ATH Levels v4# ATH Levels v4
A powerful indicator for tracking All-Time Highs (ATH) and setting customizable price levels based on percentage drops from the ATH. Perfect for cryptocurrency trading, DCA strategies, and risk management.
## Overview
ATH Levels v4 helps traders visualize key support levels calculated as percentage drops from the All-Time High within a configurable lookback period. The indicator also tracks the All-Time Low (ATL) since the last ATH, providing a complete picture of price range dynamics.
## Key Features
### Configurable Percentage Levels
- Define up to 8 custom price levels as percentage drops from ATH
- No longer limited to fixed 10% intervals
- Each level can be set anywhere from 0% to 100% drop
- Default levels: 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%
### ATL Tracking (NEW in v4)
- Automatically tracks the All-Time Low since the last ATH was reached
- Displays ATL price and percentage drop from ATH
- Resets when a new ATH is detected
- Can be toggled on/off
### Portfolio Management
- Allocate pot size percentages to each level
- Visualize dollar amounts for each level based on your total pot size
- Plan your DCA (Dollar Cost Averaging) strategy
- Only displays levels with allocated pot percentages
### Flexible Display Options
- Show/hide level lines
- Hide ATH level for zooming into lower levels
- Configurable lookback period (default 365 days)
- Adjustable right margin positioning for labels
- Color-coded labels with transparency gradient
## How to Use
### Basic Setup
1. Add the indicator to your chart
2. Set your total pot size in dollars
3. Configure the percentage drops for each level (where you want to buy/accumulate)
4. Allocate pot size percentages to each level
### Example DCA Strategy
```
Total Pot Size: $10,000
Level 3 (-30%): 10% pot = $1,000
Level 4 (-40%): 20% pot = $2,000
Level 5 (-50%): 25% pot = $2,500
Level 6 (-60%): 30% pot = $3,000
Level 7 (-70%): 10% pot = $1,000
Level 8 (-80%): 5% pot = $500
```
## Settings
### Display Options
- **Show level lines**: Toggle horizontal lines on/off
- **Hide ATH level**: Hide the ATH label for cleaner charts
- **Show ATL since last ATH**: Display/hide the All-Time Low indicator
- **Days to Lookback**: Period for calculating ATH (default: 365)
- **Margin from last bar**: Spacing between chart and labels (default: 10)
### Level Configuration
- **Level 1-8 % drop from ATH**: Set custom percentage drops (0-100%)
- **Level 1-8 pot %**: Allocate your portfolio percentage to each level (0-100%)
**Note**: Levels only display if they have a pot percentage allocated (>0%)
### Pot Size
- **Pot size**: Total amount in dollars available for the strategy
## Version History
### V4 (October 2025)
- Upgraded to PineScript v6
- Configurable percentage drops from ATH (no longer hardcoded)
- ATL tracking and display since last ATH
- Updated syntax and functions for v6 compatibility
### V3 (May 2020)
- Added option to hide ATH level for better chart zoom
### V2
- Hide/show level lines
- Configurable lookback period
- Configurable right margin
- Only shows levels with pot size % set
### V1
- Initial release with 8 fixed levels
## Use Cases
### Cryptocurrency Trading
- Plan accumulation zones during bear markets
- Set alerts at key percentage drops from ATH
- Track historical ATH and ATL ranges
### Risk Management
- Visualize potential support zones
- Plan position sizing at different levels
- Monitor distance from ATH in real-time
### DCA Strategies
- Automate dollar-cost averaging planning
- Allocate budget across multiple price levels
- Track execution of your DCA plan
## Technical Details
- **Version**: PineScript v6
- **Type**: Indicator
- **Overlay**: Yes
- **Default Timeframe**: Works on all timeframes
- **Calculations**: Based on closing prices within lookback period
## Credits
Original concept inspired by daytask. Enhanced and maintained by SilvesterScorpion.com
## License
This source code is subject to the terms of the Mozilla Public License 2.0 at mozilla.org
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**Tip**: For best results, use on higher timeframes (4H, Daily, Weekly) to identify major support zones. Combine with volume analysis and other indicators for confirmation.
Perolehan
Quarterly Earnings - v1This script shows company fundamentals in a TradingView table: Earnings Per Share (EPS), Price-to-Earnings Ratio (P/E, TTM), Sales (in Crores), Operating Margin (OPM %), Return on Assets (ROA %), and Return on Equity (ROE %).
Quarterly Earnings - v1This script shows company fundamentals in a TradingView table: Earnings Per Share (EPS), Price-to-Earnings Ratio (P/E, TTM), Sales (in Crores), Operating Margin (OPM %), Return on Assets (ROA %), and Return on Equity (ROE %).
DCA vs One-ShotCompare a DCA strategy by choosing the payment frequency (daily, weekly, or monthly), and by choosing whether or not to pay on weekends for cryptocurrency. You can add fees and the reference price (opening, closing, etc.).
Price Deviation StrategyThis strategy getting in long position only after the price drop
The % of the drop is Determined by SMA for the first trade
The inputs of SMA and % of the drop can be adjust from the User
After that bot start taking safe trades if not take profit from the first trade
The safe trades are Determined by step down deviation % and by quantity
There is no Stop loss is not for one with small tolerance to getting under
Take profit is average price + take profit - note if you use % trailing profit back test is not realistic but is working on real time
Max Safe Trades = 15
Capital max = $30000
Doge-USDT is just a example What the Strategy Can do
Green line - take profit
Black line - Brake even with fee - adjust for exchange
Quarterly EarningsEarnings Per Share (EPS), Price-to-Earnings Ratio (P/E, TTM), Sales (in Crores), Operating Margin (OPM %), Return on Assets (ROA %), and Return on Equity (ROE %). Each metric includes its absolute value and quarter-over-quarter or year-over-year percentage change.
Quarterly EarningsThis Pine script shows quarterly EPS, Sales, and P/E (TTM-based) in a styled table.
PE Rating by The Noiseless TraderPE Rating by The Noiseless Trader
This script analyzes a symbol’s Price-to-Earnings (P/E) ratio, using Diluted EPS (TTM) fundamentals directly from TradingView.
The script calculates the Price-to-Earnings ratio (P/E) using Diluted EPS (TTM) fundamentals. It then identifies:
PE High → the highest valuation point over a 3-year historical range.
PE Low → the lowest valuation point over a 3-year historical range.
PE Median → the midpoint between the two extremes, offering a fair-value benchmark.
PE (Int) → an additional intermediate low to track more recent undervaluation points. This is calculated based on lowest valuation point over a 1-year historical range
These levels are plotted directly on the chart as horizontal references, with markers showing the exact bars/dates when the extremes occurred. Candles corresponding to those days are also highlighted for context.
Bars corresponding to these extremes are highlighted (red = PE High, green = PE Low).
How it helps
Provides a historical valuation framework that complements technical analysis. We look for long opportunity or base formation near the PE Low and be cautious when stocks tends to trade near High PE.
We do not short the stock at High PE infact be cautious with long trades.
Helps identify whether current price action is happening near overvalued or undervalued zones.
Adds a long-term perspective to support swing trading and investing decisions. If a stock is coming from Low PE to Median PE and along with that if we get entry based on Classical strategies like Darvas Box, or HH-HL based on Dow Theory.
Offers a simple visual map of how far the market has moved from “cheap” to “expensive.”
This tool is best suited for long-term investors and swing traders who want to merge fundamentals with technical setups.
This indicator is designed as an educational tool to illustrate how valuation metrics (like earnings multiples) can be viewed alongside price action, helping traders connect fundamental context with technical execution in real market conditions.
MarketSurge EPS Line [tradeviZion]MarketSurge EPS Line
EPS trend line overlay for TradingView charts, inspired by the IBD MarketSurge (formerly MarketSmith) EPS line style.
Displays EPS trend line on price charts
Uses 4-quarter earnings moving average
Shows earnings momentum over time
Works with actual, estimated, or standardized earnings data
Customizable line color and width
This script creates an EPS trend line overlay, similar to the EPS line feature in IBD MarketSurge (previously MarketSmith), allowing you to visualize earnings trends alongside price action.
Add script to chart
EPS line appears automatically
Adjust color and width in settings if needed
Hover over line for earnings details
Settings:
EPS data type (actual/estimate/standardized)
Line color and width
💡 Tip:
For the complete IBD Style experience, pair this EPS line with IBD Style Candles to visualize price action with clean bars like IBD Style
Forward P/E CalculatorI could not find a forward P/E indicator that gave me proper results. So here is mine.
EPS QoQ % ChangeThis indicator calculates and displays the quarter-over-quarter (QoQ) percentage change in earnings per share (EPS) directly on your chart, aligned with each earnings event.
It is designed to quickly highlight EPS growth or decline without the need to open an earnings report, providing traders and investors with instant, visual performance context.
Features :
- Automatic Earnings Detection: Identifies earnings bars and calculates QoQ % change.
- Color-Coded Text: Positive changes are shown in your chosen “up” color, declines in your “down” color, and flat results in a neutral color.
- Customizable Appearance: Choose text size and colors to match your chart style.
- Tooltip Support: Optional detailed tooltip showing reported EPS, previous EPS, and calculated QoQ change.
- Compact Layout: Displays in its own pane to avoid cluttering price action.
Use Cases :
- Quickly assess EPS growth trends over time.
- Spot significant earnings beats or misses without reading earnings transcripts.
- Use alongside other technical or fundamental tools for better decision-making.
EPS+Sales+Net Profit+MCap+Sector & Industry📄 Full Description
This script displays a comprehensive financial data panel directly on your TradingView chart, helping long-term investors and swing traders make informed decisions based on fundamental trends. It consolidates key financial metrics and business classification data into a single, visually clear table.
🔍 Key Features:
🧾 Financial Metrics (Auto-Fetched via request.financial):
EPS (Earnings Per Share) – Displayed with trend direction (QoQ or YoY).
Sales / Revenue – In ₹ Crores (for Indian stocks), trend change also included.
Net Profit – Also in ₹ Crores, along with percentage change.
Market Cap – Automatically calculated using outstanding shares × price, shown in ₹ Cr.
Free Float Market Cap – Based on float shares × price, also in ₹ Cr.
🏷️ Sector & Industry Info:
Automatically identifies and displays the Sector and Industry of the stock using syminfo.sector and syminfo.industry.
Displayed inline with metrics, making it easy to know what business the stock belongs to.
📊 Table View:
Compact and responsive table shown on your chart.
Columns: Date | EPS | QoQ | Sales | QoQ | Net Profit | QoQ | Metrics
Metrics column dynamically shows:
Market Cap
Free Float
Sector (Row 4)
Industry (Row 5)
🌗 Appearance:
Supports Dark Mode and Mini Mode toggle.
You can also customize:
Number of data points (last 4+ quarters or years)
Table position and size
🎯 Use Case:
This script is ideal for:
Fundamental-focused traders who use EPS/Sales trends to identify momentum.
Swing traders who combine price action with fundamental tailwinds.
Portfolio builders who want to see sector/industry alignment quickly.
It works best with fundamentally sound stocks where earnings and profitability are a major factor in price movements.
✅ Important Notes:
Script uses request.financial which only works with supported symbols (mostly stocks).
Market Cap and Free Float are calculated in ₹ Crores.
All financial values are rounded and formatted for readability (e.g., 1,234 Cr).
🙏 Credits:
Developed and published by Sameer Thorappa
Built with a clean, minimalist approach for high readability and functionality.
Earnings [theUltimator5]This indicator highlights daily price changes on earnings announcement days using dynamic colors, labels, and optional earnings markers.
🔍 Key Features:
Earnings Detection:
Highlights only the days when an earnings event occurs.
Price Change Calculation:
Computes the percentage change from open to close on earnings day.
Color-coded Labels:
Displays the % change as a floating label above the chart on earnings days.
Color intensity reflects the size and direction of the move:
Bright green for large gains (≥ +10%)
Bright red for large losses (≤ -10%)
White for negligible change
Gradient fades between those extremes
Optional "Earnings" Marker:
A small label marked “Earnings” appears beneath the % change label, controlled by a user toggle.
Background Highlight:
The chart background is shaded on earnings days with a semi-transparent color based on the % change.
⚙️ User Input:
✅ Show 'E' Marker: Toggles the visibility of the "Earnings" label below the main price change label.
✅ Ideal Use Case:
Use this indicator to visually analyze how a stock reacts on earnings days, helping traders spot consistent behavior patterns (e.g., post-earnings rallies or selloffs).
Greer Value Yields Line📈 Greer Value Yields Line – Valuation Signal Without the Clutter
Part of the Greer Financial Toolkit, this streamlined indicator tracks four valuation-based yield metrics and presents them clearly via the Data Window, GVY Score badge, and an optional Yield Table:
Earnings Yield (EPS ÷ Price)
FCF Yield (Free Cash Flow ÷ Price)
Revenue Yield (Revenue per Share ÷ Price)
Book Value Yield (Book Value per Share ÷ Price)
✅ Each yield is compared against its historical average
✅ A point is scored for each metric above average (0–4 total)
✅ Color-coded GVY Score badge highlights valuation strength
✅ Yield trend-lines Totals (TVAVG & TVPCT) help assess direction
✅ Clean layout: no chart clutter – just actionable insights
🧮 GVY Score Color Coding (0–4):
⬜ 0 = None (White)
⬜ 1 = Weak (Gray)
🟦 2 = Neutral (Aqua)
🟩 3 = Strong (Green)
🟨 4 = Gold Exceptional (All metrics above average)
Total Value Average Line Color Coding:
🟥 Red – Average trending down
🟩 Green – Average trending up
Ideal for long-term investors focused on fundamental valuation, not short-term noise.
Enable the table and badge for a compact yield dashboard — or keep it minimal with just the Data Window and trend-lines.
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.
Greer Value Yields Dashboard🧾 Greer Value Yields Dashboard – v1.0
Author: Sean Lee Greer
Release Date: June 22, 2025
🧠 Overview
The Greer Value Yields Dashboard visualizes and evaluates four powerful valuation metrics for any publicly traded company:
📘 Earnings per Share Yield
💵 Free Cash Flow Yield
💰 Revenue Yield
🏦 Book Value Yield
Each yield is measured as a percentage of current stock price and compared against its historical average. The script assigns 1 point per metric when the current yield exceeds its long-term average. The total score (0 to 4) is displayed as a color-coded column chart, helping long-term investors quickly assess fundamental valuation strength.
✅ Key Features
📊 Real-time calculation of 4 yield-based valuation metrics
⚖ Historical average tracking for each yield
🎯 Visual scoring system:
🟥 0–1 = Weak
🟨 2 = Neutral
🟩 4 = Strong (all metrics above average)
🎛️ Toggle visibility of each yield independently
🧮 Fully compatible with other Greer Financial Toolkit indicators
🛠 Ideal For
Long-term value investors
Dividend and cash-flow-focused investors
Analysts seeking clean yield visualizations
Greer Toolkit users combining with Greer Value and BuyZone
Greer Value📈 Greer Value
This indicator evaluates the year-over-year (YoY) growth consistency of five key fundamental metrics for any stock:
Book Value Per Share
Free Cash Flow
Operating Margin
Total Revenue
Net Income
The script tracks whether each metric increases annually based on financial statement data (FY), then calculates both individual and aggregate increase percentages over time. A color-coded table is displayed on the most recent bar showing:
Raw counts of increases vs. checks per metric
Percentage of years with growth
Overall "Greer Value" score indicating total consistency across all five metrics
✅ Green = Strong YoY growth
❌ Red = Weak or inconsistent growth
Use this tool to help identify fundamentally improving companies with long-term value creation potential.
QoQ PAT, Sales & OPM% Labels by GauravThis indicator automatically displays the Quarter-over-Quarter (QoQ) percentage change in Sales, PAT (Profit After Tax), and Operating Profit Margin (OPM%) directly on the price chart.
It fetches quarterly financial data using TradingView’s request.financial() function for:
Sales (TOTAL_REVENUE),
PAT (NET_INCOME),
Operating Profit (OPER_INCOME).
For each earnings update, it calculates:
Sales QoQ %: Growth in sales vs. the previous quarter,
PAT QoQ %: Growth in PAT vs. the previous quarter,
OPM %: Operating Profit Margin = (Operating Profit / Sales) × 100.
This helps traders and investors quickly visualize fundamental growth trends right alongside the candlestick chart, improving fundamental + technical analysis integration.
S&P 500 Top 25 - EPS AnalysisEarnings Surprise Analysis Framework for S&P 500 Components: A Technical Implementation
The "S&P 500 Top 25 - EPS Analysis" indicator represents a sophisticated technical implementation designed to analyze earnings surprises among major market constituents. Earnings surprises, defined as the deviation between actual reported earnings per share (EPS) and analyst estimates, have been consistently documented as significant market-moving events with substantial implications for price discovery and asset valuation (Ball and Brown, 1968; Livnat and Mendenhall, 2006). This implementation provides a comprehensive framework for quantifying and visualizing these deviations across multiple timeframes.
The methodology employs a parameterized approach that allows for dynamic analysis of up to 25 top market capitalization components of the S&P 500 index. As noted by Bartov et al. (2002), large-cap stocks typically demonstrate different earnings response coefficients compared to their smaller counterparts, justifying the focus on market leaders.
The technical infrastructure leverages the TradingView Pine Script language (version 6) to construct a real-time analytical framework that processes both actual and estimated EPS data through the platform's request.earnings() function, consistent with approaches described by Pine (2022) in financial indicator development documentation.
At its core, the indicator calculates three primary metrics: actual EPS, estimated EPS, and earnings surprise (both absolute and percentage values). This calculation methodology aligns with standardized approaches in financial literature (Skinner and Sloan, 2002; Ke and Yu, 2006), where percentage surprise is computed as: (Actual EPS - Estimated EPS) / |Estimated EPS| × 100. The implementation rigorously handles potential division-by-zero scenarios and missing data points through conditional logic gates, ensuring robust performance across varying market conditions.
The visual representation system employs a multi-layered approach consistent with best practices in financial data visualization (Few, 2009; Tufte, 2001).
The indicator presents time-series plots of the four key metrics (actual EPS, estimated EPS, absolute surprise, and percentage surprise) with customizable color-coding that defaults to industry-standard conventions: green for actual figures, blue for estimates, red for absolute surprises, and orange for percentage deviations. As demonstrated by Padilla et al. (2018), appropriate color mapping significantly enhances the interpretability of financial data visualizations, particularly for identifying anomalies and trends.
The implementation includes an advanced background coloring system that highlights periods of significant earnings surprises (exceeding ±3%), a threshold identified by Kinney et al. (2002) as statistically significant for market reactions.
Additionally, the indicator features a dynamic information panel displaying current values, historical maximums and minimums, and sample counts, providing important context for statistical validity assessment.
From an architectural perspective, the implementation employs a modular design that separates data acquisition, processing, and visualization components. This separation of concerns facilitates maintenance and extensibility, aligning with software engineering best practices for financial applications (Johnson et al., 2020).
The indicator processes individual ticker data independently before aggregating results, mitigating potential issues with missing or irregular data reports.
Applications of this indicator extend beyond merely observational analysis. As demonstrated by Chan et al. (1996) and more recently by Chordia and Shivakumar (2006), earnings surprises can be successfully incorporated into systematic trading strategies. The indicator's ability to track surprise percentages across multiple companies simultaneously provides a foundation for sector-wide analysis and potentially improves portfolio management during earnings seasons, when market volatility typically increases (Patell and Wolfson, 1984).
References:
Ball, R., & Brown, P. (1968). An empirical evaluation of accounting income numbers. Journal of Accounting Research, 6(2), 159-178.
Bartov, E., Givoly, D., & Hayn, C. (2002). The rewards to meeting or beating earnings expectations. Journal of Accounting and Economics, 33(2), 173-204.
Bernard, V. L., & Thomas, J. K. (1989). Post-earnings-announcement drift: Delayed price response or risk premium? Journal of Accounting Research, 27, 1-36.
Chan, L. K., Jegadeesh, N., & Lakonishok, J. (1996). Momentum strategies. The Journal of Finance, 51(5), 1681-1713.
Chordia, T., & Shivakumar, L. (2006). Earnings and price momentum. Journal of Financial Economics, 80(3), 627-656.
Few, S. (2009). Now you see it: Simple visualization techniques for quantitative analysis. Analytics Press.
Gu, S., Kelly, B., & Xiu, D. (2020). Empirical asset pricing via machine learning. The Review of Financial Studies, 33(5), 2223-2273.
Johnson, J. A., Scharfstein, B. S., & Cook, R. G. (2020). Financial software development: Best practices and architectures. Wiley Finance.
Ke, B., & Yu, Y. (2006). The effect of issuing biased earnings forecasts on analysts' access to management and survival. Journal of Accounting Research, 44(5), 965-999.
Kinney, W., Burgstahler, D., & Martin, R. (2002). Earnings surprise "materiality" as measured by stock returns. Journal of Accounting Research, 40(5), 1297-1329.
Livnat, J., & Mendenhall, R. R. (2006). Comparing the post-earnings announcement drift for surprises calculated from analyst and time series forecasts. Journal of Accounting Research, 44(1), 177-205.
Padilla, L., Kay, M., & Hullman, J. (2018). Uncertainty visualization. Handbook of Human-Computer Interaction.
Patell, J. M., & Wolfson, M. A. (1984). The intraday speed of adjustment of stock prices to earnings and dividend announcements. Journal of Financial Economics, 13(2), 223-252.
Skinner, D. J., & Sloan, R. G. (2002). Earnings surprises, growth expectations, and stock returns or don't let an earnings torpedo sink your portfolio. Review of Accounting Studies, 7(2-3), 289-312.
Tufte, E. R. (2001). The visual display of quantitative information (Vol. 2). Graphics Press.
PEAD strategy█ OVERVIEW
This strategy trades the classic post-earnings announcement drift (PEAD).
It goes long only when the market gaps up after a positive EPS surprise.
█ LOGIC
1 — Earnings filter — EPS surprise > epsSprThresh %
2 — Gap filter — first regular 5-minute bar gaps ≥ gapThresh % above yesterday’s close
3 — Timing — only the first qualifying gap within one trading day of the earnings bar
4 — Momentum filter — last perfDays trading-day performance is positive
5 — Risk management
• Fixed stop-loss: stopPct % below entry
• Trailing exit: price < Daily EMA( emaLen )
█ INPUTS
• Gap up threshold (%) — 1 (gap size for entry)
• EPS surprise threshold (%) — 5 (min positive surprise)
• Past price performance — 20 (look-back bars for trend check)
• Fixed stop-loss (%) — 8 (hard stop distance)
• Daily EMA length — 30 (trailing exit length)
Note — Back-tests fill on the second 5-minute bar (Pine limitation).
Live trading: enable calc_on_every_tick=true for first-tick entries.
────────────────────────────────────────────
█ 概要（日本語）
本ストラテジーは決算後の PEAD を狙い、
EPS サプライズがプラス かつ 寄付きギャップアップ が発生した銘柄をスイングで買い持ちします。
█ ロジック
1 — 決算フィルター — EPS サプライズ > epsSprThresh %
2 — ギャップフィルター — レギュラー時間最初の 5 分足が前日終値＋ gapThresh %以上
3 — タイミング — 決算当日または翌営業日の最初のギャップのみエントリー
4 — モメンタムフィルター — 過去 perfDays 営業日の騰落率がプラス
5 — リスク管理
• 固定ストップ：エントリー − stopPct %
• 利確：終値が日足 EMA( emaLen ) を下抜け
█ 入力パラメータ
• Gap up threshold (%) — 1 (ギャップ条件)
• EPS surprise threshold (%) — 5 (EPS サプライズ最小値)
• Past price performance — 20 (パフォーマンス判定日数)
• Fixed stop-loss (%) — 8 (固定ストップ幅)
• Daily EMA length — 30 (利確用 EMA 期間)
注意 — Pine の仕様上、バックテストでは寄付き 5 分足の次バーで約定します。
実運用で寄付き成行に合わせたい場合は calc_on_every_tick=true を有効にしてください。
────
ご意見や質問があればお気軽にコメントください。
Happy trading!
Stock metrics and valueThis indicator shows:
- the valuation metrics for a stock on a table on top right: PE, EPS, dividend, ROIC, ROE, ROA, EPS growth, FCF growth, Equity growth, revenue Growth
- the fair value and the value with 50% margin of safety as chart lines
The lines will be red when they are above the current price and red when they are below the current price.
The colors on the table will be red when the values are below 10% and green when they are above, that means when everything is green the metrics for the stock are good.
Constant Valuation Multiple LevelsThis indicator adds price levels at constant multiples based on your preferred valuation metric. The settings provides options for setting this metric while Operational Income is the default one.
This indicator is not perfect as it relies on historical earnings data but does not have forecast data (not available in pinescript), thus its not a guide for future price level. It also does not account for "adjusted" earnings which may skew levels for some quarters.
However this script provides a quick way to see the stock price against your preferred valuation multiple to see if it's undervalued and worth investigating further for quality and earnings forecast.
Metatrader CalculatorThe “ Metatrader Calculator ” indicator calculates the position size, risk, and potential gain of a trade, taking into account the account balance, risk percentage, entry price, stop loss price, and risk/reward ratio. It supports the XAUUSD, XAGUSD, and BTCUSD pairs, automatically calculating the position size (in lots) based on these parameters. The calculation is displayed in a table on the chart, showing the lot size, loss in dollars, and potential gain based on the defined risk.
