize the volume data of various Bitcoin Exchange-Traded Funds (ETFs) across different exchanges. This indicator helps traders and analysts observe the inflows and outflows of trading volume in a structured and comparative manner.
Features
Multi-Ticker Support: The indicator is capable of handling volume data from multiple ETFs simultaneously, making it versatile for comparative analysis.
Volume Adjustments: Provides an option to view volume data either as the number of pieces (shares) traded or as monetary flow (value traded).
Compression Factor: Includes a volume compression factor setting that helps in emphasizing smaller volume changes or smoothing out volume spikes.
Data Calculation
Volume data is processed using a custom function that adjusts the data based on user settings for piece or monetary representation and applies a logarithmic compression factor.
This processed data is then fetched for each ticker.
Visualization
Volume data is visualized on the chart using column plots where each ETF's volume data is stacked and offset to provide a clear visual representation of in/outflows. Horizontal lines indicate the zero level for reference.
Usage Scenario
This indicator is particularly useful for traders who track multiple ETFs and need to compare their volume activities simultaneously. It provides insights into market trends, potentially indicating bullish or bearish shifts based on volume inflows and outflows across different instruments.
have fun :-)
Custom ETF with Dynamic Weights & RatioHi,
Want to create your own ETF from your portfolio?
This script lets you:
Add up to 10 stocks to form an ETF.
Assign weightings to each stock.
Create a second ETF to compare with your first ETF.
Compare both ETFs to determine which performs best.
Volume Sum BTC ETFsThis volume indicator tracks the volume of these 10 bitcoin ETFS:
AMEX:GBTC, NASDAQ:IBIT, AMEX:BTCO, AMEX:ARKB, AMEX:HODL, AMEX:EZBC, NASDAQ:BRRR, AMEX:BTCW, AMEX:DEFI, AMEX:BITB
It multiplies the traded shares with the hl2 share price and then devides the volume by the bitcoin hl2 price.
You can change to usd volume in settings.
Enjoy!
Notice that historical volume comes from etfs which traded already before launch like GBTC.
Also notice that that btc trades also when tradfi markets are closed, so then the indicator will show the last available volume. Something to fix later.
ETFHoldingsLibLibrary "ETFHoldingsLib"
spy_get()
: pulls SPY ETF data
Returns: : tickers held (string array), percent ticker holding (float array), sectors (string array), percent secture positioning (float array)
qqq_get()
: pulls QQQ ETF data
Returns: : tickers held (string array), percent ticker holding (float array), sectors (string array), percent secture positioning (float array)
arkk_get()
: pulls ARKK ETF data
Returns: : tickers held (string array), percent ticker holding (float array), sectors (string array), percent secture positioning (float array)
xle_get()
: pulls XLE ETF data
Returns: : tickers held (string array), percent ticker holding (float array), sectors (string array), percent secture positioning (float array)
brk_get()
: pulls BRK ETF data
Returns: : tickers held (string array), percent ticker holding (float array), sectors (string array), percent secture positioning (float array)
ita_get()
: pulls ITA ETF data
Returns: : tickers held (string array), percent ticker holding (float array), sectors (string array), percent secture positioning (float array)
iwm_get()
: pulls IWM ETF data
Returns: : tickers held (string array), percent ticker holding (float array), sectors (string array), percent secture positioning (float array)
xlf_get()
: pulls XLF ETF data
Returns: : tickers held (string array), percent ticker holding (float array), sectors (string array), percent secture positioning (float array)
xlv_get()
: pulls XLV ETF data
Returns: : tickers held (string array), percent ticker holding (float array), sectors (string array), percent secture positioning (float array)
vnq_get()
: pulls VNQ ETF data
Returns: : tickers held (string array), percent ticker holding (float array), sectors (string array), percent secture positioning (float array)
xbi_get()
: pulls XBI ETF data
Returns: : tickers held (string array), percent ticker holding (float array), sectors (string array), percent secture positioning (float array)
blcr_get()
: pulls BLCR ETF data
Returns: : tickers held (string array), percent ticker holding (float array), sectors (string array), percent secture positioning (float array)
vgt_get()
: pulls VGT ETF data
Returns: : tickers held (string array), percent ticker holding (float array), sectors (string array), percent secture positioning (float array)
vwo_get()
: pulls VWO ETF data
Returns: : tickers held (string array), percent ticker holding (float array), sectors (string array), percent secture positioning (float array)
vig_get()
: pulls VIG ETF data
Returns: : tickers held (string array), percent ticker holding (float array), sectors (string array), percent secture positioning (float array)
vug_get()
: pulls VUG ETF data
Returns: : tickers held (string array), percent ticker holding (float array), sectors (string array), percent secture positioning (float array)
vtv_get()
: pulls VTV ETF data
Returns: : tickers held (string array), percent ticker holding (float array), sectors (string array), percent secture positioning (float array)
vea_get()
: pulls VEA ETF data
Returns: : tickers held (string array), percent ticker holding (float array), sectors (string array), percent secture positioning (float array)
Monthly Purchase Strategy with Dynamic Contract Size This trading strategy is designed to automate monthly purchases of a security, adjusting the size of each purchase based on the percentage of the portfolio's equity. The key features of this strategy include:
Monthly Purchases: The strategy buys the security on a specified day of each month, based on the user's input.
Dynamic Position Sizing: The size of each purchase is calculated as a percentage of the current equity. This allows the position size to adjust dynamically with the portfolio's performance.
Slippage and Commission Considerations: Slippage is simulated by adjusting the entry price by a set number of ticks, while commissions are factored in as fixed costs per trade.
Drawdown Calculation: The strategy tracks the highest equity value and calculates the drawdown, which is the percentage decrease from this peak equity. This helps in assessing the performance and risk of the strategy.
Benefits of the Strategy
Automated Investment: The strategy automates the investment process, reducing the need for manual trading decisions and ensuring consistent execution.
Dynamic Position Sizing: By adjusting the purchase size based on the portfolio’s equity, the strategy helps in managing risk and capitalizing on market movements proportionally to the portfolio’s performance.
Regular Investments: Investing on a regular schedule helps in averaging the purchase price of the security, which can reduce the impact of short-term volatility.
Risk Management: Monitoring drawdown helps in assessing the risk and performance of the strategy, providing insights into potential losses relative to the highest equity value.
Scientific Documentation on ETF Savings Plans
1. Dollar-Cost Averaging and Investment Behavior:
Title: "The Benefits of Dollar-Cost Averaging: A Study of Investment Behavior"
Authors: William F. Sharpe
Journal: Financial Analysts Journal, 1994
Summary: This study discusses the concept of dollar-cost averaging (DCA), which involves investing a fixed amount of money at regular intervals regardless of market conditions. The study highlights that DCA can reduce the impact of market volatility and lower the average cost of investments over time.
Reference: Sharpe, W. F. (1994). The Benefits of Dollar-Cost Averaging: A Study of Investment Behavior. Financial Analysts Journal, 50(4), 27-36.
2. ETFs and Long-Term Investment Strategies:
Title: "Exchange-Traded Funds and Their Role in Long-Term Investment Strategies"
Authors: John C. Bogle
Journal: The Journal of Portfolio Management, 2007
Summary: This paper explores the advantages of using ETFs for long-term investment strategies, emphasizing their low costs, tax efficiency, and diversification benefits. It also discusses how ETFs can be used effectively in automated investment plans like ETF savings plans.
Reference: Bogle, J. C. (2007). Exchange-Traded Funds and Their Role in Long-Term Investment Strategies. The Journal of Portfolio Management, 33(4), 14-25.
3. Risk and Return in ETF Investments:
Title: "Risk and Return Characteristics of Exchange-Traded Funds"
Authors: Eugene F. Fama and Kenneth R. French
Journal: Journal of Financial Economics, 2010
Summary: Fama and French analyze the risk and return characteristics of ETFs compared to traditional mutual funds. The study provides insights into how ETFs can be a viable option for investors seeking diversified exposure while managing risk and optimizing returns.
Reference: Fama, E. F., & French, K. R. (2010). Risk and Return Characteristics of Exchange-Traded Funds. Journal of Financial Economics, 96(2), 257-278.
4. The Impact of Automated Investment Plans:
Title: "The Impact of Automated Investment Plans on Portfolio Performance"
Authors: David G. Blanchflower and Andrew J. Oswald
Journal: Journal of Behavioral Finance, 2012
Summary: This research examines how automated investment plans, including ETF savings plans, affect portfolio performance. It highlights the benefits of automation in reducing behavioral biases and ensuring consistent investment practices.
Reference: Blanchflower, D. G., & Oswald, A. J. (2012). The Impact of Automated Investment Plans on Portfolio Performance. Journal of Behavioral Finance, 13(2), 77-89.
Summary
The "Monthly Purchase Strategy with Dynamic Contract Size and Drawdown" provides a disciplined approach to investing by automating purchases and adjusting position sizes based on portfolio equity. It leverages the benefits of dollar-cost averaging and regular investment, with risk management through drawdown monitoring. Scientific literature supports the effectiveness of ETF savings plans and automated investment strategies in optimizing returns and managing investment risk.
TPS Short Strategy by Larry ConnersThe TPS Short strategy aims to capitalize on extreme overbought conditions in an ETF by employing a scaling-in approach when certain technical indicators signal potential reversals. The strategy is designed to short the ETF when it is deemed overextended, based on the Relative Strength Index (RSI) and moving averages.
Components:
200-Day Simple Moving Average (SMA):
Purpose: Acts as a long-term trend filter. The ETF must be below its 200-day SMA to be eligible for shorting.
Rationale: The 200-day SMA is widely used to gauge the long-term trend of a security. When the price is below this moving average, it is often considered to be in a downtrend (Tushar S. Chande & Stanley Kroll, "The New Technical Trader: Boost Your Profit by Plugging Into the Latest Indicators").
2-Period RSI:
Purpose: Measures the speed and change of price movements to identify overbought conditions.
Criteria: Short 10% of the position when the 2-period RSI is above 75 for two consecutive days.
Rationale: A high RSI value (above 75) indicates that the ETF may be overbought, which could precede a price reversal (J. Welles Wilder, "New Concepts in Technical Trading Systems").
Scaling-In Mechanism:
Purpose: Gradually increase the short position as the ETF price rises beyond previous entry points.
Scaling Strategy:
20% more when the price is higher than the first entry.
30% more when the price is higher than the second entry.
40% more when the price is higher than the third entry.
Rationale: This incremental approach allows for an increased position size in a worsening trend, potentially increasing profitability if the trend continues to align with the strategy’s premise (Marty Schwartz, "Pit Bull: Lessons from Wall Street's Champion Day Trader").
Exit Conditions:
Criteria: Close all positions when the 2-period RSI drops below 30 or the 10-day SMA crosses above the 30-day SMA.
Rationale: A low RSI value (below 30) suggests that the ETF may be oversold and could be poised for a rebound, while the SMA crossover indicates a potential change in the trend (Martin J. Pring, "Technical Analysis Explained").
Risks and Considerations:
Market Risk:
The strategy assumes that the ETF will continue to decline once shorted. However, markets can be unpredictable, and price movements might not align with the strategy's expectations, especially in a volatile market (Nassim Nicholas Taleb, "The Black Swan: The Impact of the Highly Improbable").
Scaling Risks:
Scaling into a position as the price increases may increase exposure to adverse price movements. This method can amplify losses if the market moves against the position significantly before any reversal occurs.
Liquidity Risk:
Depending on the ETF’s liquidity, executing large trades in increments might affect the price and increase trading costs. It is crucial to ensure that the ETF has sufficient liquidity to handle large trades without significant slippage (James Altucher, "Trade Like a Hedge Fund").
Execution Risk:
The strategy relies on timely execution of trades based on specific conditions. Delays or errors in order execution can impact performance, especially in fast-moving markets.
Technical Indicator Limitations:
Technical indicators like RSI and SMA are based on historical data and may not always predict future price movements accurately. They can sometimes produce false signals, leading to potential losses if used in isolation (John Murphy, "Technical Analysis of the Financial Markets").
Conclusion
The TPS Short strategy utilizes a combination of long-term trend filtering, overbought conditions, and incremental shorting to potentially profit from price reversals. While the strategy has a structured approach and leverages well-known technical indicators, it is essential to be aware of the inherent risks, including market volatility, liquidity issues, and potential limitations of technical indicators. As with any trading strategy, thorough backtesting and risk management are crucial to its successful implementation.
TASC 2023.12 Growth and Value Switching System█ OVERVIEW
This script implements a rotation system for trading value and growth ETFs, as developed by Markos Katsanos and detailed in the article titled 'Growth Or Value?' in TASC's December 2023 edition of Traders' Tips . The purpose of this script is to demonstrate how short-term momentum can be employed to track market trends and provide clarity on when to switch between value and growth.
█ CONCEPTS
The central concept of the presented rotation strategy is based on the observation that the stock market undergoes cycles favoring either growth or value stocks. Consequently, the script introduces a momentum trading system that is designed to switch between value and growth equities based on prevailing market conditions. Specifically tailored for long-term index investors, the system focuses on trading Vanguard's value and growth ETFs ( VTV and VUG ) on a weekly timeframe.
To identify the ETF likely to outperform, the script uses a custom relative strength indicator applied to both VTV and VUG in comparison with an index ( SPY ). To minimize risk and drawdowns during bear markets, when both value and growth experience downtrends, the script employs the author's custom volume flow indicator (VFI) and blocks trades when its reading indicates money outflow . Positions are closed if the relative strength of the current open trade ETF falls below that of the other ETF for two consecutive weeks and is also below its moving average. Additionally, the script implements a stop-loss when the ETF is trading below its 40-week moving average, but only during bear markets.
The script plots the relative strengths of the value and growth equities along with the signals triggered by the aforementioned rules. Information about the current readings of the relative strength and volume flow indicators, along with the current open position, is displayed in a table.
█ CALCULATIONS
The script uses the request.security() function to gather price data for both equities and the reference index. Custom relative strength and volume flow indicators are calculated based on the formulas presented in the original article. By default, the script employs the same parameters for these indicators as proposed in the original article for VTV and VUG on a weekly timeframe.
Bollinger DCA v1Simple "benchmark" strategy for ETFs, Stocks and Crypto! Super-easy to implement for beginners, a BTD (buy-the-dip) strategy means that you buy a fixed amount of an ETF / Stock / Crypto every time it falls. For instance, to BTD the S&P 500 ( SPY ), you could purchase $500 USD each time the price falls. Assuming the macro-economic conditions of the underlying country remain favourable, BTD strategies will result in capital gains over a period of many years, e.g. 10 years.
Recommended Chart Settings:
Asset Class: ETF / Stocks / Crypto
Time Frame: H1 (Hourly) / D1 (Daily) / W1 (Weekly) / M1 (Monthly)
Necessary ETF Macro Conditions:
1. Country must have healthy demographics, good ratio of young > old
2. Country population must be increasing
3. Country must be experiencing price-inflation
Necessary Stock Conditions:
1. Growing revenue
2. Growing net income
3. Consistent net margins
4. Higher gross/net profit margin compared to its peers in the industry
5. Growing share holders equity
6. Current ratios > 1
7. Debt to equity ratio (compare to peers )
8. Debt servicing ratio < 30%
9. Wide economic moat
10. Products and services used daily, and will stay relevant for at least 1 decade
Necessary Crypto Conditions:
1. Honest founders
2. Competent technical co-founders
3. Fair or non-existent pre-mine
4. Solid marketing and PR
5. Legitimate use-cases / adoption
Default Robot Settings:
Contribution (USD): $500
When: Dips below lower Bollinger Band
*Robot buys $500 worth of ETF , Stock, Crypto, every time price falls below the lower Bollinger Band
*Equity curve can be seen from the bottom panel*
Risk Warning:
This strategy is low-risk, however it assumes you have a long time horizon of at least 5 to 10 years. The longer your holding-period, the better your returns. The only thing the user has to keep-in-mind are the macro-economic conditions as stated above. If unsure, please stick to ETFs rather than buying individual stocks or cryptocurrencies.
TASC 2022.08 Trading The Fear Index█ OVERVIEW
TASC's August 2022 edition of Traders' Tips includes an article by Markos Katsanos titled "Trading The Fear Index". This script implements a trading strategy called the “daily long/short trading system for volatility ETFs” presented in this article.
█ CONCEPTS
This long-term strategy aims to capitalize on stock market volatility by using exchange-traded funds (ETFs or ETNs) linked to the VIX index.
The strategy rules (see below) are based on a combination of the movement of the Cboe VIX index, the readings of the stochastic oscillator applied to the SPY ETF relative to the VIX, and a custom indicator presented in the article and called the correlation trend . Thus, they are not based on the price movement of the traded ETF itself, but rather on the movement of the VIX and of the S&P 500 index. This allows the strategy to capture most of the spikes in volatility while profiting from the long-term time decay of the traded ETFs.
█ STRATEGY RULES
Long rules
Rising volatility: The VIX should rise by more than 50% in the last 6 days.
Trend: The correlation trend of the VIX should be 0.8 or higher and also higher than yesterday's value.
VIX-SPY relative position: The 25-day and 10-day VIX stochastics should be above the 25-day and 10-day SPY stochastics respectively. In addition, the 10-day stochastic of the VIX should be above its yesterday's value.
Long positions are closed if the 10-day stochastic of the SPY rises above the 10-day stochastic of the VIX or falls below the yesterday's value.
Short rules
Declining volatility: The VIX should drop over 20% in the last 6 days and should be down during the last 3 days.
VIX threshold: The VIX should spend less than 35% of time below 15.
VIX-SPY relative position: The 10-day VIX stochastic should be below the 10-day SPY stochastic. In addition, the 10-day SPY stochastic should be higher than the yesterday's value.
Long positions are closed if the first two Long rules are triggered (Rising volatility and Trend).
The script allows you to display the readings of the indicators used in the strategy rules in the form of oscillator time series (as in the preview chart) and/or in the form of a table.
Percentage Levels by TimeframePlots the positive and negative percentage levels from a selection of timeframes and sources for any ticker. You can use this within a pullback trading system. For example, if you historically look at the average pullback of large cap stocks and ETF's, you can use this indicator to plot the levels it could pullback to for an entry to go long. It can be used as potential targets when trading a ticker short. Another use for this is to backtest the set percentage targets using TradingView's bar replay feature to see how ETF's and large cap stocks have reacted at these levels. Note: This is intended to be used at timeframes equal to higher than the chart's as it may cause re-painting issues.
Currently percentage levels are statically set to 1, 3, 5, 10, 15, 20, 25, and 30% levels above and below the chosen source (open, high, low, close). You can also display the data based on timeframes from Daily (1D) all the way up to Yearly (12M)
*Not financial advice but in my opinion the current percentage levels set (see above) are best used for ETF's and Large Cap Stocks.
Jan 2
Release Notes: Added the ability to select the historical bars to look back when plotting levels
Jan 2
Release Notes: To get a better display or proper resolution on your charts, change the view settings to "Scale Price Chart Only"
Jan 2
Release Notes: To add % labels for this indicator on the price axis, change your chart settings to include "Indicator Name Label" & "Indicator Last Value". You can find this under the Label section after hitting the gear icon in the bottom right of your chart.
Jan 2
Release Notes: Added: Custom Line Plot Extension Settings. Ideally both values should be equal to display optimal extended lines. To return to a base setting: '1' = Historical Lookback & '0' = Offset Lines. Also note this is dependent on the timeframe you are viewing on the chart.
Jan 2
Release Notes: Removed indicator from example chart that was not needed.
Jan 2
Release Notes: Updated some comments in the Pine Script
Jan 2
Release Notes: Update: Added commentary and instructions in the indicator settings to address recommended line plot settings for Stocks/ETF's vs Futures
Jan 2
Release Notes: Changed title from "Calculation Method" to "Calculation Source"
Jan 4 2021
Normal use of security() dictates that it only be used at timeframes equal to or higher than the chart's as it may cause re-painting
Bollinger Bands %B Compare VixThis imple script converts your chosen chart price and outputs it as a percentage in relation to the Vix percentage.
If price (Blue line) is higher than 0.60 and vix (Red Line) is lower than 0. 40 then there is lower volatility and this is good for buying.
If price (Blue line) is lower than 0. 40 and vix (Red Line) is higher than 0.60 then there is higher volatility and this is good for selling, exiting and cash only.
If you like risk you can enter as soon as the price and vix cross in either direction
This is my first script, please give me a lot of critique, I won't cry hahaha :)
For greater accuracy, you use these Vix products for their specific stocks/Indicies:
Apple - VXAPL
Google - VXGOG
Amazon - CBOE:VXAZN
IBM - CBOE:VXIBM
Goldman Sachs - CBOE:VXGS
NASDAQ 100 = CBOE:VXN
SP100 - CBOE:VXO
SP500 (3months) - VIX3M
XLE(energy sector) - CBOE:VXXLE
EWZ(brazil etf) - VXEWZ
EEM( emerging markets etf) - CBOE:VXEEM
EFA (MSCI ETF) - CBOE:VXEFA
FXI (Cina ETF) - CBOE:VXFXI
Stochastics + VixFix Buy/Sell SignalsThis script is designed for long-term investors using ETFs on a weekly timeframe, where catching high-probability bottoms is the goal. It combines the Stochastic Oscillator with the Williams VixFix to identify moments of extreme fear and potential reversals.
A Buy signal is triggered when:
Stochastic %K drops below 20
VixFix forms a green spike (suggesting a panic-driven market flush)
A Sell signal is triggered when:
Stochastic %K rises above 90
VixFix falls below 5 (indicating excessive complacency)
Catching tops is much harder than catching bottoms.
These Sell signals are not designed to fully exit positions. Instead, they suggest trimming a small portion of ETF holdings — simply to free up liquidity for future opportunities.
This strategy is ideal for:
Long-term ETF investors
Weekly charts
Systematic decision-making in volatile markets
Use in conjunction with macro indicators, sector rotation, and valuation frameworks for best results.
UM Dual MA with Price Bar Color change & Fill
Description
This is a dual moving average indicator with colored bars and moving averages. I wrote this indicator to keep myself on the right side of the market and trends. It plots two moving averages, (length and type of MA are user-defined) and colors the MAs green when trending higher or red when trending lower. The price bars are green when both MAs are green, red when both MAs are red, and orange when one MA is green and the other is red. The idea behind the indicator is to be extremely visual. If I am buying a red bar, I ask myself "why?" If I am selling a green bar, again, "why?"
Recommended Usage
Configure your tow favorite Moving averages. Consider long positions when one or both turn green. Scale into a position with a portion upon the first MA turning green, and then more when the second turns green. Consider scaling out when the bars are orange after an up move.
Orange bars are either areas of consolidation or prior to major turns.
You can also look for MA crossovers.
The indicator works on any timeframe and any security. I use it on daily, hourly, 2 day charts.
Default settings
The defaults are the author's preferred settings:
- 8 period WMA and 16 period WMA.
- Bars are green when both MAs are trending higher, red when both MAs are trending lower, and orange when one MA is trending higher and the other is trending lower.
Moving average types, lengths, and colors are user-configurable. Bar colors are also user-configurable.
Alerts
Alerts can be set by right-clicking the indicator and selecting the dropdown:
- Bullish Trend Both MAs turning green
- Bearish Trend Both MAs turning red
- Mixed Trend, 1 green 1 red MA
Helpful Hints:
Look for bullish areas when both MAs turn green after a sustained downtrend
Look for bearish areas when both MAs turn red
Careful in areas of orange bars, this could be a consolidation or a warning to a potential trend direction change.
Switch up your timeframes, I toggle back and forth between 1 and 2 days.
Stretch your timeframe over a lower time frame; for example, I like the 8 and 16 daily WMA. With most securities I get 16 bars with pre and post market. This translates into 128 and 256 MAs on the hourly chart. This slows down moves and color transitions for better manageability.
Author's Subjective Observations
I like the 128/256 WMA on the hourly charts for leveraged and inverse ETFs such as SPXL/SPXS, TQQQ/SQQQ, TNA/TZA. Or even the volatility ETFs/ETNS: UVXY, VXX.
Here is a one-hour chart example:
I have noticed that as volatility increases, I should begin looking at higher timeframes. This seems counterintuitive, but higher volatility increases the level of noise or swings.
I question myself when I short a green bar or buy a red bar; "Why am I doing this?" The colors help me visually stay on the right side of trend. If I am going to speculate on a market turn, at least do it when the bars are orange (MA trends differ)
My last observation is a 2-day chart of leveraged ETFs with the 8 and 16 WMAs. I frequently trade SPXL, FNGA, and TNA. If you are really dissecting this indicator,
look at a few 2-day charts. 2-day charts seem to catch the major swings nicely up and down. They also weed out the daily sudden big swings such as a panic move from economic data
or tweets. When both the MAs turn red on a 2-day chart the same day or same bar, beware; this could be a rough ride or short opportunity. I found weekly charts too long for my style but good
to review for direction. Less decisions on longer charts equate to less brain damage for myself.
These are just my thoughts, of course you do you and what suits your style best! Happy Trading.
Buy on 5% dip strategy with time adjustment
This script is a strategy called "Buy on 5% Dip Strategy with Time Adjustment 📉💡," which detects a 5% drop in price and triggers a buy signal 🔔. It also automatically closes the position once the set profit target is reached 💰, and it has additional logic to close the position if the loss exceeds 14% after holding for 230 days ⏳.
Strategy Explanation
Buy Condition: A buy signal is triggered when the price drops 5% from the highest price reached 🔻.
Take Profit: The position is closed when the price hits a 1.22x target from the average entry price 📈.
Forced Sell Condition: If the position is held for more than 230 days and the loss exceeds 14%, the position is automatically closed 🚫.
Leverage & Capital Allocation: Leverage is adjustable ⚖️, and you can set the percentage of capital allocated to each trade 💸.
Time Limits: The strategy allows you to set a start and end time ⏰ for trading, making the strategy active only within that specific period.
Code Credits and References
Credits: This script utilizes ideas and code from @QuantNomad and jangdokang for the profit table and algorithm concepts 🔧.
Sources:
Monthly Performance Table Script by QuantNomad:
ZenAndTheArtOfTrading's Script:
Strategy Performance
This strategy provides risk management through take profit and forced sell conditions and includes a performance table 📊 to track monthly and yearly results. You can compare backtest results with real-time performance to evaluate the strategy's effectiveness.
The performance numbers shown in the backtest reflect what would have happened if you had used this strategy since the launch date of the SOXL (the Direxion Daily Semiconductor Bull 3x Shares ETF) 📅. These results are not hypothetical but based on actual performance from the day of the ETF’s launch 📈.
Caution ⚠️
No Guarantee of Future Results: The results are based on historical performance from the launch of the SOXL ETF, but past performance does not guarantee future results. It’s important to approach with caution when applying it to live trading 🔍.
Risk Management: Leverage and capital allocation settings are crucial for managing risk ⚠️. Make sure to adjust these according to your risk tolerance ⚖️.
BetaBeta , also known as the Beta coefficient, is a measure that compares the volatility of an individual underlying or portfolio to the volatility of the entire market, typically represented by a market index like the S&P 500 or an investible product such as the SPY ETF (SPDR S&P 500 ETF Trust). A Beta value provides insight into how an asset's returns are expected to respond to market swings.
Interpretation of Beta Values
Beta = 1: The asset's volatility is in line with the market. If the market rises or falls, the asset is expected to move correspondingly.
Beta > 1: The asset is more volatile than the market. If the market rises or falls, the asset's price is expected to rise or fall more significantly.
Beta < 1 but > 0: The asset is less volatile than the market. It still moves in the same direction as the market but with less magnitude.
Beta = 0: The asset's returns are not correlated with the market's returns.
Beta < 0: The asset moves in the opposite direction to the market.
Example
A beta of 1.20 relative to the S&P 500 Index or SPY implies that if the S&P's return increases by 1%, the portfolio is expected to increase by 12.0%.
A beta of -0.10 relative to the S&P 500 Index or SPY implies that if the S&P's return increases by 1%, the portfolio is expected to decrease by 0.1%. In practical terms, this implies that the portfolio is expected to be predominantly 'market neutral' .
Calculation & Default Values
The Beta of an asset is calculated by dividing the covariance of the asset's returns with the market's returns by the variance of the market's returns over a certain period (standard period: 1 years, 250 trading days). Hint: It's noteworthy to mention that Beta can also be derived through linear regression analysis, although this technique is not employed in this Beta Indicator.
Formula: Beta = Covariance(Asset Returns, Market Returns) / Variance(Market Returns)
Reference Market: Essentially any reference market index or product can be used. The default reference is the SPY (SPDR S&P 500 ETF Trust), primarily due to its investable nature and broad representation of the market. However, it's crucial to note that Beta can also be calculated by comparing specific underlyings, such as two different stocks or commodities, instead of comparing an asset to the broader market. This flexibility allows for a more tailored analysis of volatility and correlation, depending on the user's specific trading or investment focus.
Look-back Period: The standard look-back period is typically 1-5 years (250-1250 trading days), but this can be adjusted based on the user's preference and the specifics of the trading strategy. For robust estimations, use at least 250 trading days.
Option Delta: An optional feature in the Beta Indicator is the ability to select a specific Delta value if options are written on the underlying asset with Deltas less than 1, providing an estimation of the beta-weighted delta of the position. It involves multiplying the beta of the underlying asset by the delta of the option. This addition allows for a more precise assessment of the underlying asset's correspondence with the overall market in case you are an options trader. The default Delta value is set to 1, representing scenarios where no options on the underlying asset are being analyzed. This default setting aligns with analyzing the direct relationship between the asset itself and the market, without the layer of complexity introduced by options.
Calculation: Simple or Log Returns: In the calculation of Beta, users have the option to choose between using simple returns or log returns for both the asset and the market. The default setting is 'Simple Returns'.
Advantages of Using Beta
Risk Management: Beta provides a clear metric for understanding and managing the risk of a portfolio in relation to market movements.
Portfolio Diversification: By knowing the beta of various assets, investors can create a balanced portfolio that aligns with their risk tolerance and investment goals.
Performance Benchmarking: Beta allows investors to compare an asset's risk-adjusted performance against the market or other benchmarks.
Beta-Weighted Deltas for Options Traders
For options traders, understanding the beta-weighted delta is crucial. It involves multiplying the beta of the underlying asset by the delta of the option. This provides a more nuanced view of the option's risk relative to the overall market. However, it's important to note that the delta of an option is dynamic, changing with the asset's price, time to expiration, and other factors.
vol_premiaThis script shows the volatility risk premium for several instruments. The premium is simply "IV30 - RV20". Although Tradingview doesn't provide options prices, CBOE publishes 30-day implied volatilities for many instruments (most of which are VIX variations). CBOE calculates these in a standard way, weighting at- and out-of-the-money IVs for options that expire in 30 days, on average. For realized volatility, I used the standard deviation of log returns. Since there are twenty trading periods in 30 calendar days, IV30 can be compared to RV20. The "premium" is the difference, which reflects market participants' expectation for how much upcoming volatility will over- or under-shoot recent volatility.
The script loads pretty slow since there are lots of symbols, so feel free to delete the ones you don't care about. Hopefully the code is straightforward enough. I won't list the meaning of every symbols here, since I might change them later, but you can type them into tradingview for data, and read about their volatility index on CBOE's website. Some of the more well-known ones are:
ES: S&P futures, which I prefer to the SPX index). Its implied volatility is VIX.
USO: the oil ETF representing WTI future prices. Its IV is OVX.
GDX: the gold miner's ETF, which is usually more volatile than gold. Its IV is VXGDX.
FXI: a china ETF, whose volatility is VXFXI.
And so on. In addition to the premium, the "percentile" column shows where this premium ranks among the previous 252 trading days. 100 = the highest premium, 0 = the lowest premium.
Coppock Curve StrategyThis strategy makes use of a not widely known technical indicator called "Coppock Curve".
The indicator is derived by taking a weighted moving average of the rate-of-change (ROC) of a market index such as the S&P 500 or a trading equivalent such as the S&P 500 SPDR ETF. For more info: (www.investopedia.com)
This strategy uses $SPY Coppock curve as a proxy to generate buy signals on other ETF's and stocks.
Buy signals are generated when the Coppock Curve crosses above zero, and sell signals are generated when it crosses below.
An optional, trailing stop loss is available, with default settings to 100% so that it does not currently affect the buy and sell signals solely generated by the Coppock Curve. But you may find adding a Trailing stop loss may improve results on certain ETF's/Stocks.
You may also change the symbol for which signals are generated for, default is $SPY.
The published example shows using this strategy on a leverage ETF $TQQQ w/ starting capital of 10k, w/ 10k per trade. Try it on other stocks such as $AAPL, $AMZN $NFLX ect... I have found it to be an effective strategy that has a favorable risk to reward profile.
Any questions, please let me know!
Short in Bollinger Band Down trend (Weekly and Daily) // © PlanTradePlanMM
// 6/14/2020
// ---------------------------------------------------
// Name: Short in Bollinger Band Down trend (Weekly and Daily)
// ---------------------------------------------------
// Key Points in this study:
// 1. Short in BB Lower band, probability of price going down is more than 50%
// 2. Short at the top 1/4 of Lower band (EMA - Lower line), Stop is EMA, tartget is Lower line; it matches risk:/reward=1:3 naturally
//
// Draw Lines:
// BB Lower : is the Target (Black line)
// BB EMA : is the initial Stop (Black line)
// ShortLine : EMA - 1/4 of (Stop-target), which matches risk:/reward=1:3
// Prepare Zone : between EMA and ShortLine
// shortPrice : Blue dot line only showing when has Short position, Which shows entry price.
// StopPrice : Black dot line only showing when has Short position, Which shows updated stop price.
//
// Add SMA50 to filter the trend. Price <= SMA, allow to short
//
// What (Condition): in BB down trend band
// When (Price action): Price cross below ShortLine;
// How (Trading Plan): Short at ShortLine;
// Initial Stop is EMA;
// Initial Target is BB Lower Line;
// FollowUp: if price moves down first, and EMA is below Short Price. Move stop to EMA, At least "make even" in this trade;
// if Price touched Short Line again and goes down, new EMA will be the updated stop
//
// Exit: 1. Initial stop -- "Stop" when down first, Close above stop
// 2. Target reached -- "TR" when down quickly, Target reached
// 3. make even -- "ME" when small down and up, Exit at Entry Price
// 4. Small Winner -- "SM" when EMA below Entry price, Exit when Close above EMA
//
// --------------
// Because there are too many flags in up trend study already, I created this down trend script separately.
// Uptrend study is good for SPY, QQQ, and strong stocks.
// Downtrend Study is good for weak ETF, stock, and (-2x, -3x) ETFs, such as FAZ, UVXY, USO, XOP, AAL, CCL
// -----------------------------------------------------------------------------------------------------------------
// Back test Weekly and daily chart for SPY, QQQ, XOP, AAL, BA, MMM, FAZ, UVXY
// The best sample is FAZ Weekly chart.
// When SPY and QQQ are good in long term up trend, these (-2x, -3x) ETFs are always going down in long term.
// Some of them are not allowed to short. I used option Put/Put spread for the short entry.
//
RSP / VOO 比值指標The RSP/VOO ratio compares the performance of the S&P 500 Equal Weight ETF (RSP) to the S&P 500 Market Cap Weighted ETF (VOO). When the ratio is falling, it indicates that large-cap stocks—especially mega-cap tech names—are outperforming the broader market. In contrast, a rising ratio suggests that smaller and mid-sized companies are catching up or leading, which may signal a healthy broadening of market participation. Investors often use this ratio to identify shifts in market leadership and assess the strength or fragility of a rally.
5DMA Optional HMA Entry📈 5DMA Optional HMA Entry Signal – Precision-Based Momentum Trigger
Category: Trend-Following / Reversal Timing / Entry Optimization
🔍 Overview:
The 5DMA Optional HMA Entry indicator is a refined price-action entry tool built for traders who rely on clean trend alignment and precise timing. This script identifies breakout-style entry points when price gains upward momentum relative to short-term moving averages — specifically the 5-day Simple Moving Average (5DMA) and an optional Hull Moving Average (HMA).
Whether you're swing trading stocks, scalping ETFs like UVXY or VXX, or looking for pullback recovery entries, this tool helps time your long entries with clarity and flexibility.
⚙️ Core Logic:
Primary Condition (Always On):
🔹 Close must be above the 5DMA – ensuring upward short-term momentum is confirmed.
Optional Condition (Toggled by User):
🔹 Close above the HMA – adds slope-responsive trend filtering for smoother setups. Enable or disable via checkbox.
Bonus Entry Filter (Optional):
🔹 Green Candle Wick Breakout – optional pattern logic that detects bullish momentum when the high pierces above both MAs, with a green body.
Reset Mechanism:
🔁 Signal resets only after price closes back below all active MAs (5DMA and HMA if enabled), reducing noise and avoiding repeated signals during chop.
🧠 Why This Works:
This indicator captures the kind of setups that professional traders look for:
Momentum crossovers without chasing late.
Mean reversion snapbacks that align with fresh bullish moves.
Avoids premature entries by requiring clear structure above moving averages.
Optional HMA filter allows adaptability: turn it off during choppy markets or range conditions, and on during trending environments.
🔔 Features:
✅ Adjustable HMA Length
✅ Enable/Disable HMA Filter
✅ Optional Green Wick Breakout Detection
✅ Visual “Buy” label plotted below qualifying bars
✅ Real-time Alert Conditions for automated trading or manual alerts
🎯 Use Cases:
VIX-based ETFs (e.g., UVXY, VXX): Catch early breakouts aligned with volatility spikes.
Growth Stocks: Time pullback entries during bullish runs.
Futures/Indices: Combine with macro levels for intraday scalps or swing setups.
Overlay on Trend Filters: Combine with RSI, MACD, or VWAP for confirmation.
🛠️ Recommended Settings:
For smooth setups in volatile names, use:
HMA Length: 20
Keep green wick filter ON
For fast momentum trades, disable the HMA filter to act on 5DMA alone.
⭐ Final Thoughts:
This script is built to serve both systematic traders and discretionary scalpers who want actionable signals without noise or lag. The toggleable HMA feature lets you adjust sensitivity depending on market conditions — a key edge in adapting to volatility cycles.
Perfect for those who value clean, non-repainting entries rooted in logical structure.
