SMT Divergence ICT 01 [TradingFinder] Smart Money Technique🔵 Introduction
SMT Divergence (short for Smart Money Technique Divergence) is a trading technique in the ICT Concepts methodology that focuses on identifying divergences between two positively correlated assets in financial markets.
These divergences occur when two assets that should move in the same direction move in opposite directions. Identifying these divergences can help traders spot potential reversal points and trend changes.
Bullish and Bearish divergences are clearly visible when an asset forms a new high or low, and the correlated asset fails to do so. This technique is applicable in markets like Forex, stocks, and cryptocurrencies, and can be used as a valid signal for deciding when to enter or exit trades.
Bullish SMT Divergence : This type of divergence occurs when one asset forms a higher low while the correlated asset forms a lower low. This divergence is typically a sign of weakness in the downtrend and can act as a signal for a trend reversal to the upside.
Bearish SMT Divergence : This type of divergence occurs when one asset forms a higher high while the correlated asset forms a lower high. This divergence usually indicates weakness in the uptrend and can act as a signal for a trend reversal to the downside.
🔵 How to Use
SMT Divergence is an analytical technique that identifies divergences between two correlated assets in financial markets.
This technique is used when two assets that should move in the same direction move in opposite directions.
Identifying these divergences can help you pinpoint reversal points and trend changes in the market.
🟣 Bullish SMT Divergence
This divergence occurs when one asset forms a higher low while the correlated asset forms a lower low. This divergence indicates weakness in the downtrend and can signal a potential price reversal to the upside.
In this case, when the correlated asset is forming a lower low, and the main asset is moving lower but the correlated asset fails to continue the downward trend, there is a high probability of a trend reversal to the upside.
🟣 Bearish SMT Divergence
Bearish divergence occurs when one asset forms a higher high while the correlated asset forms a lower high. This type of divergence indicates weakness in the uptrend and can signal a potential trend reversal to the downside.
When the correlated asset fails to make a new high, this divergence may be a sign of a trend reversal to the downside.
🟣 Confirming Signals with Correlation
To improve the accuracy of the signals, use assets with strong correlation. Forex pairs like OANDA:EURUSD and OANDA:GBPUSD , or cryptocurrencies like COINBASE:BTCUSD and COINBASE:ETHUSD , or commodities such as gold ( FX:XAUUSD ) and silver ( FX:XAGUSD ) typically have significant correlation. Identifying divergences between these assets can provide a strong signal for a trend change.
🔵 Settings
Second Symbol : This setting allows you to select another asset for comparison with the primary asset. By default, "XAUUSD" (Gold) is set as the second symbol, but you can change it to any currency pair, stock, or cryptocurrency. For example, you can choose currency pairs like EUR/USD or GBP/USD to identify divergences between these two assets.
Divergence Fractal Periods : This parameter defines the number of past candles to consider when identifying divergences. The default value is 2, but you can change it to suit your preferences. This setting allows you to detect divergences more accurately by selecting a greater number of candles.
Bullish Divergence Line : Displays a line showing bullish divergence from the lows.
Bearish Divergence Line : Displays a line showing bearish divergence from the highs.
Bullish Divergence Label : Displays the "+SMT" label for bullish divergences.
Bearish Divergence Label : Displays the "-SMT" label for bearish divergences.
🔵 Conclusion
SMT Divergence is an effective tool for identifying trend changes and reversal points in financial markets based on identifying divergences between two correlated assets. This technique helps traders receive more accurate signals for market entry and exit by analyzing bullish and bearish divergences.
Identifying these divergences can provide opportunities to capitalize on trend changes in Forex, stocks, and cryptocurrency markets. Using SMT Divergence along with risk management and confirming signals with other technical analysis tools can improve the accuracy of trading decisions and reduce risks from sudden market changes.
Forecasting
Xpro_Shark V1.0 This indicator is the first version and it is a primitive indicator. Wait for the next versions.
Xpro_Shark V1.0: Powerful Signals at a Glance
This robust indicator combines the power of MACD, RSI, and RSI 80/20 into a single, easy-to-read table. Get clear, concise signals for spotting potential trend reversals and strong momentum shifts.
Indicators Included:
MACD: Identifies changes in trend direction and strength.
RSI: Detects overbought and oversold conditions.
RSI 80/20: Highlights extreme overbought and oversold conditions for potentially stronger signals.
Table Display:
The table clearly shows the current status of each indicator ("up," "down," or "none") for quick interpretation.
Unleash the Power of Xpro_Shark V1.0:
This comprehensive indicator offers a powerful edge for traders looking to make informed decisions. Combine its insights with your analysis for enhanced trading performance.
(Disclaimer: Past performance is not indicative of future results. Use at your own risk.)
Forecasted VLThis indicator is vertical lines bases around the 2PM CRT (Candle Range Theory) the lines are forecasted so will be ahead which helps saves some time and you can see price going into them, however the indicator only works on the 5M which is the timeframe you should be entering trades on anyway, wait for a sweep of the 10am-2pm 4 hour candle and enter inside the vertical lines with the first 5m BOS.
Period Separator & Candle OHLCThis Pine Script combines two functionalities: Period Separator and Candle OHLC, into a single indicator. Here's a breakdown of what the script does:
The period separator visually marks the start of a new period (e.g., hourly, daily, weekly) on the chart.
The Candle OHLC component plots the open, high, low, and close levels of candles from a higher timeframe (e.g., H4 or Daily) on the current chart's timeframe.
Combined Functionality:
The Period Separator helps users track new periods, such as session changes or daily resets.
The Candle OHLC provides a clear view of important price levels from higher timeframes, directly overlaid on the current chart.
Customization Options:
Both features are independently configurable, allowing users to:
Adjust the timeframe and visual properties of the period separators.
Choose which OHLC levels to display and how they appear.
Example Use Cases
Day Trader:
Use the period separator to mark hourly or session changes.
Overlay daily OHLC levels to identify key price zones.
Swing Trader:
Use the period separator to track daily or weekly periods.
Plot H4 or daily OHLC levels on lower timeframes to identify potential breakout or reversal zones.
Scalper:
Combine minute-level period separators with H1 or H4 OHLC levels for precise entry/exit zones.
Period Separator Marks the start of user-defined periods with customizable vertical lines.
Custom OHLC Levels Plots the open, high, low, and close of higher timeframe candles.
Bullish/Bearish Colors Differentiates bullish and bearish bars with green and red colors.
Fill Bars Optionally fills the area between open and close with colors.
Configurable Timeframes Allows independent timeframes for separators and OHLC levels.
O Pia Das Criptos - Gráfico Ninja!# Candle Cores:
-Blue Candles: Represent buy candles when the asset is above Bitcoin. The candles are blue when the closing price is higher than the opening price, lowering buying pressure when the asset is above BTC.
-Orange Candles: Used for sell candles only when the asset is above Bitcoin, indicating a slight correction. When the asset is above BTC, but the closing is lower than the opening, the candles turn orange. This may indicate a loss of strength, even if the asset is still outperforming BTC.
_________
#Dots:
-Blue Dot:
Condition: The blue dot is projected when the asset's RSI is above 50, in addition to being above the EMA (Exponential Moving Average) of the Bitcoin RSI and the Bitcoin RSI itself.
Meaning: Indicates that the asset is in a strong condition in relation to Bitcoin, indicating a positive moment.
- Green Dot:
Condition: The green dot is drawn when there is an upward trend between the short EMA of the Bitcoin RSI and the long EMA of the Bitcoin RSI.
Meaning: This dot suggests a buy signal or an entry opportunity, indicating that the momentum is strengthening.
-Red Dot
Condition: The red dot is drawn when there is a downward trend between the short EMA of the Bitcoin RSI and the long EMA of the Bitcoin RSI.
Meaning: This indicates a sell signal or an exit opportunity, indicating that the momentum may be weakening.
-White Dot
Condition: A white dot appears when there is a trend between the RSI of the asset and the RSI of Bitcoin.
Meaning: This crossover is considered a point of attention or a change in the dynamics between the asset and Bitcoin. It indicates that the asset may be gaining or losing strength against Bitcoin.
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#The "X":
- When a crossUp occurs, the green 'X' signal is plotted. This indicates that the asset’s RSI is becoming stronger relative to the Bitcoin RSI EMA, which could be a sign of strength in the asset.
Green: Indicates that the asset’s RSI has crossed above the Bitcoin RSI EMA, indicating positive momentum and possible increase in strength.
- When a downward crossover occurs, the red ‘X’ sign is plotted. This indicates that the asset’s RSI is becoming weaker relative to the Bitcoin RSI EMA, which could be interpreted as a sign of weakness in the asset.
Red: Indicates that the asset’s RSI has crossed below the Bitcoin RSI EMA, indicating negative momentum and possible decrease.
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# Labels:
- When the Bitcoin RSI is below 50 (indicating a possible bearish movement) and the asset’s RSI is above 50 (suggesting strength), the label is set to “ATTENTION!” with a green color.
- When Bitcoin’s RSI is above 50 (indicating strength) and the asset’s RSI is below 50 (indicating weakness), the label is also “WARNING!” but with a red color.
- An RSI crossover occurs when the asset’s RSI crosses above Bitcoin’s RSI. If this condition is true, the label is set to “ASSET GETTING STRONGER THAN BTC!” with a green color.
- When Bitcoin’s EMA RSI is below 30, and Bitcoin’s RSI is also below 30, while the asset’s SMMA (Smooth Moving Average) RSI and the asset’s RSI are both above 33, the label is set to “WARNING! MAY GO UP!” with a green color. This suggests that despite Bitcoin’s losses, the asset may be gearing up for a recovery.
_________
#Asset RSI Line:
- White Color: This is the default color for an asset’s RSI line when it is between overbought and oversold levels (i.e. between 30 and 70).
- Blue Color: The line changes to blue when the asset’s RSI is above Bitcoin’s RSI, indicating that the asset is stronger relative to Bitcoin.
- Green Color: When the asset’s RSI crosses above 70, indicating an overbought condition, the line turns green. This suggests that the asset may be approaching a correction point.
- Red Color: When the asset’s RSI falls below 30, the line changes to red, indicating an oversold condition. This suggests that the asset may be undervalued and a reversal may be near.
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#Bitcoin RSI Line:
- Red Color: Bitcoin’s RSI is represented by a red line. If Bitcoin’s RSI is above 70, the red line becomes more intense (opacity 0), indicating that Bitcoin is in an overbought condition. Otherwise, the line is a lighter red (opacity 80), indicating a less critical condition.
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# Bitcoin RSI EMA Line:
- Orange Color: The exponential moving average (EMA) of Bitcoin’s RSI is represented by an orange line. This line helps to smooth out the fluctuations of the RSI, allowing you to better visualize the trend.
_________
# Asset RSI SMMA Line:
- Gray Color: The asset's RSI smoothing (SMMA - Smoothed Moving Average) is represented by a gray line. This line also serves to smooth the RSI data, helping to identify trends more clearly.
_________
# Overbought and Oversold Lines:
- Overbought Line (70): Represented by a green dotted line. This indicates the level at which the asset is considered overbought.
- Oversold Line (30): Represented by a red dotted line. This indicates the level at which the asset is considered oversold.
- Middle Line (50): This line is gray and represents the neutral point between overbought and oversold.
_________
# Filled Areas:
- Overbought Area: The area above the overbought line (70) is filled in light green, indicating the region where the assets are considered overbought. - Oversold Area: The area below the oversold line (30) is filled in light red, indicating the region where assets are considered oversold.
Kalman PredictorThe **Kalman Predictor** indicator is a powerful tool designed for traders looking to enhance their market analysis by smoothing price data and projecting future price movements. This script implements a Kalman filter, a statistical method for noise reduction, to dynamically estimate price trends and velocity. Combined with ATR-based confidence bands, it provides actionable insights into potential price movement, while offering clear trend and momentum visualization.
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#### **Key Features**:
1. **Kalman Filter Smoothing**:
- Dynamically estimates the current price state and velocity to filter out market noise.
- Projects three future price levels (`Next Bar`, `Next +2`, `Next +3`) based on velocity.
2. **Dynamic Confidence Bands**:
- Confidence bands are calculated using ATR (Average True Range) to reflect market volatility.
- Visualizes potential price deviation from projected levels.
3. **Trend Visualization**:
- Color-coded prediction dots:
- **Green**: Indicates an upward trend (positive velocity).
- **Red**: Indicates a downward trend (negative velocity).
- Dynamically updated label displaying the current trend and velocity value.
4. **User Customization**:
- Inputs to adjust the process and measurement noise for the Kalman filter (`q` and `r`).
- Configurable ATR multiplier for confidence bands.
- Toggleable trend label with adjustable positioning.
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#### **How It Works**:
1. **Kalman Filter Core**:
- The Kalman filter continuously updates the estimated price state and velocity based on real-time price changes.
- Projections are based on the current price trend (velocity) and extend into the future (Next Bar, +2, +3).
2. **Confidence Bands**:
- Calculated using ATR to provide a dynamic range around the projected future prices.
- Indicates potential volatility and helps traders assess risk-reward scenarios.
3. **Trend Label**:
- Updates dynamically on the last bar to show:
- Current trend direction (Up/Down).
- Velocity value, providing insight into the expected magnitude of the price movement.
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#### **How to Use**:
- **Trend Analysis**:
- Observe the direction and spacing of the prediction dots relative to current candles.
- Larger spacing indicates a potential strong move, while clustering suggests consolidation.
- **Risk Management**:
- Use the confidence bands to gauge potential price volatility and set stop-loss or take-profit levels accordingly.
- **Pullback Detection**:
- Look for flattening or clustering of dots during trends as a signal of potential pullbacks or reversals.
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#### **Customizable Inputs**:
- **Kalman Filter Parameters**:
- `lookback`: Adjusts the smoothing window.
- `q`: Process noise (higher values make the filter more reactive to changes).
- `r`: Measurement noise (controls sensitivity to price deviations).
- **Confidence Bands**:
- `band_multiplier`: Multiplies ATR to define the range of confidence bands.
- **Visualization**:
- `show_label`: Option to toggle the trend label.
- `label_offset`: Adjusts the label’s distance from the price for better visibility.
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#### **Examples of Use**:
- **Scalping**: Use on lower timeframes (e.g., 1-minute, 5-minute) to detect short-term price trends and reversals.
- **Swing Trading**: Identify pullbacks or continuations on higher timeframes (e.g., 4-hour, daily) by observing the prediction dots and confidence bands.
- **Risk Assessment**: Confidence bands help visualize potential price volatility, aiding in the placement of stops and targets.
---
#### **Notes for Traders**:
- The **Kalman Predictor** does not predict the future with certainty but provides a statistically informed estimate of price movement.
- Confidence bands are based on historical volatility and should be used as guidelines, not guarantees.
- Always combine this tool with other analysis techniques for optimal results.
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This script is open-source, and the Kalman filter logic has been implemented uniquely to integrate noise reduction with dynamic confidence band visualization. If you find this indicator useful, feel free to share your feedback and experiences!
---
#### **Credits**:
This script was developed leveraging the statistical principles of Kalman filtering and is entirely original. It incorporates ATR for dynamic confidence band calculations to enhance trader usability and market adaptability.
Estratégia wmilas EMA20 e RSI//@version=5
indicator(title="Estratégia EMA20 e RSI", shorttitle="EMA20+RSI", overlay=true)
// Configurações da EMA
emaLength = input.int(20, title="Comprimento da EMA")
emaSource = input.source(close, title="Fonte da EMA")
emaValue = ta.ema(emaSource, emaLength)
// Configurações do RSI
rsiLength = input.int(14, title="Comprimento do RSI")
rsiOverbought = input.int(70, title="Nível de Sobrecompra do RSI", minval=50, maxval=100)
rsiOversold = input.int(30, title="Nível de Sobrevenda do RSI", minval=0, maxval=50)
rsiValue = ta.rsi(close, rsiLength)
// Plotagem da EMA
plot(emaValue, color=color.blue, title="EMA20", linewidth=2)
// Condições de entrada
longCondition = ta.crossover(close, emaValue) and rsiValue < rsiOversold
shortCondition = ta.crossunder(close, emaValue) and rsiValue > rsiOverbought
// Plotagem das setas de entrada
plotshape(series=longCondition, title="Sinal de Compra", location=location.belowbar, color=color.green, style=shape.triangleup, size=size.small)
plotshape(series=shortCondition, title="Sinal de Venda", location=location.abovebar, color=color.red, style=shape.triangledown, size=size.small)
// Alertas
if longCondition
alert("Sinal de compra detectado! Fechamento acima da EMA20 e RSI em sobrevenda.", alert.freq_once_per_bar_close)
if shortCondition
alert("Sinal de venda detectado! Fechamento abaixo da EMA20 e RSI em sobrecompra.", alert.freq_once_per_bar_close)
O Pia das Criptos - RSI Ninja!- RSI and Delta BTC indicator: It compares the RSI of the current asset with the RSI of Bitcoin, creating a relative strength chart for multiple periods (1min, 5min, 15min, 1h, 4h, 1d). It also calculates the difference of the Delta BTC and the Exponential BTC for the same intervals, allowing a broader view of the strength or weakness of the cryptocurrency in relation to Bitcoin.
___________
- EMA display: Plots of EMAs (9, 21, 50, 100, etc.) on the chart to aid in trend analysis. These EMAs help determine buy and sell signals based on the crossovers between EMAs 9 and 21. I put several EMAs because I know that the TradingView asshole only allows 2 technical indicators for each free account, so to avoid the guy opening several screens, I put several in the same code to be used. ___________
- Strength and Weakness Alerts: Alerts when the asset's strength against Bitcoin changes from weak to strong, and vice versa. This is useful for monitoring significant changes in the asset's behavior against BTC.
___________
- Emoji and Dot Display: Uses emojis on the chart as visual indicators for buy ("😍") and sell ("😡") signals. Additionally, it displays green and red dots indicating the cryptocurrency's strength or weakness against BTC on that candle, which can be enabled or disabled as desired. This basically occurs when there are EMA9 and EMA21 crossovers on the selected timeframe.
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# Bull and Bear Targets:
-For Uptrends: When a crossover occurs that indicates that the price is going up, the code calculates two price targets. These targets are defined based on the difference between the highest price observed and the average of the prices at the crossovers. This helps predict how far the price can go before encountering resistance.
-For Downtrends: Similarly, when the price crosses downwards, the code calculates price targets that may indicate where the price may find support. These targets are determined by the difference between the average of the prices at the bearish crossovers and the lowest observed price.
TrendScalp BotTrendScalp Bot for FOREX Trading only. This bot uses news reports to help with finding entry and exit points.
The Dragons Maw [inspired by Kioseff Trading]Inspired by Kioseff Tradings Monte Carlo Simulation, I modified their code a bit to ...
... just for fun.
I didn't change anything in the Montecarlo Mechanism but only messed around with the visual representation of the data.
This indicator doesn't provide a valid forecast. So, don't take it serious!
Nevertheless, it's nice anyway. You can reduce the fire by reducing the 'Simulations'-Parameter.
Multi-Strategy Trading System - BearMarket SurvivorThis strategy combines multiple technical analysis methods to capture buying and selling opportunities in the cryptocurrency market. Designed for traders seeking consistency and risk control, the strategy uses classic indicators (such as MACD, EMA, MA and Stochastic Oscillator) combined with advanced risk management and time filters.
Shifted Chart (5 Minutes Ahead - Window 2)//@version=5
indicator("Shifted Chart (5 Minutes Ahead - Window 2)", overlay=false)
// --- Input for Time Shift ---
time_shift_minutes = 5 // Fixed 5-minute shift
// --- Timeframe Adjustment ---
bars_to_shift = math.round(time_shift_minutes / (timeframe.multiplier * 1)) // Convert minutes to bars based on the chart timeframe
bars_to_shift := bars_to_shift > 0 ? bars_to_shift : 1 // Ensure at least 1 bar shift
// --- Shifted OHLC Values ---
shifted_open = open
shifted_high = high
shifted_low = low
shifted_close = close
// --- Plot the Shifted Data ---
plotcandle(shifted_open, shifted_high, shifted_low, shifted_close, title="Shifted Candles", color=color.new(color.green, 0))
Forex Chart Window 2 (5 Minutes Ahead)//@version=5
indicator("Forex Chart Window 2 (5 Minutes Ahead)", overlay=true)
// --- Input for Time Shift ---
time_shift_minutes = input.int(5, title="Time Shift (Minutes)", minval=1, tooltip="Shift data forward by this amount in minutes")
// --- Timeframe Adjustment ---
bars_to_shift = math.round(time_shift_minutes / (timeframe.multiplier * 1)) // Convert minutes to bars based on the timeframe
bars_to_shift := bars_to_shift > 0 ? bars_to_shift : 1 // Ensure at least 1 bar shift
// --- Shifted OHLC Values ---
shifted_open = open
shifted_high = high
shifted_low = low
shifted_close = close
// --- Plot the Shifted Data ---
plot(shifted_close, color=color.new(color.green, 0), title="Shifted Close")
plotcandle(shifted_open, shifted_high, shifted_low, shifted_close, color=color.new(color.blue, 0), title="Shifted Candlesticks")
Lux Algo Dmytro Price Action IndicatorThe Lux Algo Dmytro Price Action Indicator is a custom TradingView script designed to help traders identify key price action patterns and potential market reversals. This indicator is based on swing highs and lows, providing a clear visual representation of market structure and momentum shifts.
FuTech : Earnings (All 269 Fundamental Metrics of Tradingview)FuTech : Earnings Indicator
The FuTech : Earnings Indicator is a revolutionary tool, offering the most comprehensive integration of all 269 fundamental financial metrics available from the TradingView platform.
This groundbreaking indicator is designed to empower financial researchers, traders, investors, and analysts with an unmatched depth of data, enabling superior analysis and decision-making.
Overview
"FuTech : Earnings Indicator" is the first-ever indicator to provide a holistic comparison of fundamental financial metrics for any stock, covering quarterly, yearly, and trailing twelve months (TTM) periods.
This tool brings together key financial data from income statements, balance sheets, cash flows, and other critical metrics found in company annual reports.
It also incorporates additional unique features like per-employee data, R&D expenses, and capital expenditures (CapEx), which are typically hidden within dense financial statements of Annual Reports.
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Key Features and Capabilities
1. Comprehensive Financial Metrics
- "FuTech : Earnings Indicator" offers access to all 269 fundamental metrics available on TradingView platform. This includes widely used data such as revenue, profit margins, and EPS, alongside more niche metrics like R&D expenditure, employee efficiency, and financial scores developed by renowned analysts.
- Users can explore income statement data (e.g., net income, gross profit), balance sheet items (e.g., total assets, liabilities), cash flow metrics, and other financial statistics such as Altman Score, per employee expenses etc. in unparalleled detail.
2. Comparison Across Time Periods
- "FuTech : Earnings Indicator" allows users to analyze data for:
- Quarterly periods (e.g., Q1, Q2, Q3, Q4).
- Yearly comparisons for a broad historical view.
- TTM analysis to observe the most recent trends and developments.
- Users can select a minimum of 4 periods up to an unlimited range for detailed comparisons in both quarter.
3. Dynamic Data Display
- Users can select up to 5 key metrics alongside the stock price column to focus their analysis on the most relevant data points.
- Highlighting with green and red symbols offers an intuitive and visual representation:
- Green : Positive trends or improvements.
- Red : Negative trends or deteriorations.
4. Automated Averages
- "FuTech : Earnings Indicator" automatically calculates averages of selected metrics across the chosen periods. This feature helps users quickly identify performance trends and smooth out anomalies, enabling faster and more reliable research.
5. Designed for Research Excellence
- FuTech serves a wide audience, including:
- Corporate finance professionals who need a deep dive into financial metrics.
- Individual investors seeking robust tools for investment analysis.
- Broking companies and equity research analysts performing stock analysis.
- Traders looking to incorporate fundamental metrics into their strategies.
- Technical analysts seeking a better understanding of price behavior in relation to fundamentals.
- Fundamental research aspirants who want an edge in their learning process.
6. Unmatched Detail for Deeper Insights
- By pulling all 269 Financial metrics from the TradingView, "FuTech : Earnings Indicator" enables:
- Cross-comparison of a stock’s performance with its historical benchmarks.
- Evaluation of rare data like R&D expenses, CapEx trends, and employee efficiency ratios for enhanced investment insights.
- This ensures users can study stocks in greater depth than ever before.
7. Enhanced Usability
- Simple to use and visually appealing, "FuTech : Earnings Indicator" is designed with researchers in mind.
- Its intuitive interface ensures even novice users can navigate the wealth of data without feeling overwhelmed.
Applications of FuTech : Earnings Indicator
FuTech : Earnings Indicator is incredibly versatile and has applications in diverse fields of financial research and trading:
1. Corporate Finance
- Professionals in corporate finance can leverage "FuTech : Earnings Indicator" to benchmark company performance, study efficiency ratios, and evaluate financial health across various metrics.
2. Investors and Traders
- Long-term investors can use the tool to study the fundamental strengths of a stock before making buy-and-hold decisions.
- Traders can incorporate "FuTech : Earnings Indicator" into their analysis to align comprehensive fundamental trends with their targeted technical signals.
3. Equity Research Analysts
- Analysts can streamline their workflows by quickly identifying trends, outliers, and averages across large datasets.
4. Education and Research
- "FuTech : Earnings Indicator" is ideal for students and aspiring financial analysts who want a practical tool for understanding real-world data.
How FuTech : Earnings Indicator Stands Out
1. First-Ever Integration of All Financial Metrics
- It's an exclusive tool which offers the ability to explore all 269 financial metrics available on TradingView for a single stock research in-depth for quarters, years or TTM periods.
2. Period Customization
- Users have complete flexibility to select and analyze data across any range of time periods, allowing for customized insights tailored to specific research goals.
3. Data Visualization
- The intuitive use of color-coded symbols (green for positive trends, red for negative) makes complex data easy to interpret at a glance.
4. Actionable Insights
- The automated average calculations provide actionable insights for making informed decisions without manual computations.
5. Unique Metrics
- Metrics such as research and development costs, CapEx, and per-employee efficiency data offer unique angles that aren’t typically available in traditional analysis tools.
Why to Use FuTech : Earnings Indicator ?
1. Boost Your Research Power
- With FuTech, you can unlock a world of data that gives you the edge in analyzing stocks. Whether you’re a seasoned analyst or a beginner, this tool offers something for everyone.
2. Save Time and Effort
- The automated features and intuitive interface eliminate the need for time-consuming manual calculations and formatting.
3. Make Better Decisions
- "FuTech : Earnings Indicator's" detailed comparison capabilities and insightful visual aids allow for more accurate assessments of a stock’s performance and potential.
4. Broad Appeal
- From individual investors to financial institutions, FuTech is a valuable tool for anyone in the world of finance.
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Conclusion
- The FuTech : Earnings Indicator is a must-have for anyone serious about financial analysis.
- It combines the depth of all 269 fundamental metrics with intuitive tools for comparison, visualization, and calculation.
- Designed for ease of use and powerful insights, FuTech : Earnings Indicator is set to transform the way financial data is analyzed and understood.
Thank you !
Boost, Share, Follow, and Enjoy with FuTech!
Jai Swaminarayan Dasna Das !
He Hari ! Bas Ek Tu Raji Tha !
Black Gold Barrel BY SonarTradesThe "Black Gold Barrel BY SonarTrades" indicator is a powerful visual tool designed for crude oil traders. This indicator combines session highlighting and momentum candle detection to help traders focus on critical trading periods and spot significant price action movements.
The indicator highlights two key trading sessions in IST (Indian Standard Time) converted to UTC for accurate plotting on the chart:
Session 1: 11:30 AM to 4:30 PM IST (highlighted in blue).
Session 2: 5:30 PM to 11:30 PM IST (highlighted in red).
These sessions represent high-activity periods
This tool provides a perfect balance of session tracking and candle dynamics for traders looking to capitalize on short-term opportunities in the crude oil market.
The candles in black represents the body ratio < 0.52% which determine the weakness of market and helps traders to change directions. This calculation is discovered by high trading knowledge and back testing's & Front Testing's
M2 Money Shift for Bitcoin [SAKANE]M2 Money Shift for Bitcoin was developed to visualize the impact of M2 Money, a macroeconomic indicator, on the Bitcoin market and to support trade analysis.
Bitcoin price fluctuations have a certain correlation with cycles in M2 money supply.In particular, it has been noted that changes in M2 supply can affect the bitcoin price 70 days in advance.Very high correlations have been observed in recent years in particular, making it useful as a supplemental analytical tool for trading.
Support for M2 data from multiple countries
M2 supply data from the U.S., Europe, China, Japan, the U.K., Canada, Australia, and India are integrated and all are displayed in U.S. dollar equivalents.
Slide function
Using the "Slide Days Forward" setting, M2 data can be slid up to 500 days, allowing for flexible analysis that takes into account the time difference from the bitcoin price.
Plotting Total Liquidity
Plot total liquidity (in trillions of dollars) by summing the M2 supply of multiple countries.
How to use
After applying the indicator to the chart, activate the M2 data for the required country from the settings screen. 2.
2. adjust "Slide Days Forward" to analyze the relationship between changes in M2 supply and bitcoin price
3. refer to the Gross Liquidity plot to build a trading strategy that takes into account macroeconomic influences.
Notes.
This indicator is an auxiliary tool for trade analysis and does not guarantee future price trends.
The relationship between M2 supply and bitcoin price depends on many factors and should be used in conjunction with other analysis methods.
Simple Decesion Matrix Classification Algorithm [SS]Hello everyone,
It has been a while since I posted an indicator, so thought I would share this project I did for fun.
This indicator is an attempt to develop a pseudo Random Forest classification decision matrix model for Pinescript.
This is not a full, robust Random Forest model by any stretch of the imagination, but it is a good way to showcase how decision matrices can be applied to trading and within Pinescript.
As to not market this as something it is not, I am simply calling it the "Simple Decision Matrix Classification Algorithm". However, I have stolen most of the aspects of this machine learning algo from concepts of Random Forest modelling.
How it works:
With models like Support Vector Machines (SVM), Random Forest (RF) and Gradient Boosted Machine Learning (GBM), which are commonly used in Machine Learning Classification Tasks (MLCTs), this model operates similarity to the basic concepts shared amongst those modelling types. While it is not very similar to SVM, it is very similar to RF and GBM, in that it uses a "voting" system.
What do I mean by voting system?
How most classification MLAs work is by feeding an input dataset to an algorithm. The algorithm sorts this data, categorizes it, then introduces something called a confusion matrix (essentially sorting the data in no apparently order as to prevent over-fitting and introduce "confusion" to the algorithm to ensure that it is not just following a trend).
From there, the data is called upon based on current data inputs (so say we are using RSI and Z-Score, the current RSI and Z-Score is compared against other RSI's and Z-Scores that the model has saved). The model will process this information and each "tree" or "node" will vote. Then a cumulative overall vote is casted.
How does this MLA work?
This model accepts 2 independent variables. In order to keep things simple, this model was kept as a three node model. This means that there are 3 separate votes that go in to get the result. A vote is casted for each of the two independent variables and then a cumulative vote is casted for the overall verdict (the result of the model's prediction).
The model actually displays this system diagrammatically and it will likely be easier to understand if we look at the diagram to ground the example:
In the diagram, at the very top we have the classification variable that we are trying to predict. In this case, we are trying to predict whether there will be a breakout/breakdown outside of the normal ATR range (this is either yes or no question, hence a classification task).
So the question forms the basis of the input. The model will track at which points the ATR range is exceeded to the upside or downside, as well as the other variables that we wish to use to predict these exceedences. The ATR range forms the basis of all the data flowing into the model.
Then, at the second level, you will see we are using Z-Score and RSI to predict these breaks. The circle will change colour according to "feature importance". Feature importance basically just means that the indicator has a strong impact on the outcome. The stronger the importance, the more green it will be, the weaker, the more red it will be.
We can see both RSI and Z-Score are green and thus we can say they are strong options for predicting a breakout/breakdown.
So then we move down to the actual voting mechanisms. You will see the 2 pink boxes. These are the first lines of voting. What is happening here is the model is identifying the instances that are most similar and whether the classification task we have assigned (remember out ATR exceedance classifier) was either true or false based on RSI and Z-Score.
These are our 2 nodes. They both cast an individual vote. You will see in this case, both cast a vote of 1. The options are either 1 or 0. A vote of 1 means "Yes" or "Breakout likely".
However, this is not the only voting the model does. The model does one final vote based on the 2 votes. This is shown in the purple box. We can see the final vote and result at the end with the orange circle. It is 1 which means a range exceedance is anticipated and the most likely outcome.
The Data Table Component
The model has many moving parts. I have tried to represent the pivotal functions diagrammatically, but some other important aspects and background information must be obtained from the companion data table.
If we bring back our diagram from above:
We can see the data table to the left.
The data table contains 2 sections, one for each independent variable. In this case, our independent variables are RSI and Z-Score.
The data table will provide you with specifics about the independent variables, as well as about the model accuracy and outcome.
If we take a look at the first row, it simply indicates which independent variable it is looking at. If we go down to the next row where it reads "Weighted Impact", we can see a corresponding percent. The "weighted impact" is the amount of representation each independent variable has within the voting scheme. So in this case, we can see its pretty equal, 45% and 55%, This tells us that there is a slight higher representation of z-score than RSI but nothing to worry about.
If there was a major over-respresentation of greater than 30 or 40%, then the model would risk being skewed and voting too heavily in favour of 1 variable over the other.
If we move down from there we will see the next row reads "independent accuracy". The voting of each independent variable's accuracy is considered separately. This is one way we can determine feature importance, by seeing how well one feature augments the accuracy. In this case, we can see that RSI has the greatest importance, with an accuracy of around 87% at predicting breakouts. That makes sense as RSI is a momentum based oscillator.
Then if we move down one more, we will see what each independent feature (node) has voted for. In this case, both RSI and Z-Score voted for 1 (Breakout in our case).
You can weigh these in collaboration, but its always important to look at the final verdict of the model, which if we move down, we can see the "Model prediction" which is "Bullish".
If you are using the ATR breakout, the model cannot distinguish between "Bullish" or "Bearish", must that a "Breakout" is likely, either bearish or bullish. However, for the other classification tasks this model can do, the results are either Bullish or Bearish.
Using the Function:
Okay so now that all that technical stuff is out of the way, let's get into using the function. First of all this function innately provides you with 3 possible classification tasks. These include:
1. Predicting Red or Green Candle
2. Predicting Bullish / Bearish ATR
3. Predicting a Breakout from the ATR range
The possible independent variables include:
1. Stochastics,
2. MFI,
3. RSI,
4. Z-Score,
5. EMAs,
6. SMAs,
7. Volume
The model can only accept 2 independent variables, to operate within the computation time limits for pine execution.
Let's quickly go over what the numbers in the diagram mean:
The numbers being pointed at with the yellow arrows represent the cases the model is sorting and voting on. These are the most identical cases and are serving as the voting foundation for the model.
The numbers being pointed at with the pink candle is the voting results.
Extrapolating the functions (For Pine Developers:
So this is more of a feature application, so feel free to customize it to your liking and add additional inputs. But here are some key important considerations if you wish to apply this within your own code:
1. This is a BINARY classification task. The prediction must either be 0 or 1.
2. The function consists of 3 separate functions, the 2 first functions serve to build the confusion matrix and then the final "random_forest" function serves to perform the computations. You will need all 3 functions for implementation.
3. The model can only accept 2 independent variables.
I believe that is the function. Hopefully this wasn't too confusing, it is very statsy, but its a fun function for me! I use Random Forest excessively in R and always like to try to convert R things to Pinescript.
Hope you enjoy!
Safe trades everyone!
Weekly Bullish Pattern DetectorThis script is a TradingView Pine Script designed to detect a specific bullish candlestick pattern on the weekly chart. Below is a detailed breakdown of its components:
1. Purpose
The script identifies a four-candle bullish pattern where:
The first candle is a long green (bullish) candlestick.
The second and third candles are small-bodied candles, signifying consolidation or indecision.
The fourth candle is another long green (bullish) candlestick.
When this pattern is detected, the script:
Marks the chart with a visual label.
Optionally triggers an alert to notify the trader.
2. Key Features
Overlay on Chart:
indicator("Weekly Bullish Pattern Detector", overlay=true) ensures the indicator draws directly on the price chart.
Customizable Inputs:
length (Body Size Threshold):
Defines the minimum percentage of the total range that qualifies as a "long" candle body (default: 14%).
smallCandleThreshold (Small Candle Body Threshold):
Defines the maximum percentage of the total range that qualifies as a "small" candle body (default: 10%).
Candlestick Property Calculations:
bodySize: Measures the absolute size of the candle body (close - open).
totalRange: Measures the total high-to-low range of the candle.
bodyPercentage: Calculates the proportion of the body size relative to the total range ((bodySize / totalRange) * 100).
isGreen and isRed: Identify bullish (green) or bearish (red) candles based on their open and close prices.
Pattern Conditions:
longGreenCandle:
Checks if the candle is bullish (isGreen) and its body percentage exceeds the defined length threshold.
smallCandle:
Identifies small-bodied candles where the body percentage is below the smallCandleThreshold.
consolidation:
Confirms the second and third candles are both small-bodied (smallCandle and smallCandle ).
Bullish Pattern Detection:
bullishPattern:
Detects the full four-candle sequence:
The first candle (longGreenCandle ) is a long green candle.
The second and third candles (consolidation) are small-bodied.
The fourth candle (longGreenCandle) is another long green candle.
Visualization:
plotshape(bullishPattern):
Draws a green label ("Pattern") below the price chart whenever the pattern is detected.
Alert Notification:
alertcondition(bullishPattern):
Sends an alert with the message "Bullish Pattern Detected on Weekly Chart" whenever the pattern is found.
3. How It Works
Evaluates Candle Properties:
For each weekly candle, the script calculates its size, range, and body percentage.
Identifies Each Component of the Pattern:
Checks for a long green candle (first and fourth).
Verifies the presence of two small-bodied candles (second and third).
Detects and Marks the Pattern:
Confirms the sequence and marks the chart with a label if the pattern is complete.
Sends Alerts:
Notifies the trader when the pattern is detected.
4. Use Cases
This script is ideal for:
Swing Traders:
Spotting weekly patterns that indicate potential bullish continuations.
Breakout Traders:
Identifying consolidation zones followed by upward momentum.
Pattern Recognition:
Automatically detecting a commonly used bullish formation.
5. Key Considerations
Timeframe: Works best on weekly charts.
Customization: The thresholds for "long" and "small" candles can be adjusted to suit different markets or volatility levels.
Limitations:
It doesn't confirm the pattern's success; further analysis (e.g., volume, support/resistance levels) may be required for validation
ATR% Multiple from Key Moving AverageThis script gives signal when the ATR% multiple from any chosen moving average is beyond the configurable threshold value. This indicator quantifies how extended the stock is from a given key moving average.
A lot of traders use ATR% multiple from 10DMA, 21EMA, 50SMA or 200SMA to determine how extended a stock is and accordingly sell partials or exit. By default the indicator takes 50SMA and when the ATR% multiple is greater than 7 then it gives the signal to take partials. You can back test this indicator with previous trades and determine the ideal threshold for the signal. For small and midcaps a threshold of 7 to 10 ATR% multiples from 50SMA is where partials can be taken while large caps can revert to mean even earlier at 3 to 5 ATR% multiples from 50SMA.
You can modify this script and use it anyway you please as long as you make it opensource on TradingView.
Hybrid Triple Exponential Smoothing🙏🏻 TV, I present you HTES aka Hybrid Triple Exponential Smoothing, designed by Holt & Winters in the US, assembled by me in Saint P. I apply exponential smoothing individually to the data itself, then to residuals from the fitted values, and lastly to one-point forecast (OPF) errors, hence 'hybrid'. At the same time, the method is a closed-form solution and purely online, no need to make any recalculations & optimize anything, so the method is O(1).
^^ historical OPFs and one-point forecasting interval plotted instead of fitted values and prediction interval
Before the How-to, first let me tell you some non-obvious things about Triple Exponential smoothing (and about Exponential Smoothing in general) that not many catch. Expo smoothing seems very straightforward and obvious, but if you look deeper...
1) The whole point of exponential smoothing is its incremental/online nature, and its O(1) algorithm complexity, making it dope for high-frequency streaming data that is also univariate and has no weights. Consequently:
- Any hybrid models that involve expo smoothing and any type of ML models like gradient boosting applied to residuals rarely make much sense business-wise: if you have resources to boost the residuals, you prolly have resources to use something instead of expo smoothing;
- It also concerns the fashion of using optimizers to pick smoothing parameters; honestly, if you use this approach, you have to retrain on each datapoint, which is crazy in a streaming context. If you're not in a streaming context, why expo smoothing? What makes more sense is either picking smoothing parameters once, guided by exogenous info, or using dynamic ones calculated in a minimalistic and elegant way (more on that in further drops).
2) No matter how 'right' you choose the smoothing parameters, all the resulting components (level, trend, seasonal) are not pure; each of them contains a bit of info from the other components, this is just how non-sequential expo smoothing works. You gotta know this if you wanna use expo smoothing to decompose your time series into separate components. The only pure component there, lol, is the residuals;
3) Given what I've just said, treating the level (that does contain trend and seasonal components partially) as the resulting fit is a mistake. The resulting fit is level (l) + trend (b) + seasonal (s). And from this fit, you calculate residuals;
4) The residuals component is not some kind of bad thing; it is simply the component that contains info you consciously decide not to include in your model for whatever reason;
5) Forecasting Errors and Residuals from fitted values are 2 different things. The former are deltas between the forecasts you've made and actual values you've observed, the latter are simply differences between actual datapoints and in-sample fitted values;
6) Residuals are used for in-sample prediction intervals, errors for out-of-sample forecasting intervals;
7) Choosing between single, double, or triple expo smoothing should not be based exclusively on the nature of your data, but on what you need to do as well. For example:
- If you have trending seasonal data and you wanna do forecasting exclusively within the expo smoothing framework, then yes, you need Triple Exponential Smoothing;
- If you wanna use prediction intervals for generating trend-trading signals and you disregard seasonality, then you need single (simple) expo smoothing, even on trending data. Otherwise, the trend component will be included in your model's fitted values → prediction intervals.
8) Kind of not non-obvious, but when you put one smoothing parameter to zero, you basically disregard this component. E.g., in triple expo smoothing, when you put gamma and beta to zero, you basically end up with single exponential smoothing.
^^ data smoothing, beta and gamma zeroed out, forecasting steps = 0
About the implementation
* I use a simple power transform that results in a log transform with lambda = 0 instead of the mainstream-used transformers (if you put lambda on 2 in Box-Cox, you won't get a power of 2 transform)
* Separate set of smoothing parameters for data, residuals, and errors smoothing
* Separate band multipliers for residuals and errors
* Both typical error and typical residuals get multiplied by math.sqrt(math.pi / 2) in order to approach standard deviation so you can ~use Z values and get more or less corresponding probabilities
* In script settings → style, you can switch on/off plotting of many things that get calculated internally:
- You can visualize separate components (just remember they are not pure);
- You can switch off fit and switch on OPF plotting;
- You can plot residuals and their exponentially smoothed typical value to pick the smoothing parameters for both data and residuals;
- Or you might plot errors and play with data smoothing parameters to minimize them (consult SAE aka Sum of Absolute Errors plot);
^^ nuff said
More ideas on how to use the thing
1) Use Double Exponential Smoothing (data gamma = 0) to detrend your time series for further processing (Fourier likes at least weakly stationary data);
2) Put single expo smoothing on your strategy/subaccount equity chart (data alpha = data beta = 0), set prediction interval deviation multiplier to 1, run your strat live on simulator, start executing on real market when equity on simulator hits upper deviation (prediction interval), stop trading if equity hits lower deviation on simulator. Basically, let the strat always run on simulator, but send real orders to a real market when the strat is successful on your simulator;
3) Set up the model to minimize one-point forecasting errors, put error forecasting steps to 1, now you're doing nowcasting;
4) Forecast noisy trending sine waves for fun.
^^ nuff said 2
All Good TV ∞
