Open Interest RSI [BackQuant]Open Interest RSI
A multi-venue open interest oscillator that aggregates OI across major derivatives exchanges, converts it to coin or USD terms, and runs an RSI-style engine on that aggregated OI so you can track positioning pressure, crowding, and mean reversion in leverage flows, not just in price.
What this is
This tool is an RSI built on top of aggregated open interest instead of price. It pulls futures OI from several major exchanges, converts it into a unified unit (COIN or USD), sums it into a single synthetic OI candle, then applies RSI and smoothing to that combined series.
You can then render that Open Interest RSI in different visual modes:
Clean line or colored line for classic oscillator-style reads.
Column-style oscillator for impulse and compression views.
Flag mode that fills between OI RSI and its EMA for trend/mean reversion blends. See:
Heatmap mode that paints the panel based on OI RSI extremes, ideal for scanning. See:
On top of that it includes:
Aggregated OI source selection (Binance, Bybit, OKX, Bitget, Kraken, HTX, Deribit).
Choice of OI units (COIN or USD).
Reference lines and OB/OS zones.
Extreme highlighting for either trend or mean reversion.
A vertical OI RSI meter that acts as a quick strength gauge.
Aggregated open interest source
Under the hood, the indicator builds a synthetic open interest candle by:
Looping over a list of supported exchanges: Binance, Bybit, OKX, Bitget, Kraken, HTX, Deribit.
Looping over multiple contract suffixes (such as USDT.P, USD.P, USDC.P, USD.PM) to capture different contract types on each venue.
Requesting OI candles from each venue + contract combination for the same underlying symbol.
Converting each OI stream into a common unit: In COIN mode, everything is normalized into coin-denominated OI. In USD mode, coin OI is multiplied by price to approximate notional OI.
Summing up open, high, low and close of OI across venues into a single aggregated OI candle.
If no valid OI is available for the current symbol across all sources, the script throws a clear runtime error so you know you are on an unsupported market.
This gives you a single, exchange-agnostic open interest curve instead of being tied to one venue. That aggregated OI is then passed into the RSI logic.
How the OI RSI is calculated
The RSI side is straightforward, but it is applied to the aggregated OI close:
Compute a base RSI of aggregated OI using the Calculation Period .
Apply a simple moving average of length Smoothing Period (SMA) to reduce noise in the raw OI RSI.
Optionally apply an EMA on top of the smoothed OI RSI as a moving average signal line.
Key parameters:
Calculation Period – base RSI length for OI.
Smoothing Period (SMA) – extra smoothing on the RSI value.
EMA Period – EMA length on the smoothed OI RSI.
The result is:
oi_rsi – raw RSI of aggregated OI.
oi_rsi_s – SMA-smoothed OI RSI.
ma – EMA of the smoothed OI RSI.
Thresholds and extremes
You control three core thresholds:
Mid Point – central reference level, typically 50.
Extreme Upper Threshold – high-level OI RSI edge (for example 80).
Extreme Lower Threshold – low-level OI RSI edge (for example 20).
These thresholds are used for:
Reference lines or OB/OS zone fills.
Heatmap gradient bounds.
Background highlighting of extremes.
The Extreme Highlighting mode controls how extremes are interpreted:
None – do nothing special in extreme regions.
Mean-Rev – background turns red on high OI RSI and green on low OI RSI, framing extremes as contrarian zones.
Trend – background turns green on high OI RSI and red on low OI RSI, framing extremes as participation zones aligned with the prevailing move.
Reference lines and OB/OS zones
You can choose:
None – clean plotting without guides.
Basic Reference Lines – mid, upper and lower thresholds as simple gray horizontals.
OB/OS Levels – filled zones between:
Upper OB: from the upper threshold to 100, colored with the short/overbought color.
Lower OS: from 0 to the lower threshold, colored with the long/oversold color.
These guides help visually anchor the OI RSI within "normal" versus "extreme" regions.
Plotting modes
The Plotting Type input controls how OI RSI is drawn. All modes share the same underlying OI and RSI logic, but emphasise different aspects of the signal.
1) Line mode
This is the classic oscillator representation:
Plots the smoothed OI RSI as a simple line using RSI Line Color and RSI Line Width .
Optionally plots the EMA overlay on the same panel.
Works well when you want standard RSI-style signals on leverage flows: crosses of the midline, divergences versus price, and so on.
2) Colored Line mode
In this mode:
The OI RSI is plotted as a line, but its color is dynamic.
If the smoothed OI RSI is above the mid point, it uses the Long/OB Color .
If it is below the mid point, it uses the Short/OS Color .
This creates an instant visual regime switch between "bullish positioning pressure" and "bearish positioning pressure", while retaining the feel of a traditional RSI line.
3) Oscillator mode
Oscillator mode renders OI RSI as vertical columns around the mid level:
The smoothed OI RSI is plotted as columns using plot.style_columns .
The histogram base is fixed at 50, so bars extend above and below the mid line.
Bar color is dynamic, using long or short colors depending on which side of the mid point the value sits.
This representation makes impulse and compression in OI flows more obvious. It is especially useful when you want to focus on how quickly OI RSI is expanding or contracting around its neutral level. See:
4) Flag mode
Flag mode turns OI RSI and its EMA into a two-line band with a filled area between them:
The smoothed OI RSI and its EMA are both plotted.
A fill is drawn between them.
The fill color flips between the long color and the short color depending on whether OI RSI is above or below its EMA.
Black outlines are added to both lines to make the band clear against any background.
This creates a "flag" style region where:
Green fills show OI RSI leading its EMA, suggesting positive positioning momentum.
Red fills show OI RSI trailing below its EMA, suggesting negative positioning momentum.
Crossovers of the two lines can be read as shifts in OI momentum regime.
Flag mode is useful if you want a more structural view that combines both the level and slope behaviour of OI RSI. See:
5) Heatmap mode
Heatmap mode recasts OI RSI as a single-row gradient instead of a line:
A single row at level 1 is plotted using column style.
The color is pulled from a gradient between the lower and upper thresholds: Near the lower threshold it approaches the short/oversold color and near the upper threshold it approaches the long/overbought color.
The EMA overlay and reference lines are disabled in this mode to keep the panel clean.
This is a very compact way to track OI RSI state at a glance, especially when stacking it alongside other indicators. See:
OI RSI vertical meter
Beyond the main plot, the script can draw a small "thermometer" table showing the current OI RSI position from 0 to 100:
The meter is a two-column table with a configurable number of rows.
Row colors form an inverted gradient: red at the top (100) and green at the bottom (0).
The script clamps OI RSI between 0 and 100 and maps it to a row index.
An arrow marker "▶" is drawn next to the row corresponding to the current OI RSI value.
0 and 100 labels are printed at the ends of the scale for orientation.
You control:
Show OI RSI Meter – turn the meter on or off.
OI RSI Blocks – number of vertical blocks (granularity).
OI RSI Meter Position – panel anchor (top/bottom, left/center/right).
The meter is particularly helpful if you keep the main plot in a small panel but still want an intuitive strength gauge.
How to read it as a market pressure gauge
Because this is an RSI built on aggregated open interest, its extremes and regimes speak to positioning pressure rather than price alone:
High OI RSI (near or above the upper threshold) indicates that open interest has been increasing aggressively relative to its recent history. This often coincides with crowded leverage and a buildup of directional pressure.
Low OI RSI (near or below the lower threshold) indicates aggressive de-leveraging or closing of positions, often associated with flushes, forced unwinds or post-liquidation clean-ups.
Values around the mid point indicate more balanced positioning flows.
You can combine this with price action:
Price up with rising OI RSI suggests fresh leverage joining the move, a more persistent trend.
Price up with falling OI RSI suggests shorts covering or longs taking profit, more fragile upside.
Price down with rising OI RSI suggests aggressive new shorts or levered selling.
Price down with falling OI RSI suggests de-leveraging and potential exhaustion of the move.
Trading applications
Trend confirmation on leverage flows
Use OI RSI to confirm or question a price trend:
In an uptrend, rising OI RSI with values above the mid point indicates supportive leverage flows.
In an uptrend, repeated failures to lift OI RSI above mid point or persistent weakness suggest less committed participation.
In a downtrend, strong OI RSI on the downside points to aggressive shorting.
Mean reversion in positioning
Use thresholds and the Mean-Rev highlight mode:
When OI RSI spends extended time above the upper threshold, the crowd is extended on one side. That can set up squeeze risk in the opposite direction.
When OI RSI has been pinned low, it suggests heavy de-leveraging. Once price stabilises, a re-risking phase is often not far away.
Background colours in Mean-Rev mode help visually identify these periods.
Regime mapping with plotting modes
Different plotting modes give different perspectives:
Heatmap mode for dashboard-style use where you just need to know "hot", "neutral" or "cold" on OI flows at a glance.
Oscillator mode for short term impulses and compression reads around the mid line. See:
Flag mode for blending level and trend of OI RSI into a single banded visual. See:
Settings overview
RSI group
Plotting Type – None, Line, Colored Line, Oscillator, Flag, Heatmap.
Calculation Period – base RSI length for OI.
Smoothing Period (SMA) – smoothing on RSI.
Moving Average group
Show EMA – toggle EMA overlay (not used in heatmap).
EMA Period – length of EMA on OI RSI.
EMA Color – colour of EMA line.
Thresholds group
Mid Point – central reference.
Extreme Upper Threshold and Extreme Lower Threshold – OB/OS thresholds.
Select Reference Lines – none, basic lines or OB/OS zone fills.
Extreme Highlighting – None, Mean-Rev, Trend.
Extra Plotting and UI
RSI Line Color and RSI Line Width .
Long/OB Color and Short/OS Color .
Show OI RSI Meter , OI RSI Blocks , OI RSI Meter Position .
Open Interest Source
OI Units – COIN or USD.
Exchange toggles: Binance, Bybit, OKX, Bitget, Kraken, HTX, Deribit.
Notes
This is a positioning and pressure tool, not a complete system. It:
Models aggregated futures open interest across multiple centralized exchanges.
Transforms that OI into an RSI-style oscillator for better comparability across regimes.
Offers several visual modes to match different workflows, from detailed analysis to compact dashboards.
Use it to understand how leverage and positioning are evolving behind the price, to gauge when the crowd is stretched, and to decide whether to lean with or against that pressure. Attach it to your existing signals, not in place of them.
Also, please check out @NoveltyTrade for the OI Aggregation logic & pulling the data source!
Here is the original script:
Fundamental-analysis
COT Index & Positions by Novatrix CapitalThis indicator visualizes the positioning of the two main groups from the CFTC COT reports: Commercials and Retail (Non-Reportables / Small Traders). Each group is displayed in two ways:
Index (0–100) – normalized net positions to identify bullish or bearish extremes (standard cycle: 26 weeks, optionally 52 weeks).
Raw Net Positions – actual long minus short positions.
Color coding on the chart:
Commercial Index: Blue
Commercial Positions: Blue
Retail Index: Red
Retail Positions: Red
Additional features:
Reference lines for neutral, overbought, and oversold levels.
Helps traders analyze market sentiment and the positioning of major participant groups.
Important notice:
Since COT data is published only once per week and the COT Index is built on cyclical multi-week analysis, the indicator is intended to be used exclusively on the weekly timeframe.
The selected cycle length (typically 26 weeks, optionally 52 weeks) determines how net positions are compared and normalized, and can influence how quickly extreme zones appear in the index lines.
CoreEdgeTrader™ Quarterly EPSVisualized Quarterly EPS, including：
EPS: Reported EPS
Std EPS: Standardized EPS
Actual: real number
QoQ change
YoY change
By @CoreEdgeTrader
Quarterly Earnings FQ & FYQuarterly Earnings FQ & FY — Financial Metrics Tables On Chart
This indicator visually presents key quarterly and annual financial metrics directly on your TradingView chart via customizable tables. It brings fundamental analysis data into your technical charts for seamless decision-making.
Core Features:
Displays two dynamic tables for Quarterly (FQ) and Annual (FY) financial data.
Metrics include EPS, Sales, P/E ratio, Operating Margin, Return on Assets (ROA), and Return on Equity (ROE).
User-selectable visibility for each metric and their Year-over-Year (YoY) and Quarter-over-Quarter (QoQ) change percentages.
Customizable table position with dropdown settings for flexible placement anywhere on the chart.
Color customization options for headers, positive and negative changes, backgrounds, and a dark mode toggle.
Indian-style comma formatting for sales values with clear display (sales figures omit plus/minus signs for readability).
Live financial data sourced from TradingView’s financial request functions, ensuring accuracy and timeliness.
Designed for traders and investors who want quick, near real-time access to fundamental performance without leaving the chart interface.
How It Works:
The indicator fetches financial data like earnings per share, revenue, and margins each quarter and year, then displays it in neatly formatted tables. Positive and negative changes in metrics are color-coded for intuitive analysis. Sales figures are formatted specifically to show clean, localized numbers without distracting signs. Users can tweak which columns to show and where the tables appear on the screen.
EPS Trendline (Fundamentals Insight by Mazhar Karimi)Overview
This indicator visualizes a company’s Earnings Per Share (EPS) data directly on the chart—pulled from TradingView’s fundamental database—and applies a dynamic linear regression trendline to highlight the long-term direction of earnings growth or decline.
It’s designed to help investors and quantitative traders quickly see how the company’s profitability (EPS) has evolved over time and whether it’s trending upward (growth), flat (stagnant), or downward (decline).
How it Works
Uses request.financial() to fetch EPS data (Diluted or Basic).
You can select whether to use TTM (Trailing Twelve Months), FQ (Fiscal Quarter), or FY (Fiscal Year) data.
The script fits a regression line (using ta.linreg) over a configurable window to visualize the underlying EPS trend.
Updates automatically when new financial data is released.
Inputs
EPS Period: Choose between FQ / FY / TTM
Use Diluted EPS: Toggle to compare Diluted vs. Basic EPS
Regression Window: Adjust how many bars are used to fit the trendline
Interpretation Tips
A rising trendline indicates earnings momentum and potential investor confidence.
A flat or declining trendline may warn of profitability slowdowns.
Combine with price action or valuation ratios (like P/E) for deeper analysis.
Works best on stocks or ETFs with fundamental data (not available for crypto or FX).
Suggestions / Use Cases
Pair with Price/Earnings ratio indicators to evaluate valuation vs. fundamentals.
Use in conjunction with earnings release events for context.
Ideal for long-term investors, swing traders, or fundamental quants tracking financial health trends.
Future Enhancements (Planned Ideas)
🔹 Option to display multiple regression lines (short-term and long-term)
🔹 Support for comparing multiple tickers’ EPS in the same pane
🔹 Integration with Net Income, Revenue, or Free Cash Flow trends
🔹 Add a “Rate of Change” signal for momentum-based EPS analysis
Quick Valuation V.1.0 (Ibo)This Pine Script indicator performs a Quick Discounted Cash Flow (DCF)-style Valuation to estimate the intrinsic value of a stock.
It calculates a projected Fair Value and a Margin of Safety based on user inputs or automatically pulled financial data from TradingView (like revenue, growth, margin, and exit P/E). It also automatically computes a Discount Rate using a modified CAPM model.
Key Features
Valuation Output: Calculates a target Fair Value and the resulting Margin of Safety.
Data Flexibility: Automatically pulls essential fundamentals (Revenue, Margins, Shares Outstanding, etc.) but allows the user to override any value (revenue, growth, P/E, shares, etc.) via the settings.
Automated Discount Rate: Calculates the Discount Rate (Cost of Equity) using the current 10-Year Real Yield and a computed or user-defined Beta.
Clear Display: Presents all input metrics, calculated values, and data sources (TradingView or User Input) in a neat table on the chart.
Crypto Flows [ETF|On-chain]The surge in Bitcoin and Ethereum spot ETFs has transformed how crypto is held and traded. By mid‑2025, U.S. spot Bitcoin ETFs already controlled roughly 1.28 million BTC, or about 6.5 percent of the circulating supply (Fosque, 2025). This accumulation has coincided with sharp price rallies and signals that regulated vehicles are absorbing a meaningful share of supply (Fosque, 2025; Wright, 2025). At the same time, on‑chain analytics show that exchange flows still influence markets: large inflows to exchanges often precede sell‑offs, whereas withdrawals to private wallets signal accumulation and reduced sell pressure (Singh, 2024; CryptoQuant, 2024). IntoTheBlock’s large‑holder inflow indicator even notes that spikes in whale buying frequently mark major bottoms (IntoTheBlock, 2022). I wanted to weave these pieces together, so I created this indicator.
Essence and logic
The script draws from two data streams: net flows into ETFs and net on‑chain flows from large holders, both scaled by the asset’s circulating market cap. ETF flows are aggregated across the ten largest INDEX:BTCUSD Bitcoin ETFs, the ten largest Ethereum INDEX:ETHUSD ETFs and the first CRYPTOCAP:SOL Solana ETF; each fund has its own checkbox and colour selection. On‑chain data uses IntoTheBlock’s large‑holder inflows and outflows, with dozens of coins available( CRYPTO:XRPUSD CRYPTOCAP:AVAX CRYPTOCAP:ADA CRYPTOCAP:LINK CRYPTO:DOGEUSD CRYPTOCAP:OTHERS ; if your coin isn’t shown in the dropdown you can manually enter its symbol. For each component, daily flows are converted into either a Z‑score or, by default, a percent‑of‑market‑cap series; users choose the weighting between ETF and on‑chain signals. These weighted series are summed into a composite, smoothed, and then two moving averages (a fast and a slow one) are applied to define bullish or bearish regimes. Because ETFs are a recent phenomenon, the early part of the composite is dominated by on‑chain flows; as ETF history lengthens, the fund‑flow component will become more influential. Trade signals are generated via moving‑average crossovers and optional dip triggers, and a trend table summarises current values and directions.
Why these components?
ETF flows reflect institutional adoption and supply absorption. Funds such as IBIT already hold about 744 000 BTC (roughly 3.3 percent of total supply), and cumulative ETF holdings have been growing faster than new coins are mined (Wright, 2025). Net inflows into these vehicles have tended to accompany rising prices and signal long‑horizon capital (Fosque, 2025). On‑chain flows, meanwhile, capture exchange liquidity dynamics. High inflows to exchanges often indicate that investors are preparing to sell, increasing tradable supply (Singh, 2024; CryptoQuant, 2024). Outflows into self‑custody suggest accumulation and reduced sell pressure, providing a bullish signal (Singh, 2024; CryptoQuant, 2024). IntoTheBlock points out that spikes in large‑holder inflows—whales moving coins into cold storage—have historically preceded price bottoms (IntoTheBlock, 2022). By weighting and standardising these flows relative to market cap, the composite aims to offer a more objective lens on risk‑on versus risk‑off regimes than price alone.
Limitations and outlook
ETFs a pretty new, so the data history is short. The list of tracked funds is currently limited to U.S. and European products; adding Asian or Canadian vehicles could provide a fuller picture. On‑chain flows can be noisy and occasionally give conflicting signals, and large‑holder data is not available for every crypto asset. The ETF and on‑chain components are also correlated through market cap, so equal weighting may amplify common trends. As macro conditions evolve and ETF redemption mechanisms change, the usefulness of fund flows could vary. I see this indicator as one tool among many, and I’m considering adding stablecoin flows, derivatives funding rates, or halving‑cycle adjustments. Suggestions are welcome.
Personal note
I’m a student who enjoys exploring the intersection of macro flows, on‑chain analytics and market psychology. This script is free to use. You can enable or disable each component, adjust weights, change the display mode and lookback, and select individual ETF tickers. If it brings you value, feel free to follow my work or reach out with feedback. I appreciate your support. Please remember that this indicator is for educational purposes and not investment advice. I built this indicator in addition to my Liquidity indicator, where I use Global M2, the yield curve, and the high-yield spread to define risk-on/risk-off regimes. If you are interested, you can find it here:
References
CryptoQuant Team. (2024). Exchange in/outflow and netflow user guide.
Fosque, J. (2025). Bitcoin ETFs pull $17.8 billion in 90 days as price surges past $118 K. The Digital Chamber.
IntoTheBlock. (2022). Large holders inflow indicator description.
Singh, O. (2024). Crypto exchange inflows and outflows explained: What they reveal about market trends. CCN.
Wright, L. (2025). Bitcoin ETFs to lock up 1.5 million BTC by New Year as supply squeeze tightens grip. CryptoSlate.
Extended CANSLIM Indicator❖ Extended CANSLIM Indicator.
The Extended CANSLIM indicator is an indicator that concentrates all the tools usually used by CANSLIM traders.
It shows a table where all the stock fundamental information is shown at once first for the last quarter and then up to 5 years back.
The fundamental data is checked against well known CANSLIM validation criteria and is shown over 4 state levels.
1. Good = Value is CANSLIM Compliant.
2. Acceptable = Value is not CANSLIM compliant but still good. value is shown with a lighter background color.
3. Warning = Value deserves special attention. Value is shown over orange background color.
3. Stop = Value is non CANSLIM compliant or indicates a stop trading condition. Value is shown over red background color.
The indicator has also a set of technical tools calculated on price or index and shown directly on the chart.
❖ Fundamental data shown in the table.
The table is arranged in 4 sets of data:
1. Table Header, showing Indicator and Company data.
2. CANSLIM.
3. 3Rs: RS Rating, Revenue and ROE.
4. Extra Data: Piotroski score, ATR, Trend Days, D to E, Avg Vol and Vol today.
Sets 3 and 4 can be hidden from the table.
❖ Indicator and Compay Data.
The table header shows, Indicator name and version.
It then displays Company Name, sector and industry, human size and its capitalization.
❖ CANSLIM Data.
Displays either genuine CANSLIM data from TradinView or custom data as best effort when that data cannot be obtained in TV.
C = EPS diluted growth, Quarterly YoY.
>= 25% = Good, >= 0% = Acceptable, < 0% = Stop
A = EPS diluted growth, Annual YoY.
>= 25% = Good, >= 0% = Acceptable, < 0% = Stop
N = New High as best effort (Cust).
Always Good
S = Float shares as best effort.
Always Good
L = One year performance relative to S&P 500 (Cust),
Positive : 0% .. 50% = Neutral, 50%+ = Leader, 80%+ = Leader+, 100%+ = Leader++
Negative : 0% .. -10% = Laggard, -10% .. -30% = Laggard+, -30%+ = Laggard++
>= 50% = Good, >= 0% = Acceptable, >= -10% Warning, < -10% = Stop
I = Accumulation/Distribution days over last 25 days as a clue for institutional support (Cust).
A delta is calculated by subtracting Distribution to Accumulation days.
> 0 = Good, = 0 = Acceptable, < 0 = Warning, < -5 = Stop
M = Market direction and exposure measured on S&500 closing between averages (Cust).
Varies from 0% Full Bear to 100% Full Bull
>= 80% = Good, >= 60% = Acceptable, >= 40% = Warning, < 40% = Stop
❖ Extra non CANSLIM Data.
RS = RS Rating.
>= 90 = Good, >= 80 = Accept, >= 50 = Warning, < 50 = Stop
Rev. = Revenue Growth Quarterly YoY.
>= 0% = Good, <0% = Stop
ROE = Return on Equity, Quarterly YoY.
>= 17% = Good, >= 0% = Acceptable, < 0% = Stop
Piotr. = Piotroski Score, www.investopedia.com (TV)
>= 7 = Good, >= 4 = Acceptable, < 4 = Stop
ATR = Average True Range over the last 20 days (Cust).
0% - 2% = Acceptable, 2% - 4% = Ideal, 4% - 6% = Warning, 5%+ = Stop.
Trend Days = Days since EMA150 is over EMA200 (Cust).
Always Good
D. to E. = Days left before Earnings. Maybe not a good idea buying just before earnings (Cust).
>= 28 = Good, >= 21 = Acceptable, >= 14 = Warning, < 14 = Stop
Avg Vol. = 50d Average Volume (Cust).
>= 100K = Good, < 100K = Acceptable
Vol. Today = Today's percentage volume compared to 50d average (Cust).
Always Good.
❖ Historical Data.
Optionally selectable historical data can be displayed for C, A, Revenue and ROE up to 20 quarters if available.
Quarterly numbers can also be displayed for A, C and Revenue.
Information can be shown in Chronological or Reverse Chronological order (default).
Increasing growth quarters are shown in white, while diminuing ones are shown in Yellow.
Transition from Losing to Profitable quarters are shown with an exclamation mark ‘!’
Finally, losing quarters are shown between parenthesis.
❖ MAs on chart.
Displays 200, 100, 50 and 20 days MAs on chart.
The MAs are also automatically scaled in the 1W time frame.
❖ New 52 Week High on chart.
A sun is shown on the chart the first time that a new 52 week high is reached.
The N cell shows a filled sun when a 52 week high is no older than a month, an lighter sun when it’s no older than a quarter or a moon otherwise.
❖ Pocket Pivots on chart.
Small triangles below the price are signaling pocket pivots.
❖ Bases on chart, formerly Darvas Boxes.
Draw bases as defined by Darvas boxes, both top or bottom of bases can be selected to be shown in order to only show resistance or support.
❖ Market exposure/direction indicator.
When charting S&P500 (SPX), Nasdaq 100 Index (NDX), Nasdaq composite (IXIC) or Dow Jownes Index (DJIA), the indicator switches to Market Exposure indicator, showing also Accumulation/Distribution days when volume information is available. This indication which varies from 0% to 100% is what is shown under the M letter in the CANSLIM table which is calculated on the S&P500.
❖ Follow Through Days indicator.
If you are an adept of the Low-cheat entry, then you will be highly interested by the Follow Through days indicator as measured in the S&P 500 and shown as diamonds on the chart.
The follow-through days are calculated on S&P500 but shown in current stock chart so you don’t need to chart the S&P 500 to know that a follow through day occurred.
Follow Through days show correctly on Daily time frame and most are also shown on the Weekly time frame as well.
They are also classified according to the market zone in which they occur:
0%-5% from peak = Pullback : FT day is not shown.
5%-10% from peak = Minor Correction : Minor FT days is shown.
10%-20% from peak = Correction : Intermediate FT days us shown
20+% from peak = Bear Market : Makor FT days is shown
❖ RS Line and Rating indicator.
A RS Line and Rating indicator can be added to the chart.
Relative Strength Rating Accuracy.
Please note that the RS Rating is not 100% accurate when compared to IBD values.
❖ Earning Line indicator.
An Earning Line indicator can be added to the chart.
❖ ATR Bands and ATR Trade calculator.
The motivation for this calculator came from my own need to enter trades on volatile stocks where the simple 7% Stop Loss rule doest not work.
It simply calculates the number of shares you can buy at any moment based on current stock price and using the lower ATR band as a stop loss.
A few words about the ATR Bands.
On this indicator the ATR bands are not drawn as a classical channel that follows the price.
The lower band is drawn as a support until it’s broken on a closing basis. It can’t be in a down trend.
The upper band is drawn as a resistance until it’s broken on a closing basis. It can’t be in an up trend.
The idea is that when price starts to fall down from a peak, it should not violate its lower band ATR and that means that we can use that level as a Stop Loss.
You must look back for the stock volatility and find out which ATR multiplier works well meaning that the ATR bands are not violated on normal pullbacks. By default, the indicator uses 5x multiplier.
❖ Extra things, visual features and default settings.
The first square cell of current quarter displays a check mark ‘V’ if the CANSLIM criteria is OK or acceptable or a cross ‘X’ otherwise.
The first square cell of historical C and Rev show respectively the count of last consecutive positive quarters.
There are different color themes from “Forest” to “Space” you can chose from to best fit your eyes.
You also have different table sizes going from “Micro” to “Huge” for better adjustment to the size of your display.
The default settings view show: Pocket Pivots, FT Days, MA50, RS Line and ATR Bands.
That's all, Enjoy!
PFA_Earnings Surprise %📌 Indicator Name: Earnings Surprise %
📖 Description:
The Earnings Surprise % indicator calculates and plots the difference between reported EPS (Earnings Per Share) and analyst consensus estimates, expressed as a percentage of the estimate. It helps traders and investors quickly gauge how much a company’s earnings have deviated from expectations on each earnings release date.
Earnings Surprise % — See how earnings stack up against expectations!
This simple yet powerful tool shows the percentage difference between reported EPS and analyst estimates directly on your chart. Positive surprises are plotted in green, negative surprises in red, so you can instantly spot earnings beats and misses. Great for combining with gap analysis, volume spikes, or technical setups around earnings dates. Works best on daily charts of stocks and ETFs with regular earnings reports.
Fundamental Analysis & Economic-Based Stock ValuationFundamental Analysis & Economic-Based Stock Valuation
The Fundamental Analysis & Economic-Based Stock Valuation is a powerful tool designed to give traders and investors a quick, comprehensive overview of a company’s financial health. This horizontal, color-coded table includes live financial data, progress indicators, and smart health insights for informed decision-making. Below are the key financial metrics included in the table:
________________________________________
1. Market Capitalization (Market Cap)
Definition: Market Cap is calculated as the total number of outstanding shares multiplied by the current stock price.
Importance: This gives investors an idea of the company’s size and valuation.
How to Use:
• Large-cap stocks (> $10B) are typically stable, established companies.
• Small- or mid-cap stocks may offer higher growth but come with more volatility.
aiTrendview Feature: Progress bars visually represent the company's size. This helps users quickly gauge whether the stock is a micro-cap, mid-cap, or large-cap investment opportunity.
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2. Earnings Yield (%)
Definition: Earnings Yield = (EPS / Price) × 100. It shows how much a company earns relative to its stock price.
Importance: It’s the inverse of the P/E ratio and is used to compare returns from equity with bond yields.
How to Use:
• A yield > 10% may indicate undervaluation.
• Lower yield (< 3%) may indicate an overpriced stock.
aiTrendview Feature: Health indicators like “STRONG”, “FAIR”, or “POOR” and a progress bar help investors assess return potential relative to risk.
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3. Price-to-Book Ratio (P/B Ratio)
Definition: P/B Ratio = Market Price / Book Value per Share.
Importance: Measures market valuation relative to the company's net assets.
How to Use:
• A ratio < 1 can mean the stock is undervalued.
• 3 might indicate overvaluation unless justified by high ROE.
aiTrendview Feature: Color-coded health markers show if the company is UNDERVALUED, FAIR, or OVERVALUED, making valuation analysis visual.
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4. Price-to-Earnings Ratio (P/E Ratio)
Definition: P/E = Price / Earnings per Share. It tells you how much investors are paying for each unit of earnings.
Importance: One of the most commonly used valuation metrics.
How to Use:
• A low P/E (< 15) might indicate undervaluation.
• High P/E (> 30) could mean overvaluation or growth expectations.
aiTrendview Feature: The health indicator ("CHEAP", "FAIR", "HIGH", "EXPENSIVE") with a visual bar helps judge sentiment and valuation instantly.
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5. Price-to-Sales Ratio (P/S Ratio)
Definition: Market Cap / Revenue. Indicates how much investors pay per dollar of sales.
Importance: Useful for valuing companies with low or negative earnings.
How to Use:
• < 2 is attractive in most industries.
• Higher ratios need to be justified by strong growth.
aiTrendview Feature: P/S-based health tags and progress bars help traders decide whether the stock is reasonably priced on revenue.
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6. EBITDA (Earnings Before Interest, Taxes, Depreciation & Amortization)
Definition: A measure of a company's core operational profitability.
Importance: Strips out non-operational costs and is used for comparative analysis.
How to Use:
• Positive EBITDA suggests financial strength.
• Compare year-over-year for growth consistency.
aiTrendview Feature: Visual score and health indicator classify profitability status as “PROFIT” or “LOSS”.
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7. Total Revenue
Definition: The total income from sales before expenses.
Importance: Indicates the scale of business operations.
How to Use:
• Rising revenue over quarters = growth.
• Compare with competitors for market share insight.
aiTrendview Feature: Categorizes revenue scale as “MICRO”, “SMALL”, “MEDIUM”, or “LARGE” – useful for gauging company tier.
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8. Net Income
Definition: Profit after all expenses, taxes, and interest.
Importance: Shows the company’s actual profitability.
How to Use:
• Positive Net Income = healthy bottom line.
• Use for EPS and ROE calculations.
aiTrendview Feature: Margin percentage + status label (“PROFIT” or “LOSS”) instantly convey financial strength.
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9. Book Value Per Share (BVPS)
Definition: Total equity divided by the number of outstanding shares.
Importance: Indicates the liquidation value per share.
How to Use:
• Compare with current market price.
• Price < BVPS can mean undervaluation.
aiTrendview Feature: Shows whether the stock is trading at “DISCOUNT” or “PREMIUM” to its actual value.
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10. Earnings Per Share (EPS)
Definition: Net income divided by outstanding shares.
Importance: Measures profitability on a per-share basis.
How to Use:
• Key input for valuation and dividend decisions.
• Positive EPS is essential for investment appeal.
aiTrendview Feature: Labeled “PROFIT” or “LOSS” and enhanced with visual status for clarity.
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11. Symbol & Exchange Info
Definition: Displays the trading symbol and exchange (e.g., NSE, NYSE).
Importance: Ensures clarity when analyzing or sharing screenshots.
How to Use:
• Useful for verifying ticker and confirming data source.
aiTrendview Feature: Clearly displayed with "LIVE" tag for credibility.
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12. Fundamental Health Score
Definition: aiTrendview computes a composite score (0–100) based on 5 core metrics: Net Income, EPS, P/E, P/B, and EBITDA.
Importance: Provides a single summary score to assess the company's overall financial strength.
How to Use:
• Use this as a filter to shortlist strong candidates.
• Score > 80 = “EXCELLENT”; 60–80 = “GOOD”; < 40 = “POOR”.
aiTrendview Feature: A professional horizontal progress bar with color-coded grade makes it visually intuitive.
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⚠️ Disclaimer from aiTrendview
The information provided in this Fundamental Analysis dashboard is for educational and informational purposes only. While the data is sourced live and computed dynamically, it should not be interpreted as investment advice. Traders and investors must do their own due diligence and consider risk appetite, macroeconomic factors, and other indicators before making any financial decisions. aiTrendview.com or its affiliates shall not be held liable for any loss arising from the use of this tool. Markets are risky — trade wisely and responsibly.
Greer Book Value Yield📘 Script Title
Greer Book Value Yield – Valuation Insight Based on Balance Sheet Strength
🧾 Description
Greer Book Value Yield is a valuation-focused indicator in the Greer Financial Toolkit, designed to evaluate how much net asset value (book value) a company provides per share relative to its current market price. This script calculates the Book Value Per Share Yield (BV%) using the formula:
Book Value Yield (%) = Book Value Per Share ÷ Stock Price × 100
This yield helps investors assess whether a stock is trading at a discount or premium to its underlying assets. It dynamically highlights when the yield is:
🟢 Above its historical average (potentially undervalued)
🔴 Below its historical average (potentially overvalued)
🔍 Use Case
Analyze valuation through asset-based metrics
Identify buy opportunities when book value yield is historically high
Combine with other scripts in the Greer Financial Toolkit:
📘 Greer Value – Tracks year-over-year growth consistency across six key metrics
📊 Greer Value Yields Dashboard – Visualizes multiple valuation-based yields
🟢 Greer BuyZone – Highlights long-term technical buy zones
🛠️ Inputs & Data
Uses Book Value Per Share (BVPS) from TradingView’s financial database (Fiscal Year)
Calculates and compares against a static average yield to assess historical valuation
Clean visual feedback via dynamic coloring and overlays
⚠️ Disclaimer
This tool is for educational and informational purposes only and should not be considered financial advice. Always conduct your own research before making investment decisions.
Greer EPS Yield📘 Script Title
Greer EPS Yield – Valuation Insight Based on Earnings Productivity
🧾 Description
Greer EPS Yield is a valuation-focused indicator from the Greer Financial Toolkit, designed to evaluate how efficiently a company generates earnings relative to its current stock price. This script calculates the Earnings Per Share Yield (EPS%), using the formula:
EPS Yield (%) = Earnings Per Share ÷ Stock Price × 100
This yield metric provides a quick snapshot of valuation through the lens of profitability per share. It dynamically highlights when the EPS yield is:
🟢 Above its historical average (potentially undervalued)
🔴 Below its historical average (potentially overvalued)
🔍 Use Case
Quickly assess valuation attractiveness based on earnings yield.
Identify potential buy opportunities when EPS% is above its long-term average.
Combine with other indicators in the Greer Financial Toolkit for a fundamentals-driven investment strategy:
📘 Greer Value – Tracks year-over-year growth consistency across six key metrics
📊 Greer Value Yields Dashboard – Visualizes valuation-based yield metrics
🟢 Greer BuyZone – Highlights long-term technical buy zones
🛠️ Inputs & Data
Uses fiscal year EPS data from TradingView’s built-in financial database.
Tracks a static average EPS Yield to compare current valuation to historical norms.
Clean, intuitive visual with automatic color coding.
⚠️ Disclaimer
This tool is for educational and informational purposes only and should not be considered financial advice. Always conduct your own research before making investment decisions.
Opening Range Breakout🧭 Overview
The Open Range Breakout (ORB) indicator is designed to capture and display the initial price range of the trading day (typically the first 15 minutes), and help traders identify breakout opportunities beyond this range. This is a popular strategy among intraday and momentum traders.
🔧 Features
📊 ORB High/Low Lines
Plots horizontal lines for the session’s high and low
🟩 Breakout Zones
Background highlights when price breaks above or below the range
🏷️ Breakout Labels
Text labels marking breakout events
🧭 Session Control
Customizable session input (default: 09:15–09:30 IST)
📍 ORB Line Labels
Text labels anchored to the ORB high and low lines (aligned right)
🔔 Alerts
Configurable alerts for breakout events
⚙️ Adjustable Settings
Show/hide background, labels, session window, etc.
⏱️ Session Logic
• The ORB range is calculated during a defined session window (default: 09:15–09:30).
• During this window, the highest high and lowest low are recorded as ORB High and ORB Low.
📈 Breakout Detection
• Breakout Above: Triggered when price crosses above the ORB High.
• Breakout Below: Triggered when price crosses below the ORB Low.
• Each breakout can trigger:
• A background highlight (green/red)
• A text label (“Breakout ↑” / “Breakout ↓”)
• An optional alert
🔔 Alerts
Two built-in alert conditions:
1. Breakout Above ORB High
• Message: "🔼 Price broke above ORB High: {{close}}"
2. Breakout Below ORB Low
• Message: "🔽 Price broke below ORB Low: {{close}}"
You can create alerts in TradingView by selecting these from the Add Alert window.
📌 Best Use Cases
• Intraday momentum trading
• Breakout and scalping strategies
• First 15-minute range traders (NSE, BSE markets)
Greer Free Cash Flow Yield✅ Title
Greer Free Cash Flow Yield (FCF%) — Long-Term Value Signal
📝 Description
The Greer Free Cash Flow Yield indicator is part of the Greer Financial Toolkit, designed to help long-term investors identify fundamentally strong and potentially undervalued companies.
📊 What It Does
Calculates Free Cash Flow Per Share (FY) from official financial reports
Divides by the current stock price to produce Free Cash Flow Yield %
Tracks a static average across all available financial years
Color-codes the yield line:
🟩 Green when above average (stronger value signal)
🟥 Red when below average (weaker value signal)
💼 Why It Matters
FCF Yield is a powerful metric that reveals how efficiently a company turns revenue into usable cash. This can be a better long-term value indicator than earnings yield or P/E ratios, especially in capital-intensive industries.
✅ Best used in combination with:
📘 Greer Value (fundamental growth score)
🟢 Greer BuyZone (technical buy zone detection)
🔍 Designed for:
Fundamental investors
Value screeners
Dividend and FCF-focused strategies
📌 This tool is for informational and educational use only. Always do your own research before investing.
MVRV Ratio [Alpha Extract]The MVRV Ratio Indicator provides valuable insights into Bitcoin market cycles by tracking the relationship between market value and realized value. This powerful on-chain metric helps traders identify potential market tops and bottoms, offering clear buy and sell signals based on historical patterns of Bitcoin valuation.
🔶 CALCULATION The indicator processes MVRV ratio data through several analytical methods:
Raw MVRV Data: Collects MVRV data directly from INTOTHEBLOCK for Bitcoin
Optional Smoothing: Applies simple moving average (SMA) to reduce noise
Status Classification: Categorizes market conditions into four distinct states
Signal Generation: Produces trading signals based on MVRV thresholds
Price Estimation: Calculates estimated realized price (Current price / MVRV ratio)
Historical Context: Compares current values to historical extremes
Formula:
MVRV Ratio = Market Value / Realized Value
Smoothed MVRV = SMA(MVRV Ratio, Smoothing Length)
Estimated Realized Price = Current Price / MVRV Ratio
Distance to Top = ((3.5 / MVRV Ratio) - 1) * 100
Distance to Bottom = ((MVRV Ratio / 0.8) - 1) * 100
🔶 DETAILS Visual Features:
MVRV Plot: Color-coded line showing current MVRV value (red for overvalued, orange for moderately overvalued, blue for fair value, teal for undervalued)
Reference Levels: Horizontal lines indicating key MVRV thresholds (3.5, 2.5, 1.0, 0.8)
Zone Highlighting: Background color changes to highlight extreme market conditions (red for potentially overvalued, blue for potentially undervalued)
Information Table: Comprehensive dashboard showing current MVRV value, market status, trading signal, price information, and historical context
Interpretation:
MVRV ≥ 3.5: Potential market top, strong sell signal
MVRV ≥ 2.5: Overvalued market, consider selling
MVRV 1.5-2.5: Neutral market conditions
MVRV 1.0-1.5: Fair value, consider buying
MVRV < 1.0: Potential market bottom, strong buy signal
🔶 EXAMPLES
Market Top Identification: When MVRV ratio exceeds 3.5, the indicator signals potential market tops, highlighting periods where Bitcoin may be significantly overvalued.
Example: During bull market peaks, MVRV exceeding 3.5 has historically preceded major corrections, helping traders time their exits.
Bottom Detection: MVRV values below 1.0, especially approaching 0.8, have historically marked excellent buying opportunities.
Example: During bear market bottoms, MVRV falling below 1.0 has identified the most profitable entry points for long-term Bitcoin accumulation.
Tracking Market Cycles: The indicator provides a clear visualization of Bitcoin's market cycles from undervalued to overvalued states.
Example: Following the progression of MVRV from below 1.0 through fair value and eventually to overvalued territory helps traders position themselves appropriately throughout Bitcoin's market cycle.
Realized Price Support: The estimated realized price often acts as a significant
support/resistance level during market transitions.
Example: During corrections, price often finds support near the realized price level calculated by the indicator, providing potential entry points.
🔶 SETTINGS
Customization Options:
Smoothing: Toggle smoothing option and adjust smoothing length (1-50)
Table Display: Show/hide the information table
Table Position: Choose between top right, top left, bottom right, or bottom left positions
Visual Elements: All plots, lines, and background highlights can be customized for color and style
The MVRV Ratio Indicator provides traders with a powerful on-chain metric to identify potential market tops and bottoms in Bitcoin. By tracking the relationship between market value and realized value, this indicator helps identify periods of overvaluation and undervaluation, offering clear buy and sell signals based on historical patterns. The comprehensive information table delivers valuable context about current market conditions, helping traders make more informed decisions about market positioning throughout Bitcoin's cyclical patterns.
FA Dashboard: Valuation, Profitability & SolvencyFundamental Analysis Dashboard: A Multi-Dimensional View of Company Quality
This script presents a structured and customizable dashboard for evaluating a company’s fundamentals across three key dimensions: Valuation, Profitability, and Solvency & Liquidity.
Unlike basic fundamental overlays, this dashboard consolidates multiple financial indicators into visual tables that update dynamically and are grouped by category. Each ratio is compared against configurable thresholds, helping traders quickly assess whether a company meets certain value investing criteria. The tables use color-coded checkmarks and fail marks (✔️ / ❌) to visually signal pass/fail evaluations.
▶️ Key Features
Valuation Ratios:
Earnings Yield: EBIT / EV
EV / EBIT and EV / FCF: Enterprise value metrics for profitability
Price-to-Book, Free Cash Flow Yield, PEG Ratio
Profitability Ratios:
Return on Invested Capital (ROIC), ROE, Operating, Net & Gross Margins, Revenue Growth
Solvency & Liquidity Ratios:
Debt to Equity, Debt to EBITDA, Current Ratio, Quick Ratio, Altman Z-Score
Each of these metrics is calculated using request.financial() and can be viewed using either annual (FY) or quarterly (FQ) data, depending on user preference.
🧠 How to Use
Add the script to any stock chart.
Select your preferred data period (FY or FQ).
Adjust thresholds if desired to match your personal investing strategy.
Review the visual dashboard to see which metrics the company passes or fails.
💡 Why It’s Useful
This tool is ideal for traders or long-term investors looking to filter stocks using fundamental criteria. It draws inspiration from principles used by Benjamin Graham, Warren Buffett, and Joel Greenblatt, offering a fast and informative way to screen quality businesses.
This is not a repackaged built-in or autogenerated script. It’s a custom-built, interactive tool tailored for fundamental analysis using official financial data provided via Pine Script’s request.financial().
TradFi Fundamentals: Momentum Trading with Macroeconomic DataIntroduction
This indicator combines traditional price momentum with key macroeconomic data. By retrieving GDP, inflation, unemployment, and interest rates using security calls, the script automatically adapts to the latest economic data. The goal is to blend technical analysis with fundamental insights to generate a more robust momentum signal.
Original Research Paper by Mohit Apte, B. Tech Scholar, Department of Computer Science and Engineering, COEP Technological University, Pune, India
Link to paper
Explanation
Price Momentum Calculation:
The indicator computes price momentum as the percentage change in price over a configurable lookback period (default is 50 days). This raw momentum is then normalized using a rolling simple moving average and standard deviation over a defined period (default 200 days) to ensure comparability with the economic indicators.
Fetching and Normalizing Economic Data:
Instead of manually inputting economic values, the script uses TradingView’s security function to retrieve:
GDP from ticker "GDP"
Inflation (CPI) from ticker "USCCPI"
Unemployment rate from ticker "UNRATE"
Interest rates from ticker "USINTR"
Each series is normalized over a configurable normalization period (default 200 days) by subtracting its moving average and dividing by its standard deviation. This standardization converts each economic indicator into a z-score for direct integration into the momentum score.
Combined Momentum Score:
The normalized price momentum and economic indicators are each multiplied by user-defined weights (default: 50% price momentum, 20% GDP, and 10% each for inflation, unemployment, and interest rates). The weighted components are then summed to form a comprehensive momentum score. A horizontal zero line is plotted for reference.
Trading Signals:
Buy signals are generated when the combined momentum score crosses above zero, and sell signals occur when it crosses below zero. Visual markers are added to the chart to assist with trade timing, and alert conditions are provided for automated notifications.
Settings
Price Momentum Lookback: Defines the period (in days) used to compute the raw price momentum.
Normalization Period for Price Momentum: Sets the window over which the price momentum is normalized.
Normalization Period for Economic Data: Sets the window over which each macroeconomic series is normalized.
Weights: Adjust the influence of each component (price momentum, GDP, inflation, unemployment, and interest rate) on the overall momentum score.
Conclusion
This implementation leverages TradingView’s economic data feeds to integrate real-time macroeconomic data into a momentum trading strategy. By normalizing and weighting both technical and economic inputs, the indicator offers traders a more holistic view of market conditions. The enhanced momentum signal provides additional context to traditional momentum analysis, potentially leading to more informed trading decisions and improved risk management.
The next script I release will be an improved version of this that I have added my own flavor to, improving the signals.
Dollar Cost Averaging (DCA) | FractalystWhat's the purpose of this strategy?
The purpose of dollar cost averaging (DCA) is to grow investments over time using a disciplined, methodical approach used by many top institutions like MicroStrategy and other institutions.
Here's how it functions:
Dollar Cost Averaging (DCA): This technique involves investing a set amount of money regularly, regardless of market conditions. It helps to mitigate the risk of investing a large sum at a peak price by spreading out your investment, thus potentially lowering your average cost per share over time.
Regular Contributions: By adding money to your investments on a pre-determined frequency and dollar amount defined by the user, you take advantage of compounding. The script will remind you to contribute based on your chosen schedule, which can be weekly, bi-weekly, monthly, quarterly, or yearly. This systematic approach ensures that your returns can earn their own returns, much like interest on savings but potentially at a higher rate.
Technical Analysis: The strategy employs a market trend ratio to gauge market sentiment. It calculates the ratio of bullish vs bearish breakouts across various timeframes, assigning this ratio a percentage-based score to determine the directional bias. Once this score exceeds a user-selected percentage, the strategy looks to take buy entries, signaling a favorable time for investment based on current market trends.
Fundamental Analysis: This aspect looks at the health of the economy and companies within it to determine bullish market conditions. Specifically, we consider:
Specifically, it considers:
Interest Rate: High interest rates can affect borrowing costs, potentially slowing down economic growth or making stocks less attractive compared to fixed income.
Inflation Rate: Inflation erodes purchasing power, but moderate inflation can be a sign of a healthy economy. We look for investments that might benefit from or withstand inflation.
GDP Rate: GDP growth indicates the overall health of the economy; we aim to invest in sectors poised to grow with the economy.
Unemployment Rate: Lower unemployment typically signals consumer confidence and spending power, which can boost certain sectors.
By integrating these elements, the strategy aims to:
Reduce Investment Volatility: By spreading out your investments, you're less impacted by short-term market swings.
Enhance Growth Potential: Using both technical and fundamental filters helps in choosing investments that are more likely to appreciate over time.
Manage Risk: The strategy aims to balance the risk of market timing by investing consistently and choosing assets wisely based on both economic data and market conditions.
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What are Regular Contributions in this strategy?
Regular Contributions involve adding money to your investments on a pre-determined frequency and dollar amount defined by the user. The script will remind you to contribute based on your chosen schedule, which can be weekly, bi-weekly, monthly, quarterly, or yearly. This systematic approach ensures that your returns can earn their own returns, much like interest on savings but potentially at a higher rate.
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How do regular contributions enhance compounding and reduce timing risk?
Enhances Compounding: Regular contributions leverage the power of compounding, where returns on investments can generate their own returns, potentially leading to exponential growth over time.
Reduces Timing Risk: By investing regularly, the strategy minimizes the risk associated with trying to time the market, spreading out the investment cost over time and potentially reducing the impact of volatility.
Automated Reminders: The script reminds users to make contributions based on their chosen schedule, ensuring consistency and discipline in investment practices, which is crucial for long-term success.
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How does the strategy integrate technical and fundamental analysis for investors?
A: The strategy combines technical and fundamental analysis in the following manner:
Technical Analysis: It uses a market trend ratio to determine the directional bias by calculating the ratio of bullish vs bearish breakouts. Once this ratio exceeds a user-selected percentage threshold, the strategy signals to take buy entries, optimizing the timing within the given timeframe(s).
Fundamental Analysis: This aspect assesses the broader economic environment to identify sectors or assets that are likely to benefit from current economic conditions. By understanding these fundamentals, the strategy ensures investments are made in assets with strong growth potential.
This integration allows the strategy to select investments that are both technically favorable for entry and fundamentally sound, providing a comprehensive approach to investment decisions in the crypto, stock, and commodities markets.
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How does the strategy identify market structure? What are the underlying calculations?
Q: How does the strategy identify market structure?
A: The strategy identifies market structure by utilizing an efficient logic with for loops to pinpoint the first swing candle that features a pivot of 2. This marks the beginning of the break of structure, where the market's previous trend or pattern is considered invalidated or changed.
What are the underlying calculations for identifying market structure?
A: The underlying calculations involve:
Identifying Swing Points: The strategy looks for swing highs (marked with blue Xs) and swing lows (marked with red Xs). A swing high is identified when a candle's high is higher than the highs of the candles before and after it. Conversely, a swing low is when a candle's low is lower than the lows of the candles before and after it.
Break of Structure (BOS):
Bullish BOS: This occurs when the price breaks above the swing high level of the previous structure, indicating a potential shift to a bullish trend.
Bearish BOS: This happens when the price breaks below the swing low level of the previous structure, signaling a potential shift to a bearish trend.
Structural Liquidity and Invalidation:
Structural Liquidity: After a break of structure, liquidity levels are updated to the first swing high in a bullish BOS or the first swing low in a bearish BOS.
Structural Invalidation: If the price moves back to the level of the first swing low before the bullish BOS or the first swing high before the bearish BOS, it invalidates the break of structure, suggesting a potential reversal or continuation of the previous trend.
This method provides users with a technical approach to filter market regimes, offering an advantage by minimizing the risk of overfitting to historical data, which is often a concern with traditional indicators like moving averages.
By focusing on identifying pivotal swing points and the subsequent breaks of structure, the strategy maintains a balance between sensitivity to market changes and robustness against historical data anomalies, ensuring a more adaptable and potentially more reliable market analysis tool.
What entry criteria are used in this script?
The script uses two entry models for trading decisions: BreakOut and Fractal.
Underlying Calculations:
Breakout: The script records the most recent swing high by storing it in a variable. When the price closes above this recorded level, and all other predefined conditions are satisfied, the script triggers a breakout entry. This approach is considered conservative because it waits for the price to confirm a breakout above the previous high before entering a trade. As shown in the image, as soon as the price closes above the new candle (first tick), the long entry gets taken. The stop-loss is initially set and then moved to break-even once the price moves in favor of the trade.
Fractal: This method involves identifying a swing low with a period of 2, which means it looks for a low point where the price is lower than the two candles before and after it. Once this pattern is detected, the script executes the trade. This is an aggressive approach since it doesn't wait for further price confirmation. In the image, this is represented by the 'Fractal 2' label where the script identifies and acts on the swing low pattern.
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How does the script calculate trend score? What are the underlying calculations?
Market Trend Ratio: The script calculates the ratio of bullish to bearish breakouts. This involves:
Counting Bullish Breakouts: A bullish breakout is counted when the price breaks above a recent swing high (as identified in the strategy's market structure analysis).
Counting Bearish Breakouts: A bearish breakout is counted when the price breaks below a recent swing low.
Percentage-Based Score: This ratio is then converted into a percentage-based score:
For example, if there are 10 bullish breakouts and 5 bearish breakouts in a given timeframe, the ratio would be 10:5 or 2:1. This could be translated into a score where 66.67% (10/(10+5) * 100) represents the bullish trend strength.
The score might be calculated as (Number of Bullish Breakouts / Total Breakouts) * 100.
User-Defined Threshold: The strategy uses this score to determine when to take buy entries. If the trend score exceeds a user-defined percentage threshold, it indicates a strong enough bullish trend to justify a buy entry. For instance, if the user sets the threshold at 60%, the script would look for a buy entry when the trend score is above this level.
Timeframe Consideration: The calculations are performed across the timeframes specified by the user, ensuring the trend score reflects the market's behavior over different periods, which could be daily, weekly, or any other relevant timeframe.
This method provides a quantitative measure of market trend strength, helping to make informed decisions based on the balance between bullish and bearish market movements.
What type of stop-loss identification method are used in this strategy?
This strategy employs two types of stop-loss methods: Initial Stop-loss and Trailing Stop-Loss.
Underlying Calculations:
Initial Stop-loss:
ATR Based: The strategy uses the Average True Range (ATR) to set an initial stop-loss, which helps in accounting for market volatility without predicting price direction.
Calculation:
- First, the True Range (TR) is calculated for each period, which is the greatest of:
- Current Period High - Current Period Low
- Absolute Value of Current Period High - Previous Period Close
- Absolute Value of Current Period Low - Previous Period Close
- The ATR is then the moving average of these TR values over a specified period, typically 14 periods by default. This ATR value can be used to set the stop-loss at a distance from the entry price that reflects the current market volatility.
Swing Low Based:
For this method, the stop-loss is set based on the most recent swing low identified in the market structure analysis. This approach uses the lowest point of the recent price action as a reference for setting the stop-loss.
Trailing Stop-Loss:
The strategy uses structural liquidity and structural invalidation levels across multiple timeframes to adjust the stop-loss once the trade is profitable. This method involves:
Detecting Structural Liquidity: After a break of structure, the liquidity levels are updated to the first swing high in a bullish scenario or the first swing low in a bearish scenario. These levels serve as potential areas where the price might find support or resistance, allowing the stop-loss to trail the price movement.
Detecting Structural Invalidation: If the price returns to the level of the first swing low before a bullish break of structure or the first swing high before a bearish break of structure, it suggests the trend might be reversing or invalidating, prompting the adjustment of the stop-loss to lock in profits or minimize losses.
By using these methods, the strategy dynamically adjusts the initial stop-loss based on market volatility, helping to protect against adverse price movements while allowing for enough room for trades to develop. The ATR-based stop-loss adapts to the current market conditions by considering the volatility, ensuring that the stop-loss is not too tight during volatile periods, which could lead to premature exits, nor too loose during calm markets, which might result in larger losses. Similarly, the swing low based stop-loss provides a logical exit point if the market structure changes unfavorably.
Each market behaves differently across various timeframes, and it is essential to test different parameters and optimizations to find out which trailing stop-loss method gives you the desired results and performance. This involves backtesting the strategy with different settings for the ATR period, the distance from the swing low, and how the trailing stop-loss reacts to structural liquidity and invalidation levels.
Through this process, you can tailor the strategy to perform optimally in different market environments, ensuring that the stop-loss mechanism supports the trade's longevity while safeguarding against significant drawdowns.
What type of break-even and take profit identification methods are used in this strategy? What are the underlying calculations?
For Break-Even:
Percentage (%) Based:
Moves the initial stop-loss to the entry price when the price reaches a certain percentage above the entry.
Calculation:
Break-even level = Entry Price * (1 + Percentage / 100)
Example:
If the entry price is $100 and the break-even percentage is 5%, the break-even level is $100 * 1.05 = $105.
Risk-to-Reward (RR) Based:
Moves the initial stop-loss to the entry price when the price reaches a certain RR ratio.
Calculation:
Break-even level = Entry Price + (Initial Risk * RR Ratio)
For TP
- You can choose to set a take profit level at which your position gets fully closed.
- Similar to break-even, you can select either a percentage (%) or risk-to-reward (RR) based take profit level, allowing you to set your TP1 level as a percentage amount above the entry price or based on RR.
What's the day filter Filter, what does it do?
The day filter allows users to customize the session time and choose the specific days they want to include in the strategy session. This helps traders tailor their strategies to particular trading sessions or days of the week when they believe the market conditions are more favorable for their trading style.
Customize Session Time:
Users can define the start and end times for the trading session.
This allows the strategy to only consider trades within the specified time window, focusing on periods of higher market activity or preferred trading hours.
Select Days:
Users can select which days of the week to include in the strategy.
This feature is useful for excluding days with historically lower volatility or unfavorable trading conditions (e.g., Mondays or Fridays).
Benefits:
Focus on Optimal Trading Periods:
By customizing session times and days, traders can focus on periods when the market is more likely to present profitable opportunities.
Avoid Unfavorable Conditions:
Excluding specific days or times can help avoid trading during periods of low liquidity or high unpredictability, such as major news events or holidays.
What tables are available in this script?
- Summary: Provides a general overview, displaying key performance parameters such as Net Profit, Profit Factor, Max Drawdown, Average Trade, Closed Trades and more.
Total Commission: Displays the cumulative commissions incurred from all trades executed within the selected backtesting window. This value is derived by summing the commission fees for each trade on your chart.
Average Commission: Represents the average commission per trade, calculated by dividing the Total Commission by the total number of closed trades. This metric is crucial for assessing the impact of trading costs on overall profitability.
Avg Trade: The sum of money gained or lost by the average trade generated by a strategy. Calculated by dividing the Net Profit by the overall number of closed trades. An important value since it must be large enough to cover the commission and slippage costs of trading the strategy and still bring a profit.
MaxDD: Displays the largest drawdown of losses, i.e., the maximum possible loss that the strategy could have incurred among all of the trades it has made. This value is calculated separately for every bar that the strategy spends with an open position.
Profit Factor: The amount of money a trading strategy made for every unit of money it lost (in the selected currency). This value is calculated by dividing gross profits by gross losses.
Avg RR: This is calculated by dividing the average winning trade by the average losing trade. This field is not a very meaningful value by itself because it does not take into account the ratio of the number of winning vs losing trades, and strategies can have different approaches to profitability. A strategy may trade at every possibility in order to capture many small profits, yet have an average losing trade greater than the average winning trade. The higher this value is, the better, but it should be considered together with the percentage of winning trades and the net profit.
Winrate: The percentage of winning trades generated by a strategy. Calculated by dividing the number of winning trades by the total number of closed trades generated by a strategy. Percent profitable is not a very reliable measure by itself. A strategy could have many small winning trades, making the percent profitable high with a small average winning trade, or a few big winning trades accounting for a low percent profitable and a big average winning trade. Most mean-reversion successful strategies have a percent profitability of 40-80% but are profitable due to risk management control.
BE Trades: Number of break-even trades, excluding commission/slippage.
Losing Trades: The total number of losing trades generated by the strategy.
Winning Trades: The total number of winning trades generated by the strategy.
Total Trades: Total number of taken traders visible your charts.
Net Profit: The overall profit or loss (in the selected currency) achieved by the trading strategy in the test period. The value is the sum of all values from the Profit column (on the List of Trades tab), taking into account the sign.
- Monthly: Displays performance data on a month-by-month basis, allowing users to analyze performance trends over each month and year.
- Weekly: Displays performance data on a week-by-week basis, helping users to understand weekly performance variations.
- UI Table: A user-friendly table that allows users to view and save the selected strategy parameters from user inputs. This table enables easy access to key settings and configurations, providing a straightforward solution for saving strategy parameters by simply taking a screenshot with Alt + S or ⌥ + S.
User-input styles and customizations:
Please note that all background colors in the style are disabled by default to enhance visualization.
How to Use This Strategy to Create a Profitable Edge and Systems?
Choose Your Strategy mode:
- Decide whether you are creating an investing strategy or a trading strategy.
Select a Market:
- Choose a one-sided market such as stocks, indices, or cryptocurrencies.
Historical Data:
- Ensure the historical data covers at least 10 years of price action for robust backtesting.
Timeframe Selection:
- Choose the timeframe you are comfortable trading with. It is strongly recommended to use a timeframe above 15 minutes to minimize the impact of commissions/slippage on your profits.
Set Commission and Slippage:
- Properly set the commission and slippage in the strategy properties according to your broker/prop firm specifications.
Parameter Optimization:
- Use trial and error to test different parameters until you find the performance results you are looking for in the summary table or, preferably, through deep backtesting using the strategy tester.
Trade Count:
- Ensure the number of trades is 200 or more; the higher, the better for statistical significance.
Positive Average Trade:
- Make sure the average trade is above zero.
(An important value since it must be large enough to cover the commission and slippage costs of trading the strategy and still bring a profit.)
Performance Metrics:
- Look for a high profit factor, and net profit with minimum drawdown.
- Ideally, aim for a drawdown under 20-30%, depending on your risk tolerance.
Refinement and Optimization:
- Try out different markets and timeframes.
- Continue working on refining your edge using the available filters and components to further optimize your strategy.
What makes this strategy original?
Incorporation of Fundamental Analysis:
This strategy integrates fundamental analysis by considering key economic indicators such as interest rates, inflation, GDP growth, and unemployment rates. These fundamentals help in assessing the broader economic health, which in turn influences sector performance and market trends. By understanding these economic conditions, the strategy can identify sectors or assets that are likely to thrive, ensuring investments are made in environments conducive to growth. This approach allows for a more informed investment decision, aligning technical entries with fundamentally strong market conditions, thus potentially enhancing the strategy's effectiveness over time.
Technical Analysis Without Classical Methods:
The strategy's technical analysis diverges from traditional methods like moving averages by focusing on market structure through a trend score system.
Instead of using lagging indicators, it employs a real-time analysis of market trends by calculating the ratio of bullish to bearish breakouts. This provides several benefits:
Immediate Market Sentiment: The trend score system reacts more dynamically to current market conditions, offering insights into the market's immediate sentiment rather than historical trends, which can often lag behind real-time changes.
Reduced Overfitting: By not relying on moving averages or similar classical indicators, the strategy avoids the common pitfall of overfitting to historical data, which can lead to poor performance in new market conditions. The trend score provides a fresh perspective on market direction, potentially leading to more robust trading signals.
Clear Entry Signals: With the trend score, entry decisions are based on a clear percentage threshold, making the strategy's decision-making process straightforward and less subjective than interpreting moving average crossovers or similar signals.
Regular Contributions and Reminders:
The strategy encourages regular investments through a system of predefined frequency and amount, which could be weekly, bi-weekly, monthly, quarterly, or yearly. This systematic approach:
Enhances Compounding: Regular contributions leverage the power of compounding, where returns on investments can generate their own returns, potentially leading to exponential growth over time.
Reduces Timing Risk: By investing regularly, the strategy minimizes the risk associated with trying to time the market, spreading out the investment cost over time and potentially reducing the impact of volatility.
Automated Reminders: The script reminds users to make contributions based on their chosen schedule, ensuring consistency and discipline in investment practices, which is crucial for long-term success.
Long-Term Wealth Building:
Focused on long-term wealth accumulation, this strategy:
Promotes Patience and Discipline: By emphasizing regular contributions and a disciplined approach to both entry and risk management, it aligns with the principles of long-term investing, discouraging impulsive decisions based on short-term market fluctuations.
Diversification Across Asset Classes: Operating across crypto, stocks, and commodities, the strategy provides diversification, which is a key component of long-term wealth building, reducing risk through varied exposure.
Growth Over Time: The strategy's design to work with the market's natural growth cycles, supported by fundamental analysis, aims for sustainable growth rather than quick profits, aligning with the goals of investors looking to build wealth over decades.
This comprehensive approach, combining fundamental insights, innovative technical analysis, disciplined investment habits, and a focus on long-term growth, offers a unique and potentially effective pathway for investors seeking to build wealth steadily over time.
Terms and Conditions | Disclaimer
Our charting tools are provided for informational and educational purposes only and should not be construed as financial, investment, or trading advice. They are not intended to forecast market movements or offer specific recommendations. Users should understand that past performance does not guarantee future results and should not base financial decisions solely on historical data.
Built-in components, features, and functionalities of our charting tools are the intellectual property of @Fractalyst Unauthorized use, reproduction, or distribution of these proprietary elements is prohibited.
- By continuing to use our charting tools, the user acknowledges and accepts the Terms and Conditions outlined in this legal disclaimer and agrees to respect our intellectual property rights and comply with all applicable laws and regulations.
Blockchain Fundamentals: Liquidity & BTC YoYLiquidity & BTC YoY Indicator
Overview:
This indicator calculates the Year-over-Year (YoY) percentage change for two critical metrics: a custom Liquidity Index and Bitcoin's price. The Liquidity Index is derived from a blend of economic and forex data representing the M2 money supply, while the BTC price is obtained from a reliable market source. A dedicated limit(length) function is implemented to handle limited historical data, ensuring that the YoY calculations are available immediately—even when the chart's history is short.
Features Breakdown:
1. Limited Historical Data Workaround
- Functionality: limit(length) The function dynamically adjusts the lookback period when there isn’t enough historical data. This prevents delays in displaying YoY metrics at the beginning of the chart.
2. Liquidity Calculation
- Data Sources: Combines multiple data streams:
USM2, ECONOMICS:CNM2, USDCNY, ECONOMICS:JPM2, USDJPY, ECONOMICS:EUM2, USDEUR
- Formula:
Liquidity Index = USM2 + (CNM2 / USDCNY) + (JPM2 / USDJPY) + (EUM2 / USDEUR)
[b3. Bitcoin Price Calculation
- Data Source: Retrieves Bitcoin's price from BITSTAMP:BTCUSD on the user-selected timeframe for its historical length.
4. Year-over-Year (YoY) Percent Change Calculation
- Methodology:
- The indicator uses a custom function, to autodetect the proper number of bars, based on the selected timeframe.
- It then compares the current value to that from one year ago for both the Liquidity Index and BTC price, calculating the YoY percentage change.
5. Visual Presentation
- Plotting:
- The YoY percentage changes for Liquidity (plotted in blue) and BTC price (plotted in orange) are clearly displayed.
- A horizontal zero line is added for visual alignment, making it easier to compare the two copies of the metric. You add one copy and only display the BTC YoY. Then you add another copy and only display the M2 YoY.
-The zero lines are then used to align the scripts to each other by interposing them. You scale each chart the way you like, then move each copy individually to align both zero lines on top of each other.
This indicator is ideal for analysts and investors looking to monitor macroeconomic liquidity trends alongside Bitcoin's performance, providing immediate insights.
Blockchain Fundamentals: Global LiquidityGlobal Liquidity Indicator Overview
This indicator provides a comprehensive technical analysis of liquidity trends by deriving a Global Liquidity metric from multiple data sources. It applies a suite of technical indicators directly on this liquidity measure, rather than on price data. When this metric is expanding Bitcoin and crypto tends to bullish conditions.
Features:
1. Global Liquidity Calculation
Data Integration: Combines multiple market data sources using a ratio-based formula to produce a unique liquidity measure.
Custom Metric: This liquidity metric serves as the foundational input for further technical analysis.
2. Timeframe Customization
User-Selected Period: Users can select the data timeframe (default is 2 months) to ensure consistency and flexibility in analysis.
3. Additional Technical Indicators
RSI, Momentum, ROC, MACD, and Stochastic:
Each indicator is computed using the Global Liquidity series rather than price.
User-selectable toggles allow for enabling or disabling each individual indicator as desired.
4. Enhanced MACD Visualization
Dynamic Histogram Coloring:
The MACD histogram color adjusts dynamically: brighter hues indicate rising histogram values while darker hues indicate falling values.
When the histogram is above zero, green is used; when below zero, red is applied, offering immediate visual insight into momentum shifts.
Conclusion
This indicator is an enlightening tool for understanding liquidity dynamics, aiding in macroeconomic analysis and investment decision-making by highlighting shifts in liquidity conditions and market momentum.
Interest Rate & CPI Differential By King OsamaINTEREST RATE & CPI Differential Indicator By King Osama
A must-have tool for forex traders and macro analysts, this indicator tracks interest rate differentials, real interest rate gaps, and CPI (inflation) differences to provide a fundamental edge in trading.
Key Features:
✅ Interest Rate Differential (Rate Diff) – Measures the gap between base and quote currency interest rates. Higher rates attract capital, influencing currency strength. Ideal for carry trade opportunities.
✅ Real Interest Rate Differential (Real Rate Diff) – Adjusts interest rates for inflation (CPI) to reflect the true return on holding a currency. A more accurate indicator of long-term strength.
✅ CPI Differential (CPI Diff) – Compares inflation rates between two economies, helping traders anticipate central bank actions (rate hikes/cuts) based on inflation trends.
✅ Dynamic Table & Bias Signals – Clear LONG/SHORT indications, historical tracking, and real-time updates for macro-driven forex decisions.
🔹 Perfect for swing traders combining fundamentals with technicals! 🚀
DCA Fundamentals 1.0DCA Fundamentals 1.0
Description:
DCA Fundamentals 1.0 is an invite-only indicator designed to help traders and investors make informed decisions by analyzing key fundamental metrics of a company. It aggregates essential financial data—such as book value, earnings per share, total equity, total debt, net income, and total revenue—to provide a comprehensive overview of the stock’s intrinsic value and risk profile. By examining factors like the debt-to-equity ratio and dynamically computing Buffet’s Limit, this tool assists in identifying whether a stock may be undervalued, fairly valued, or overvalued.
Key Features:
Intrinsic Value Calculation: Estimates a stock’s intrinsic worth using a weighted combination of book value per share and EPS.
Buffet’s Limit & Margin of Safety: Adjusts intrinsic value based on the company’s debt-to-equity ratio, providing a margin of safety percentage to gauge potential investment risk.
Debt Warning: Highlights when the debt-to-equity ratio exceeds 2, signaling possible financial instability.
Data Visualization: Displays equity, debt, net income, and revenue as area plots or histograms, helping users quickly assess financial health.
Investment Status: Classifies the stock as undervalued, fairly valued, or overvalued based on current price relative to intrinsic value and Buffet’s Limit.
Dividend-to-ROE Ratio: Offers insight into dividend payout sustainability relative to the company’s return on equity.
Instructions
Fallback Data Handling:
If any financial data is unavailable, fallback values are automatically used to ensure that key calculations remain meaningful and uninterrupted.
Intrinsics & Risk Assessment:
Intrinsic Value: Computed using book value and EPS to understand the stock’s core worth.
Buffet’s Limit: Adjusted from the intrinsic value based on the debt-to-equity ratio. The resulting margin of safety helps gauge the current price’s risk level.
Debt Warning:
Debt-to-Equity Ratio > 2: Triggers a red warning, advising caution due to potentially excessive debt.
Visual Indicators:
Intrinsically Undervalued (Green Area): When price is below intrinsic value, a green shaded area suggests the stock may be undervalued, potentially presenting a buying opportunity.
Debt vs. Equity (Area Plots):
Red Area: Represents debt. A larger red area signals relatively high debt levels.
Green Area: Represents equity. A larger green area suggests stronger financial health.
Revenue & Net Income (Histograms):
Green Bars: Positive or improving fundamentals.
Red Bars: Negative or declining performance.
Investment Status:
Undervalued (Green): Price below intrinsic value.
Fairly Valued (Yellow): Price between intrinsic value and Buffet’s Limit.
Overvalued (Red): Price above intrinsic value, implying increased downside risk.
Table Display:
A convenient table summarizes key metrics at a glance, including P/E ratio, Debt-to-Equity ratio, intrinsic value, margin of safety, net income, total revenue, and the Dividend-to-ROE Ratio.
Dividend-to-ROE Ratio:
This metric provides additional context on the company’s dividend policy relative to its return on equity, aiding in evaluating dividend sustainability.
Disclaimer
Important Disclaimer:
The DCA Fundamentals 1.0 indicator is provided solely for educational and informational purposes. It is not investment advice, a recommendation, or an endorsement of any security or strategy. All calculations are based on data provided by third parties, and their accuracy or completeness is not guaranteed.
Investing and trading involve significant risks. You may lose more than your initial investment. Historical performance or indicators cannot guarantee future results. Before making any investment decisions, you should conduct thorough research, consider consulting a qualified financial professional, and implement robust risk management strategies.
By using DCA Fundamentals 1.0, you acknowledge these risks and agree that neither the creator nor any affiliated parties are responsible for any losses incurred. Use this tool at your own discretion and risk.
CAPE / Shiller PE RatioThe CAPE (Cyclically Adjusted Price-to-Earnings) or Shiller PE ratio is a popular valuation measure used by investors to assess whether a stock or index is over or undervalued relative to its historical earnings. Unlike the traditional P/E ratio, the CAPE ratio smooths earnings over ten years, adjusting for inflation and providing a more stable and long-term view of valuation.
This indicator lets you quickly calculate and visualize the CAPE ratio for any stock on TradingView, helping you make informed decisions about the sustainability of current price levels. With its clear presentation and intuitive setup, you can compare historical CAPE levels and identify potential opportunities for long-term investments or avoid overvalued markets.
Advantages of the CAPE Ratio:
Long-Term Focus : Smooth earnings over ten years, reducing the impact of short-term volatility.
Inflation-Adjusted : Provides a more precise, inflation-adjusted valuation measure over time.
Historical Comparison : Allows for benchmarking against long-term historical averages.
Market Sentiment Indicator : Can highlight overvalued or undervalued markets for long-term investors.
Reduces Noise : Filters out short-term earnings fluctuations, offering a more stable view.
Disadvantages of the CAPE Ratio:
Ignores Recent Earnings : Misses short-term earnings changes, which can affect current valuations.
Outdated Data : Relies on old earnings data that may not reflect recent company performance.
Less Effective for Growth Stocks : May undervalue high-growth stocks focused on future earnings.
Sector Limitations : Works best for broad markets, less so for fast-changing industries.
Debated Predictive Power : It’s unreliable for timing short-term market movements.
In short, the CAPE ratio is excellent for long-term valuation but has limitations for short-term or growth-focused investing.
Disclaimer
Please remember that past performance may not be indicative of future results.
Due to various factors, including changing market conditions, the strategy may no longer perform as well as in historical backtesting.
This post and the script don’t provide any financial advice.
