ADX Full [Titans_Invest]ADX Full
This is, without a doubt, the most complete ADX indicator available on TradingView — and quite possibly the most advanced in the world. We took the classic ADX structure and fully optimized it, preserving its essence while elevating its functionality to a whole new level. Every aspect has been enhanced — from internal logic to full visual customization. Now you can see exactly what’s happening inside the indicator in real time, with tags, flags, and informative levels. This indicator includes over 22 long entry conditions and 22 short entry conditions , covering absolutely every possibility the ADX can offer. Everything is transparent, adjustable, and ready to fit seamlessly into any professional trading strategy. This isn’t just another ADX — it’s the definitive ADX, built for traders who take the market seriously.
⯁ WHAT IS THE ADX❓
The Average Directional Index (ADX) is a technical analysis indicator developed by J. Welles Wilder. It measures the strength of a trend in a market, regardless of whether the trend is up or down.
The ADX is an integral part of the Directional Movement System, which also includes the Plus Directional Indicator (+DI) and the Minus Directional Indicator (-DI). By combining these components, the ADX provides a comprehensive view of market trend strength.
⯁ HOW TO USE THE ADX❓
The ADX is calculated based on the moving average of the price range expansion over a specified period (usually 14 periods). It is plotted on a scale from 0 to 100 and has three main zones:
Strong Trend: When the ADX is above 25, indicating a strong trend.
Weak Trend: When the ADX is below 20, indicating a weak or non-existent trend.
Neutral Zone: Between 20 and 25, where the trend strength is unclear.
⯁ ENTRY CONDITIONS
The conditions below are fully flexible and allow for complete customization of the signal.
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🔹 CONDITIONS TO BUY 📈
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• Signal Validity: The signal will remain valid for X bars .
• Signal Sequence: Configurable as AND or OR .
🔹 +DI > -DI
🔹 +DI < -DI
🔹 +DI > ADX
🔹 +DI < ADX
🔹 -DI > ADX
🔹 -DI < ADX
🔹 ADX > Threshold
🔹 ADX < Threshold
🔹 +DI > Threshold
🔹 +DI < Threshold
🔹 -DI > Threshold
🔹 -DI < Threshold
🔹 +DI (Crossover) -DI
🔹 +DI (Crossunder) -DI
🔹 +DI (Crossover) ADX
🔹 +DI (Crossunder) ADX
🔹 +DI (Crossover) Threshold
🔹 +DI (Crossunder) Threshold
🔹 -DI (Crossover) ADX
🔹 -DI (Crossunder) ADX
🔹 -DI (Crossover) Threshold
🔹 -DI (Crossunder) Threshold
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🔸 CONDITIONS TO SELL 📉
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• Signal Validity: The signal will remain valid for X bars .
• Signal Sequence: Configurable as AND or OR .
🔸 +DI > -DI
🔸 +DI < -DI
🔸 +DI > ADX
🔸 +DI < ADX
🔸 -DI > ADX
🔸 -DI < ADX
🔸 ADX > Threshold
🔸 ADX < Threshold
🔸 +DI > Threshold
🔸 +DI < Threshold
🔸 -DI > Threshold
🔸 -DI < Threshold
🔸 +DI (Crossover) -DI
🔸 +DI (Crossunder) -DI
🔸 +DI (Crossover) ADX
🔸 +DI (Crossunder) ADX
🔸 +DI (Crossover) Threshold
🔸 +DI (Crossunder) Threshold
🔸 -DI (Crossover) ADX
🔸 -DI (Crossunder) ADX
🔸 -DI (Crossover) Threshold
🔸 -DI (Crossunder) Threshold
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🤖 AUTOMATION 🤖
• You can automate the BUY and SELL signals of this indicator.
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⯁ UNIQUE FEATURES
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Signal Validity: The signal will remain valid for X bars
Signal Sequence: Configurable as AND/OR
Condition Table: BUY/SELL
Condition Labels: BUY/SELL
Plot Labels in the Graph Above: BUY/SELL
Automate and Monitor Signals/Alerts: BUY/SELL
Signal Validity: The signal will remain valid for X bars
Signal Sequence: Configurable as AND/OR
Table of Conditions: BUY/SELL
Conditions Label: BUY/SELL
Plot Labels in the graph above: BUY/SELL
Automate & Monitor Signals/Alerts: BUY/SELL
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📜 SCRIPT : ADX Full
🎴 Art by : @Titans_Invest & @DiFlip
👨💻 Dev by : @Titans_Invest & @DiFlip
🎑 Titans Invest — The Wizards Without Gloves 🧤
✨ Enjoy!
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o Mission 🗺
• Inspire Traders to manifest Magic in the Market.
o Vision 𐓏
• To elevate collective Energy 𐓷𐓏
Bitcoin (Criptomoeda)
Arrow's Flexible MA Cross Strategy [API Ready]Arrow's High-Frequency MA Cross Scalper By: © ArrowTrade
=== OVERVIEW ===
This strategy is engineered for high-frequency trading and scalping opportunities, utilizing rapid Moving Average (MA) crossovers coupled with essential filters and precise risk management tools. Developed by ArrowTrade, it's specifically designed for seamless integration with automated trading systems via API (webhooks, etc.), enabling swift execution of short-term signals.
While adaptable, its core design favors capturing small, quick price movements typical of scalping approaches.
=== CORE LOGIC ===
Entry Signal: Primary entries are triggered by the crossover/crossunder of a Fast MA and a Slow MA. Configurable MA types (EMA, SMA, WMA, HMA, VWMA) and periods allow fine-tuning signal sensitivity for different market rhythms.
Trend Filter (Optional): A longer-term MA acts as a regime filter. When enabled, entries are only permitted in the direction of this broader trend, aiming to avoid counter-trend scalps in strongly directional markets.
Confirmation Filters (Optional):
ATR Volatility Filter: Designed to pause entries during extremely flat or "dead" markets where volatility drops below a dynamic threshold (based on average ATR). This helps prevent whipsaws in non-trending, low-energy conditions.
Volume Filter: Validates entry signals by requiring a minimum level of market participation (volume compared to its moving average). This helps avoid entries based on low-liquidity spikes or insignificant price action.
=== RISK MANAGEMENT SUITE (Crucial for Scalping) ===
Initial Volatility Stop: An ATR-based initial stop provides an objective starting point for risk definition on each trade, adapting to recent volatility. Tighter multipliers are often preferred for scalping.
ATR Trailing Stop: Essential for dynamic markets. Trails the stop loss behind favorable price action, aiming to protect profits on successful scalps while cutting losses relatively quickly if the move reverses. Fine-tune the ATR period and multiplier for desired responsiveness.
Break-Even Stop (Optional): Can be configured to automatically move the stop to entry (plus buffer) once TP1 is hit or price travels a specific ATR distance. Useful for quickly neutralizing risk on a trade that has shown initial promise.
Dual Take Profit Levels:
TP1: Designed for rapid, partial profit-taking. Set a tight percentage target and define the portion (%) of the position to close (e.g., 50%). This secures initial gains quickly, a key element in many scalping systems.
TP2: Target for the remaining portion of the position, aiming for a slightly larger move if the initial momentum continues.
Fixed Quantity Sizing: Enables precise control over position size per trade, crucial for consistent risk application in high-frequency environments and straightforward API command generation.
=== INTENDED USE: HIGH-FREQUENCY & API AUTOMATION ===
This strategy is purpose-built for traders leveraging API automation for high-frequency scalping.
Parameter Tuning for Scalping: Achieve higher signal frequency by using:
Shorter Fast MA Period and Slow MA Period.
Faster MA Types like EMA or HMA.
Tighter Initial Stop ATR Multiplier and Trailing ATR Multiplier.
Smaller TP1 Target (%) and potentially TP2 Target (%).
Careful adjustment of ATR Volatility Filter and Volume Filter thresholds to balance signal frequency with noise reduction.
API Integration: The strategy's clear entry (MA Cross + Filters OK) and exit logic (SL Hit, TP Limit Hit) generates unambiguous signals. Use TradingView alerts (alertcondition or native strategy alerts) configured with webhook URLs to trigger your external trading bot (e.g., 3Commas, PineConnector, custom solutions) for near-instantaneous order execution. The fixed quantity simplifies the payload sent to your API endpoint.
=== RISK MANAGEMENT FOR SCALPING ===
High-frequency trading requires extremely disciplined risk management:
Position Size (qtyValue): CRITICAL. Calculate this based on a small, fixed percentage of your capital risked per trade (e.g., 0.25% - 1%) relative to your initial stop distance. Due to the high number of trades, even small consistent losses can accumulate rapidly if sizing is too large.
Stop Loss: NON-NEGOTIABLE. Always use stops. Scalping often benefits from tighter initial stops combined with an aggressive trailing stop to protect small gains.
Commissions & Slippage: Account for these meticulously in settings and backtests. High trade frequency means these costs significantly impact net profitability. Ensure commission_value and slippage inputs reflect your actual trading environment.
Overfitting: Be highly aware of overfitting during optimization, especially with many parameters. Validate results on out-of-sample data or through forward testing.
=== CUSTOMIZATION & OPTIMIZATION ===
Explore different Signal Source options (e.g., hlc3) for potentially smoother MA signals.
Systematically optimize MA lengths, filter parameters, ATR multipliers, and TP percentages using TradingView's Strategy Tester, focusing on metrics like Profit Factor, Sharpe Ratio (or Sortino), and Net Profit while keeping Max Drawdown within acceptable limits.
Test different combinations of the optional filters. Sometimes fewer filters can perform better.
=== DISCLAIMER ===
Trading involves substantial risk. Past performance is not indicative of future results.
This script is provided for educational and informational purposes only and does not constitute financial advice.
© ArrowTrade makes no guarantees regarding the performance or profitability of this strategy.
You are solely responsible for all trading decisions and risk management. Always perform thorough testing and validation before deploying any strategy with real capital. Adjust all settings, especially risk parameters, to your specific needs.
RTB - Momentum Breakout Strategy V3
📈 RTB - Momentum Breakout Strategy V3 is a directional breakout strategy based on momentum. It combines exponential moving averages (EMAs), RSI, and recent support/resistance levels to detect breakout entries with trend confirmation. The system includes dynamic risk management using ATR-based stop-loss and trailing stop levels. Webhook alerts are supported for external automated trading integrations.
🔎 The strategy was backtested using default parameters on BTCUSDT Futures (Bybit) with 4-hour timeframe and a 0.05% commission per trade.
⚠️ This script is for educational purposes only and does not constitute financial advice. Always do your own research before trading.
Global M2 Money Supply Top20 + Offset & WaveThe M2 Top20 is a global aggregation of the M2 money supply from the 20 largest economies in the world , providing a comprehensive view of the total liquidity in the global financial system. It is expressed in trillions of USD.
This script calculates and visualizes the M2 Money Supply of the Top 20 Global Economies, adjusted to various timeframes (4H, 1D, 1W, 1M) with customizable offset adjustments (in days) from -1000 days to +1000 days. This indicator includes data from the Americas, Europe, Africa, and the Asia Middle East , offering a diverse and balanced representation of major economic regions. The M2 of each country has been converted to USD.
Additionally, the user can set a minimum and maximum offset to create a wave around the main offset and expand the comparison.
Combining these options, this indicator enables users to visualize a range of the global money supply, making it useful for market analysis, economic forecasting, and understanding macroeconomic trends. This indicator is particularly valuable for traders and analysts interested in understanding the dynamics of global monetary systems and their potential impact on financial markets.
Key Features:
Global M2 Money Supply calculation from the Top 20 Economies.
Adjustable Offset: Adjust the offset to align the indicator with the best bar. Adjustment in days, usable on different timeframes (1D, 1W, 4H, 1M).
Wave Projection: Displays a "probability cloud"—a smoothed area that shows the probable path of Bitcoin, derived from shifts in global liquidity.
Min/Max Offset Adjustments: Customizable offsets allow you to determine the range of future windows, helping to shape the wave and better identify liquidity-driven turning points.
Use Cases:
Economic Forecasting: Identify trends in global money supply and their potential market impact (e.g., historically leads Bitcoin price by +/- 78 days to +/-108 days).
Market Analysis: Track the growth or contraction of money supply across key economies.
Macro-Economic Analysis: Understand the relationship between monetary policies and market performance.
How to use:
Add the indicator to your chart.
Set the timeframe to 1D to customize the offset.
Set the Offset (in days).
Set the Offset Range Minimum and Maximum.
Show/Hide the Range Wave
.
Use offset = 0 to have the indicator align directly with the current data, without any shift, providing a baseline for comparison with the most recent market conditions.
Countries included in the M2 Top20:
China (CN), Japan (JP), South Korea (KR), Hong Kong (HK), Taiwan (TW), India (IN), Saudi Arabia (SA), Thailand (TH), Vietnam (VN), United Arab Emirates (AE), Malawi (MW) – Africa, United States (US), Canada (CA), Brazil (BR), Mexico (MX), Eurozone (EU), United Kingdom (GB), Russia (RU), Poland (PL), Switzerland (CH).
These countries were selected from the ranking of the World Economy Indicator of Trading View .
WAGMI LAB Trend Reversal Indicator HMA-Kahlman (m15)WAGMI HMA-Kahlman Trend Reversal Indicator
This indicator combines the Hull Moving Average (HMA) with the Kahlman filter to provide a dynamic trend reversal signal, perfect for volatile assets like Bitcoin. The strategy works particularly well on lower timeframes, making it ideal for intraday trading and fast-moving markets.
Key Features:
Trend Detection: It uses a blend of HMA and Kahlman filters to detect trend reversals, providing more accurate and timely signals.
Volatility Adaptability: Designed with volatile assets like Bitcoin in mind, this indicator adapts to rapid price movements, offering smoother trend detection during high volatility.
Easy Visualization: Buy (B) and Sell (S) signals are clearly marked with labels, helping traders spot trend shifts quickly and accurately.
Trendlines Module: The indicator plots trendlines based on pivot points, highlighting important support and resistance levels. This helps traders understand the market structure and identify potential breakout or breakdown zones.
Customizable: Adjust the HMA and Kahlman parameters to fit different assets or trading styles, making it flexible for various market conditions.
Usage Tips:
Best Timeframes: The indicator performs exceptionally well on lower timeframes (such as 15-minute to 1-hour charts), making it ideal for scalping and short-term trading strategies.
Ideal for Volatile Assets: This strategy is perfect for highly volatile assets like Bitcoin, but can also be applied to other cryptocurrencies and traditional markets with high price fluctuations.
Signal Confirmation: Use the trend signals (green for uptrend, red for downtrend) along with the buy/sell labels to help you confirm potential entries and exits. It's also recommended to combine the signals with other technical tools like volume analysis or RSI for enhanced confirmation.
Trendline Analysis: The plotted trendlines provide additional visual context to identify key market zones, supporting your trading decisions with a clear view of ongoing trends and possible reversal areas.
Risk Management: As with any strategy, always consider proper risk management techniques, such as stop-loss and take-profit levels, to protect against unforeseen market moves.
BTC Price-Volume Efficiency Z-Score (PVER-Z)Overview:
This PVER-Z Score measures Bitcoin’s price movement efficiency relative to trading volume, normalized using a Z-Score over a long-term 200-day period.
It highlights statistically rare inefficiencies, helping investors spot extreme accumulation and distribution zones for systematic SDCA strategies.
Concept:
- Measures how efficiently price has moved relative to the volume that supported it over a long historical window (Default 200 days) but can be adjustable.
- It compares cumulative price changes vs cumulative volume flow.
- Then normalizes those inefficiencies using Z-Score statistics.
How It Works:
1. Calculates the absolute daily price change divided by volume (price-volume efficiency ratio).
2. Applies EMA smoothing to remove noisy fluctuations.
3. Normalizes the result into a Z-Score to detect statistically significant outliers.
4. Plots dynamic heatmap colors as the efficiency score moves through different deviation zones.
5. Background fills appear when the Z-Score moves beyond ±2 to ±3 SD, signaling rare macro opportunities.
Why is Bitcoin price rising while PVER-Z is falling toward green zone?
1. PVER-Z is not just "price" — it's price change relative to volume. PVER-Z measures how efficient the price movement is relative to volume. It's not "price going up" or "price going down" directly. It's how unusual or inefficient the price versus volume relationship is, compared to its historical average.
2. A rising Bitcoin price + weak efficiency = PVER-Z falls.
If Bitcoin rises but volume is super strong (normal buying volume), no problem, the PVER-Z stays normal. If Bitcoin rises but with very weak volume support, PVER-Z falls.
***Usage Notes***:
- Best used on the daily timeframe or higher.
- When the Z-Score enters the green zone (-2 to -3 SD), it signals a historically rare accumulation zone — favoring long-term buying for SDCA.
- When the Z-Score enters the red zone (+2 to +3 SD), it signals overextended distribution — caution recommended.
- Designed strictly for mean-reversion analysis, no trend-following signals.
- The red zone on a proper Z chart would be -2SD to -3SD and +2SD to +3SD for the green zone. At the time of publishing I do not know how to adjust the values on the indicator itself. The red zone at -2SD is actually +2 Standard Deviations on a Z Score SD Chart. (overbought zone).
- Your green zone at +2SD is actually -2SD Standard Deviations (oversold zone).
- Built manually with no reliance on built-in indicators
- Designed for Bitcoin on the 1D, 3D, or Weekly timeframes. NOT for intraday trading.
- DO NOT SOELY RELY ON THIS INDICATOR FOR YOUR LONG TERM VALUATION. I AM NOT RESPONSIBLE FOR YOUR FINANICAL ASSETS.
M2 Liqudity WaveGlobal Liquidity Wave Indicator (M2-Based)
The Global Liquidity Wave Indicator is designed to track and visualize the impact of global M2 liquidity on risk assets—especially those highly correlated to monetary expansion, like Bitcoin, MSTR, and other macro-sensitive equities.
Key features include:
Leading Signal: Historically leads Bitcoin price action by approximately 70 days, offering traders and analysts a forward-looking edge.
Wave-Based Projection: Visualizes a "probability cloud"—a smoothed band representing the most likely trajectory for Bitcoin based on changes in global liquidity.
Min/Max Offset Controls: Adjustable offsets let you define the range of lookahead windows to shape the wave and better capture liquidity-driven inflection points.
Explicit Offset Visualization: Option to manually specify an exact offset to fine-tune the overlay, ideal for testing hypotheses or aligning with macro narratives.
Macro Alignment: Particularly effective for assets with high sensitivity to global monetary policy and liquidity cycles.
This tool is not just a chart overlay—it's a lens into the liquidity engine behind the market, helping anticipate directional bias in advance of price moves.
How to use?
- Enable the indicator for BTCUSD.
- Set Offset Range Start and End to 70 and 115 days
- Set Specific Offset to 78 days (this can change so you'll need to play around)
FAQ
Why a global liquidity wave?
The global liquidity wave accounts for variability in how much global liquidity affects an underlying asset. Think of the Global Liquidity Wave as an area that tracks the most probable path of Bitcoin, MSTR, etc. based on the total global liquidity.
Why the offset?
Global liquidity takes time to make its way into assets such as #Bitcoin, Strategy, etc. and there can be many reasons for that. It's never a specific number of days of offset, which is why a global liquidity wave is helpful in tracking probable paths for highly correlated risk assets.
Reversal Strength Meter – Adib NooraniThe Reversal Strength Meter is an oscillator designed to identify potential reversal zones based on supply and demand dynamics. It uses smoothed stochastic logic to reduce noise and highlight areas where momentum may be weakening, signaling possible market turning points.
🔹 Smooth, noise-reduced stochastic oscillator
🔹 Custom zones to highlight potential supply and demand imbalances
🔹 Non-repainting, compatible across all timeframes and assets
🔹 Visual-only tool — intended to support discretionary trading decisions
This oscillator assists scalpers and intraday traders in tracking subtle shifts in momentum, helping them identify when a market may be preparing to reverse — always keeping in mind that trading is based on probabilities, not certainties.
📘 How to Use the Indicator Efficiently
For Reversal Trading:
Buy Setup
– When the blue line dips below the 20 level, wait for it to re-enter above 20.
– Look for reversal candlestick patterns (e.g., bullish engulfing, hammer, or morning star).
– Enter above the pattern’s high, with a stop loss below its low.
Sell Setup
– When the blue line rises above the 80 level, wait for it to re-enter below 80.
– Look for bearish candlestick patterns (e.g., bearish engulfing, inverted hammer, or evening star).
– Enter below the pattern’s low, with a stop loss above its high.
🛡 Risk Management Guidelines
Risk only 0.5% of your capital per trade
Book 50% profits at a 1:1 risk-reward ratio
Trail the remaining 50% using price action or other supporting indicators
Reversal Scalping Ribbon - Adib NooraniThe Reversal Scalping Ribbon is a trend-following overlay tool designed to visually identify potential reversal zones based on price extremes and dynamic volatility bands. It calculates adaptive upper and lower bands using price action and custom ATR logic, helping traders quickly assess market direction and possible turning points
🔹 Volatility-adjusted bands based on price highs/lows
🔹 Color-coded ribbons to indicate trend bias and potential reversal shifts
🔹 No repainting, works on all timeframes and assets
🔹 Visual-only display, no trade signals — supports discretion-based entries
This ribbon is designed for scalpers and intraday traders to spot reversal setups with clarity. It enhances your trading by showing real-time market bias without unnecessary distractions. By focusing on probabilities, it helps to improve decision-making in fast-paced environments
How to use the indicator efficiently
For Reversal Trading:
Buy: When price closes below the green ribbon with a red candle, then re-enters with a green candle. Enter above the high of the green candle with a stop loss below the lowest low of the recent green/red candles
Sell: When price closes above the red ribbon with a green candle, then re-enters with a red candle. Enter below the low of the red candle with a stop loss above the highest high of the recent red/green candles
Risk Management:
Limit risk to 0.5% of your capital per trade
Take 50% profit at a 1:1 risk-reward ratio
For the remaining 50%, trail using the lower edge of the green band for buys and the upper edge of the red band for sells
Relative Crypto Dominance Polar Chart [LuxAlgo]The Relative Crypto Dominance Polar Chart tool allows traders to compare the relative dominance of up to ten different tickers in the form of a polar area chart, we define relative dominance as a combination between traded dollar volume and volatility, making it very easy to compare them at a glance.
🔶 USAGE
The use is quite simple, traders just have to load the indicator on the chart, and the graph showing the relative dominance will appear.
The 10 tickers loaded by default are the major cryptocurrencies by market cap, but traders can select any ticker in the settings panel.
Each area represents dominance as volatility (radius) by dollar volume (arc length); a larger area means greater dominance on that ticker.
🔹 Choosing Period
The tool supports up to five different periods
Hourly
Daily
Weekly
Monthly
Yearly
By default, the tool period is set on auto mode, which means that the tool will choose the period depending on the chart timeframe
timeframes up to 2m: Hourly
timeframes up to 15m: Daily
timeframes up to 1H: Weekly
timeframes up to 4H: Monthly
larger timeframes: Yearly
🔹 Sorting & Sizing
Traders can sort the graph areas by volatility (radius of each area) in ascending or descending order; by default, the tickers are sorted as they are in the settings panel.
The tool also allows you to adjust the width of the chart on a percentage basis, i.e., at 100% size, all the available width is used; if the graph is too wide, just decrease the graph size parameter in the settings panel.
🔹 Set your own style
The tool allows great customization from the settings panel, traders can enable/disable most of the components, and add a very nice touch with curved lines enabled for displaying the areas with a petal-like effect.
🔶 SETTINGS
Period: Select up to 5 different time periods from Hourly, Daily, Weekly, Monthly and Yearly. Enable/disable Auto mode.
Tickers: Enable/disable and select tickers and colors
🔹 Style
Graph Order: Select sort order
Graph Size: Select percentage of width used
Labels Size: Select size for ticker labels
Show Percent: Show dominance in % under each ticker
Curved Lines: Enable/disable petal-like effect for each area
Show Title: Enable/disable graph title
Show Mean: Enable/disable volatility average and select color
Bitcoin Polynomial Regression ModelThis is the main version of the script. Click here for the Oscillator part of the script.
💡Why this model was created:
One of the key issues with most existing models, including our own Bitcoin Log Growth Curve Model , is that they often fail to realistically account for diminishing returns. As a result, they may present overly optimistic bull cycle targets (hence, we introduced alternative settings in our previous Bitcoin Log Growth Curve Model).
This new model however, has been built from the ground up with a primary focus on incorporating the principle of diminishing returns. It directly responds to this concept, which has been briefly explored here .
📉The theory of diminishing returns:
This theory suggests that as each four-year market cycle unfolds, volatility gradually decreases, leading to more tempered price movements. It also implies that the price increase from one cycle peak to the next will decrease over time as the asset matures. The same pattern applies to cycle lows and the relationship between tops and bottoms. In essence, these price movements are interconnected and should generally follow a consistent pattern. We believe this model provides a more realistic outlook on bull and bear market cycles.
To better understand this theory, the relationships between cycle tops and bottoms are outlined below:https://www.tradingview.com/x/7Hldzsf2/
🔧Creation of the model:
For those interested in how this model was created, the process is explained here. Otherwise, feel free to skip this section.
This model is based on two separate cubic polynomial regression lines. One for the top price trend and another for the bottom. Both follow the general cubic polynomial function:
ax^3 +bx^2 + cx + d.
In this equation, x represents the weekly bar index minus an offset, while a, b, c, and d are determined through polynomial regression analysis. The input (x, y) values used for the polynomial regression analysis are as follows:
Top regression line (x, y) values:
113, 18.6
240, 1004
451, 19128
655, 65502
Bottom regression line (x, y) values:
103, 2.5
267, 211
471, 3193
676, 16255
The values above correspond to historical Bitcoin cycle tops and bottoms, where x is the weekly bar index and y is the weekly closing price of Bitcoin. The best fit is determined using metrics such as R-squared values, residual error analysis, and visual inspection. While the exact details of this evaluation are beyond the scope of this post, the following optimal parameters were found:
Top regression line parameter values:
a: 0.000202798
b: 0.0872922
c: -30.88805
d: 1827.14113
Bottom regression line parameter values:
a: 0.000138314
b: -0.0768236
c: 13.90555
d: -765.8892
📊Polynomial Regression Oscillator:
This publication also includes the oscillator version of the this model which is displayed at the bottom of the screen. The oscillator applies a logarithmic transformation to the price and the regression lines using the formula log10(x) .
The log-transformed price is then normalized using min-max normalization relative to the log-transformed top and bottom regression line with the formula:
normalized price = log(close) - log(bottom regression line) / log(top regression line) - log(bottom regression line)
This transformation results in a price value between 0 and 1 between both the regression lines. The Oscillator version can be found here.
🔍Interpretation of the Model:
In general, the red area represents a caution zone, as historically, the price has often been near its cycle market top within this range. On the other hand, the green area is considered an area of opportunity, as historically, it has corresponded to the market bottom.
The top regression line serves as a signal for the absolute market cycle peak, while the bottom regression line indicates the absolute market cycle bottom.
Additionally, this model provides a predicted range for Bitcoin's future price movements, which can be used to make extrapolated predictions. We will explore this further below.
🔮Future Predictions:
Finally, let's discuss what this model actually predicts for the potential upcoming market cycle top and the corresponding market cycle bottom. In our previous post here , a cycle interval analysis was performed to predict a likely time window for the next cycle top and bottom:
In the image, it is predicted that the next top-to-top cycle interval will be 208 weeks, which translates to November 3rd, 2025. It is also predicted that the bottom-to-top cycle interval will be 152 weeks, which corresponds to October 13th, 2025. On the macro level, these two dates align quite well. For our prediction, we take the average of these two dates: October 24th 2025. This will be our target date for the bull cycle top.
Now, let's do the same for the upcoming cycle bottom. The bottom-to-bottom cycle interval is predicted to be 205 weeks, which translates to October 19th, 2026, and the top-to-bottom cycle interval is predicted to be 259 weeks, which corresponds to October 26th, 2026. We then take the average of these two dates, predicting a bear cycle bottom date target of October 19th, 2026.
Now that we have our predicted top and bottom cycle date targets, we can simply reference these two dates to our model, giving us the Bitcoin top price prediction in the range of 152,000 in Q4 2025 and a subsequent bottom price prediction in the range of 46,500 in Q4 2026.
For those interested in understanding what this specifically means for the predicted diminishing return top and bottom cycle values, the image below displays these predicted values. The new values are highlighted in yellow:
And of course, keep in mind that these targets are just rough estimates. While we've done our best to estimate these targets through a data-driven approach, markets will always remain unpredictable in nature. What are your targets? Feel free to share them in the comment section below.
Bitcoin Polynomial Regression OscillatorThis is the oscillator version of the script. Click here for the other part of the script.
💡Why this model was created:
One of the key issues with most existing models, including our own Bitcoin Log Growth Curve Model , is that they often fail to realistically account for diminishing returns. As a result, they may present overly optimistic bull cycle targets (hence, we introduced alternative settings in our previous Bitcoin Log Growth Curve Model).
This new model however, has been built from the ground up with a primary focus on incorporating the principle of diminishing returns. It directly responds to this concept, which has been briefly explored here .
📉The theory of diminishing returns:
This theory suggests that as each four-year market cycle unfolds, volatility gradually decreases, leading to more tempered price movements. It also implies that the price increase from one cycle peak to the next will decrease over time as the asset matures. The same pattern applies to cycle lows and the relationship between tops and bottoms. In essence, these price movements are interconnected and should generally follow a consistent pattern. We believe this model provides a more realistic outlook on bull and bear market cycles.
To better understand this theory, the relationships between cycle tops and bottoms are outlined below:https://www.tradingview.com/x/7Hldzsf2/
🔧Creation of the model:
For those interested in how this model was created, the process is explained here. Otherwise, feel free to skip this section.
This model is based on two separate cubic polynomial regression lines. One for the top price trend and another for the bottom. Both follow the general cubic polynomial function:
ax^3 +bx^2 + cx + d.
In this equation, x represents the weekly bar index minus an offset, while a, b, c, and d are determined through polynomial regression analysis. The input (x, y) values used for the polynomial regression analysis are as follows:
Top regression line (x, y) values:
113, 18.6
240, 1004
451, 19128
655, 65502
Bottom regression line (x, y) values:
103, 2.5
267, 211
471, 3193
676, 16255
The values above correspond to historical Bitcoin cycle tops and bottoms, where x is the weekly bar index and y is the weekly closing price of Bitcoin. The best fit is determined using metrics such as R-squared values, residual error analysis, and visual inspection. While the exact details of this evaluation are beyond the scope of this post, the following optimal parameters were found:
Top regression line parameter values:
a: 0.000202798
b: 0.0872922
c: -30.88805
d: 1827.14113
Bottom regression line parameter values:
a: 0.000138314
b: -0.0768236
c: 13.90555
d: -765.8892
📊Polynomial Regression Oscillator:
This publication also includes the oscillator version of the this model which is displayed at the bottom of the screen. The oscillator applies a logarithmic transformation to the price and the regression lines using the formula log10(x) .
The log-transformed price is then normalized using min-max normalization relative to the log-transformed top and bottom regression line with the formula:
normalized price = log(close) - log(bottom regression line) / log(top regression line) - log(bottom regression line)
This transformation results in a price value between 0 and 1 between both the regression lines.
🔍Interpretation of the Model:
In general, the red area represents a caution zone, as historically, the price has often been near its cycle market top within this range. On the other hand, the green area is considered an area of opportunity, as historically, it has corresponded to the market bottom.
The top regression line serves as a signal for the absolute market cycle peak, while the bottom regression line indicates the absolute market cycle bottom.
Additionally, this model provides a predicted range for Bitcoin's future price movements, which can be used to make extrapolated predictions. We will explore this further below.
🔮Future Predictions:
Finally, let's discuss what this model actually predicts for the potential upcoming market cycle top and the corresponding market cycle bottom. In our previous post here , a cycle interval analysis was performed to predict a likely time window for the next cycle top and bottom:
In the image, it is predicted that the next top-to-top cycle interval will be 208 weeks, which translates to November 3rd, 2025. It is also predicted that the bottom-to-top cycle interval will be 152 weeks, which corresponds to October 13th, 2025. On the macro level, these two dates align quite well. For our prediction, we take the average of these two dates: October 24th 2025. This will be our target date for the bull cycle top.
Now, let's do the same for the upcoming cycle bottom. The bottom-to-bottom cycle interval is predicted to be 205 weeks, which translates to October 19th, 2026, and the top-to-bottom cycle interval is predicted to be 259 weeks, which corresponds to October 26th, 2026. We then take the average of these two dates, predicting a bear cycle bottom date target of October 19th, 2026.
Now that we have our predicted top and bottom cycle date targets, we can simply reference these two dates to our model, giving us the Bitcoin top price prediction in the range of 152,000 in Q4 2025 and a subsequent bottom price prediction in the range of 46,500 in Q4 2026.
For those interested in understanding what this specifically means for the predicted diminishing return top and bottom cycle values, the image below displays these predicted values. The new values are highlighted in yellow:
And of course, keep in mind that these targets are just rough estimates. While we've done our best to estimate these targets through a data-driven approach, markets will always remain unpredictable in nature. What are your targets? Feel free to share them in the comment section below.
M2SL/DXY RatioThis is the ratio of M2 money supply (M2SL) to the U.S. dollar index (DXY), taking into account the impact of U.S. dollar strength and weakness on liquidity.
M2SL/DXY better represents the current impact of the United States on cryptocurrency prices.
Global M2 Money Supply (USD) GrowthThe Global M2 Growth indicator evaluates the total liquid money supply, including cash, checking deposits, and assets that can be easily converted to cash. It reflects changes in global liquidity by tracking year-on-year (YoY) changes in the Global M2 money supply rather than its absolute value. This approach highlights the velocity of liquidity expansion or contraction, offering a clearer understanding of its correlation with asset performance, such as Bitcoin.
How It Works
When the Global M2 money supply expands, it reflects an increase in available liquidity. This often leads to an influx of capital into higher-yielding and riskier assets like Bitcoin, equities, and commodities. Conversely, when M2 contracts, liquidity tightens, leading to declines in the values of these assets.
An essential insight is that Bitcoin's price is not immediately affected by changes in M2. Research shows a lag of approximately 56-60 days (around two months) between liquidity changes and Bitcoin's price movements. Shifting the liquidity data forward by this period improves the correlation between Global M2 and Bitcoin performance.
How to Use
Track Global M2 YoY Change: Focus on liquidity's yearly change to identify trends. Rapid increases in liquidity often signify favorable conditions for Bitcoin and other risk assets to rise, while contractions often predict price declines or consolidation phases.
Account for the Lag Effect: Incorporate the two-month lag into your analysis to predict Bitcoin's potential moves more accurately. For instance, a recent resurgence in liquidity growth could signal a Bitcoin rally within the next two months.
Use as a Macro Indicator: Monitor liquidity trends alongside other economic indicators and asset performance metrics to build a more comprehensive investment framework.
By tracking these dynamics, traders and investors can better anticipate Bitcoin's trajectory and make informed decisions.
Economic Crises by @zeusbottradingEconomic Crises Indicator by @zeusbottrading
Description and Use Case
Overview
The Economic Crises Highlight Indicator is designed to visually mark major economic crises on a TradingView chart by shading these periods in red. It provides a historical context for financial analysis by indicating when major recessions occurred, helping traders and analysts assess the performance of assets before, during, and after these crises.
What This Indicator Shows
This indicator highlights the following major economic crises (from 1953 to 2020), which significantly impacted global markets:
• 1953 Korean War Recession
• 1957 Monetary Tightening Recession
• 1960 Investment Decline Recession
• 1969 Employment Crisis
• 1973 Oil Crisis
• 1980 Inflation Crisis
• 1981 Fed Monetary Policy Recession
• 1990 Oil Crisis and Gulf War Recession
• 2001 Dot-Com Bubble Crash
• 2008 Global Financial Crisis (Great Recession)
• 2020 COVID-19 Recession
Each of these periods is shaded in red with 80% transparency, allowing you to clearly see the impact of economic downturns on various financial assets.
How This Indicator is Useful
This indicator is particularly valuable for:
✅ Comparative Performance Analysis – It allows traders and investors to compare how different assets (e.g., Gold, Silver, S&P 500, Bitcoin) performed before, during, and after major economic crises.
✅ Identifying Market Trends – Helps recognize recurring patterns in asset price movements during times of financial distress.
✅ Risk Management & Strategy Development – Understanding how markets reacted in the past can assist in making better-informed investment decisions for future downturns.
✅ Gold, Silver & Bitcoin as Safe Havens – Comparing precious metals and cryptocurrencies against traditional stocks (e.g., SPY) to analyze their performance as hedges during economic turmoil.
How to Use It in Your Analysis
By overlaying this indicator on your Gold, Silver, SPY, and Bitcoin chart (for example), you can quickly spot historical market reactions and use that insight to predict possible behaviors in future downturns.
⸻
How to Apply This in TradingView?
1. Click on Use on chart under the image.
2. Overlay it with Gold ( OANDA:XAUUSD ), Silver ( OANDA:XAGUSD ), SPY ( AMEX:SPY ), and Bitcoin ( COINBASE:BTCUSD ) for comparative analysis.
⸻
Conclusion
This indicator serves as a powerful historical reference for traders analyzing asset performance during economic downturns. By studying past crises, you can develop a data-driven investment strategy and improve your market insights. 🚀📈
Let me know if you need any modifications or enhancements!
Ragi's 24h volumeThis script is a TradingView Pine Script indicator that displays the 24-hour trading volume for a given asset. It provides both the native volume of the asset and, if the asset is not already listed on Binance, also displays the 24-hour volume from Binance (if applicable). Here's a breakdown of the key components:
Volume Calculation:
It sums the volume data over different time frames: 1-minute, 5-minute (for daily charts), or 60-minute intervals.
The volume is calculated based on the asset's volume type (either "quote" volume or a calculated value of close * volume).
For crypto assets, if the volume data is unavailable, it raises an error.
Binance Volume:
If the asset is not from Binance, the script fetches 24-hour volume data from Binance for that symbol, ensuring it is using the correct currency rate.
Display:
The indicator displays a table with the 24-hour volume in the chosen position on the chart (top, middle, or bottom).
The table displays the current exchange's volume, and if applicable, the Binance volume.
The volume is color-coded based on predefined thresholds:
Attention: Displays a warning color for volumes exceeding the attention level.
Warning: Shows an alert color for volumes above the warning threshold.
Normal: Displays in standard color when the volume is lower than the warning level.
The text and background color are customizable, and users can adjust the text size and position of the table.
User Inputs:
The script allows customization of table text size, position, background color, and volume thresholds for attention and warning.
In summary, this indicator is designed to track and display 24-hour volume on a chart, with additional volume information from Binance if necessary, and provides visual cues based on volume levels to help traders quickly assess trading activity.
Crypto Fear & Greed Score [Underblock]Crypto Fear & Greed Score - Methodology & Functioning
Introduction
The Crypto Fear & Greed Score is a comprehensive indicator designed to assess market sentiment by detecting extreme conditions of panic (fear) and euphoria (greed). By combining multiple technical factors, it helps traders identify potential buying and selling opportunities based on the emotional state of the market.
This indicator is highly customizable, allowing users to adjust weight parameters for RSI, volatility, Bitcoin dominance, and trading volume, making it adaptable to different market conditions.
Key Components
The indicator consists of two primary sub-scores:
Fear Score (Panic) - Measures the intensity of fear in the market.
Greed Score (Euphoria) - Measures the level of overconfidence and excessive optimism.
The difference between these two values results in the Net Score, which indicates the dominant market sentiment at any given time.
1. Relative Strength Index (RSI)
The indicator utilizes multiple RSI timeframes to measure momentum and overbought/oversold conditions:
RSI 1D (Daily) - Captures medium-term sentiment shifts.
RSI 4H (4-hour) - Identifies short-term market movements.
RSI 1W (Weekly) - Helps detect long-term overbought/oversold conditions.
2. Volatility Analysis
High volatility is often associated with fear and panic-driven selling.
Low volatility in bullish markets may indicate complacency and overconfidence.
3. Bitcoin Dominance (BTC.D)
Bitcoin dominance provides insights into capital flow between Bitcoin and altcoins:
Rising BTC dominance suggests fear as investors move into BTC for safety.
Declining BTC dominance indicates increased risk appetite and potential market euphoria.
4. Buying and Selling Volume
The indicator analyzes both buying and selling volume, ensuring a clearer confirmation of market sentiment.
High buying volume in uptrends reinforces bullish momentum.
Spikes in selling volume indicate panic and possible market bottoms.
Calculation Methodology
The indicator allows users to adjust weight parameters for each component, making it adaptable to different trading strategies. The formulas are structured as follows:
Fear Score (Panic Calculation)
Fear Score = (1 - RSI_1D) * W_RSI1D + (1 - RSI_4H) * W_RSI4H + (1 - Dominance) * W_Dominance + Volatility * W_Volatility + Sell Volume * W_SellVolume
Greed Score (Euphoria Calculation)
Greed Score = RSI_1D * W_RSI1D + RSI_4H * W_RSI4H + Dominance * W_Dominance + (1 - Volatility) * W_Volatility + Buy Volume * W_BuyVolume
Net Fear & Greed Score
Net Score = (Greed Score - Fear Score) * 100
Interpretation:
Above 70: Extreme greed -> possible overbought conditions.
Below -70: Extreme fear -> potential buying opportunity.
Near 0: Neutral market sentiment.
Trend Reversal Detection
The indicator includes two moving averages for enhanced trend detection:
Short-term SMA (50-periods) - Reacts quicklier to changes in sentiment.
Long-term SMA (200-periods) - Captures broader trend reversals.
How Crossovers Work:
Short SMA crossing above Long SMA -> Potential bullish reversal.
Short SMA crossing below Long SMA -> Possible bearish trend shift.
Alerts for SMA crossovers help traders act on momentum shifts in real-time.
Customization and Visualization
The Net Score dynamically changes color: green for greed, red for fear.
Users can adjust weightings directly from settings, avoiding manual script modifications.
Reference levels at 70 and -70 provide clarity on extreme market conditions.
Conclusion
The Crypto Fear & Greed Score provides a powerful and objective measure of market sentiment, helping traders navigate extreme conditions effectively.
🟢 If the Net Score is below -70, panic may present a buying opportunity.
🔴 If the Net Score is above 70, excessive euphoria may indicate a selling opportunity.
⚖️ Neutral values suggest a balanced market sentiment.
By customizing weight parameters and utilizing trend reversal alerts, traders can gain a deeper insight into market psychology and make more informed trading decisions. 🚀
ADX for BTC [PineIndicators]The ADX Strategy for BTC is a trend-following system that uses the Average Directional Index (ADX) to determine market strength and momentum shifts. Designed for Bitcoin trading, this strategy applies a customizable ADX threshold to confirm trend signals and optionally filters entries using a Simple Moving Average (SMA). The system features automated entry and exit conditions, dynamic trade visualization, and built-in trade tracking for historical performance analysis.
⚙️ Core Strategy Components
1️⃣ Average Directional Index (ADX) Calculation
The ADX indicator measures trend strength without indicating direction. It is derived from the Positive Directional Movement (+DI) and Negative Directional Movement (-DI):
+DI (Positive Directional Index): Measures upward price movement.
-DI (Negative Directional Index): Measures downward price movement.
ADX Value: Higher values indicate stronger trends, regardless of direction.
This strategy uses a default ADX length of 14 to smooth out short-term fluctuations while detecting sustainable trends.
2️⃣ SMA Filter (Optional Trend Confirmation)
The strategy includes a 200-period SMA filter to validate trend direction before entering trades. If enabled:
✅ Long Entry is only allowed when price is above a long-term SMA multiplier (5x the standard SMA length).
✅ If disabled, the strategy only considers the ADX crossover threshold for trade entries.
This filter helps reduce entries in sideways or weak-trend conditions, improving signal reliability.
📌 Trade Logic & Conditions
🔹 Long Entry Conditions
A buy signal is triggered when:
✅ ADX crosses above the threshold (default = 14), indicating a strengthening trend.
✅ (If SMA filter is enabled) Price is above the long-term SMA multiplier.
🔻 Exit Conditions
A position is closed when:
✅ ADX crosses below the stop threshold (default = 45), signaling trend weakening.
By adjusting the entry and exit ADX levels, traders can fine-tune sensitivity to trend changes.
📏 Trade Visualization & Tracking
Trade Markers
"Buy" label (▲) appears when a long position is opened.
"Close" label (▼) appears when a position is exited.
Trade History Boxes
Green if a trade is profitable.
Red if a trade closes at a loss.
Trend Tracking Lines
Horizontal lines mark entry and exit prices.
A filled trade box visually represents trade duration and profitability.
These elements provide clear visual insights into trade execution and performance.
⚡ How to Use This Strategy
1️⃣ Apply the script to a BTC chart in TradingView.
2️⃣ Adjust ADX entry/exit levels based on trend sensitivity.
3️⃣ Enable or disable the SMA filter for trend confirmation.
4️⃣ Backtest performance to analyze historical trade execution.
5️⃣ Monitor trade markers and history boxes for real-time trend insights.
This strategy is designed for trend traders looking to capture high-momentum market conditions while filtering out weak trends.
Cash And Carry Arbitrage BTC Compare Month 6 by SeoNo1Detailed Explanation of the BTC Cash and Carry Arbitrage Script
Script Title: BTC Cash And Carry Arbitrage Month 6 by SeoNo1
Short Title: BTC C&C ABT Month 6
Version: Pine Script v5
Overlay: True (The indicators are plotted directly on the price chart)
Purpose of the Script
This script is designed to help traders analyze and track arbitrage opportunities between the spot market and futures market for Bitcoin (BTC). Specifically, it calculates the spread and Annual Percentage Yield (APY) from a cash-and-carry arbitrage strategy until a specific expiry date (in this case, June 27, 2025).
The strategy helps identify profitable opportunities when the futures price of BTC is higher than the spot price. Traders can then buy BTC in the spot market and short BTC futures contracts to lock in a risk-free profit.
1. Input Settings
Spot Symbol: The real-time BTC spot price from Binance (BTCUSDT).
Futures Symbol: The BTC futures contract that expires in June 2025 (BTCUSDM2025).
Expiry Date: The expiration date of the futures contract, set to June 27, 2025.
These inputs allow users to adjust the symbols or expiry date according to their trading needs.
2. Price Data Retrieval
Spot Price: Fetches the latest closing price of BTC from the spot market.
Futures Price: Fetches the latest closing price of BTC futures.
Spread: The difference between the futures price and the spot price (futures_price - spot_price).
The spread indicates how much higher (or lower) the futures price is compared to the spot market.
3. Time to Maturity (TTM) and Annual Percentage Yield (APY) Calculation
Current Date: Gets the current timestamp.
Time to Maturity (TTM): The number of days left until the futures contract expires.
APY Calculation:
Formula:
APY = ( Spread / Spot Price ) x ( 365 / TTM Days ) x 100
This represents the annualized return from holding a cash-and-carry arbitrage position if the trader buys BTC at the spot price and sells BTC futures.
4. Display Information Table on the Chart
A table is created on the chart's top-right corner showing the following data:
Metric: Labels such as Spread and APY
Value: Displays the calculated spread and APY
The table automatically updates at the latest bar to display the most recent data.
5. Alert Condition
This sets an alert condition that triggers every time the script runs.
In practice, users can modify this alert to trigger based on specific conditions (e.g., APY exceeds a threshold).
6. Plotting the APY and Spread
APY Plot: Displays the annualized yield as a blue line on the chart.
Spread Plot: Visualizes the futures-spot spread as a red line.
This helps traders quickly identify arbitrage opportunities when the spread or APY reaches desirable levels.
How to Use the Script
Monitor Arbitrage Opportunities:
A positive spread indicates a potential cash-and-carry arbitrage opportunity.
The larger the APY, the more profitable the arbitrage opportunity could be.
Timing Trades:
Execute a buy on the BTC spot market and simultaneously sell BTC futures when the APY is attractive.
Close both positions upon futures contract expiry to realize profits.
Risk Management:
Ensure you have sufficient margin to hold both positions until expiry.
Monitor funding rates and volatility, which could affect returns.
Conclusion
This script is an essential tool for traders looking to exploit price discrepancies between the BTC spot market and futures market through a cash-and-carry arbitrage strategy. It provides real-time data on spreads, annualized returns (APY), and visual alerts, helping traders make informed decisions and maximize their profit potential.
Bitcoin Log Growth Curve OscillatorThis script presents the oscillator version of the Bitcoin Logarithmic Growth Curve 2024 indicator, offering a new perspective on Bitcoin’s long-term price trajectory.
By transforming the original logarithmic growth curve into an oscillator, this version provides a normalized view of price movements within a fixed range, making it easier to identify overbought and oversold conditions.
For a comprehensive explanation of the mathematical derivation, underlying concepts, and overall development of the Bitcoin Logarithmic Growth Curve, we encourage you to explore our primary script, Bitcoin Logarithmic Growth Curve 2024, available here . This foundational script details the regression-based approach used to model Bitcoin’s long-term price evolution.
Normalization Process
The core principle behind this oscillator lies in the normalization of Bitcoin’s price relative to the upper and lower regression boundaries. By applying Min-Max Normalization, we effectively scale the price into a bounded range, facilitating clearer trend analysis. The normalization follows the formula:
normalized price = (upper regresionline − lower regressionline) / (price − lower regressionline)
This transformation ensures that price movements are always mapped within a fixed range, preventing distortions caused by Bitcoin’s exponential long-term growth. Furthermore, this normalization technique has been applied to each of the confidence interval lines, allowing for a structured and systematic approach to analyzing Bitcoin’s historical and projected price behavior.
By representing the logarithmic growth curve in oscillator form, this indicator helps traders and analysts more effectively gauge Bitcoin’s position within its long-term growth trajectory while identifying potential opportunities based on historical price tendencies.
TSI Long/Short for BTC 2HThe TSI Long/Short for BTC 2H strategy is an advanced trend-following system designed specifically for trading Bitcoin (BTC) on a 2-hour timeframe. It leverages the True Strength Index (TSI) to identify momentum shifts and executes both long and short trades in response to dynamic market conditions.
Unlike traditional moving average-based strategies, this script uses a double-smoothed momentum calculation, enhancing signal accuracy and reducing noise. It incorporates automated position sizing, customizable leverage, and real-time performance tracking, ensuring a structured and adaptable trading approach.
🔹 What Makes This Strategy Unique?
Unlike simple crossover strategies or generic trend-following approaches, this system utilizes a customized True Strength Index (TSI) methodology that dynamically adjusts to market conditions.
🔸 True Strength Index (TSI) Filtering – The script refines the TSI by applying double exponential smoothing, filtering out weak signals and capturing high-confidence momentum shifts.
🔸 Adaptive Entry & Exit Logic – Instead of fixed thresholds, it compares the TSI value against a dynamically determined high/low range from the past 100 bars to confirm trade signals.
🔸 Leverage & Risk Optimization – Position sizing is dynamically adjusted based on account equity and leverage settings, ensuring controlled risk exposure.
🔸 Performance Monitoring System – A built-in performance tracking table allows traders to evaluate monthly and yearly results directly on the chart.
📊 Core Strategy Components
1️⃣ Momentum-Based Trade Execution
The strategy generates long and short trade signals based on the following conditions:
✅ Long Entry Condition – A buy signal is triggered when the TSI crosses above its 100-bar highest value (previously set), confirming bullish momentum.
✅ Short Entry Condition – A sell signal is generated when the TSI crosses below its 100-bar lowest value (previously set), indicating bearish pressure.
Each trade execution is fully automated, reducing emotional decision-making and improving trading discipline.
2️⃣ Position Sizing & Leverage Control
Risk management is a key focus of this strategy:
🔹 Dynamic Position Sizing – The script calculates position size based on:
Account Equity – Ensuring trade sizes adjust dynamically with capital fluctuations.
Leverage Multiplier – Allows traders to customize risk exposure via an adjustable leverage setting.
🔹 No Fixed Stop-Loss – The strategy relies on reversals to exit trades, meaning each position is closed when the opposite signal appears.
This design ensures maximum capital efficiency while adapting to market conditions in real time.
3️⃣ Performance Visualization & Tracking
Understanding historical performance is crucial for refining strategies. The script includes:
📌 Real-Time Trade Markers – Buy and sell signals are visually displayed on the chart for easy reference.
📌 Performance Metrics Table – Tracks monthly and yearly returns in percentage form, helping traders assess profitability over time.
📌 Trade History Visualization – Completed trades are displayed with color-coded boxes (green for long trades, red for short trades), visually representing profit/loss dynamics.
📢 Why Use This Strategy?
✔ Advanced Momentum Detection – Uses a double-smoothed TSI for more accurate trend signals.
✔ Fully Automated Trading – Removes emotional bias and enforces discipline.
✔ Customizable Risk Management – Adjust leverage and position sizing to suit your risk profile.
✔ Comprehensive Performance Tracking – Integrated reporting system provides clear insights into past trades.
This strategy is ideal for Bitcoin traders looking for a structured, high-probability system that adapts to both bullish and bearish trends on the 2-hour timeframe.
📌 How to Use: Simply add the script to your 2H BTC chart, configure your leverage settings, and let the system handle trade execution and tracking! 🚀
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.
Ultimate Volatility Scanner by NHBprod - Requested by Client!Hey Everyone!
I created another script to add to my growing library of strategies and indicators that I use for automated crypto and stock trading! This strategy is for BITCOIN but can be used on any stock or crypto. This was requested by a client so I thought I should create it and hopefully build off of it and build variants!
This script gets and compares the 14-day volatility using the ATR percentage for a list of cryptocurrencies and stocks. Cryptocurrencies are preloaded into the script, and the script will show you the TOP 5 coins in terms of volatility, and then compares it to the Bitcoin volatility as a reference. It updates these values once per day using daily timeframe data from TradingView. The coins are then sorted in descending order by their volatility.
If you don't want to use the preloaded set of coins, you have the option of inputting your own coins AND/OR stocks!
Let me know your thoughts.
Ultimate T3 Fibonacci for BTC Scalping. Look at backtest report!Hey Everyone!
I created another script to add to my growing library of strategies and indicators that I use for automated crypto trading! This strategy is for BITCOIN on the 30 minute chart since I designed it to be a scalping strategy. I calculated for trading fees, and use a small amount of capital in the backtest report. But feel free to modify the capital and how much per order to see how it changes the results:)
It is called the "Ultimate T3 Fibonacci Indicator by NHBprod" that computes and displays two T3-based moving averages derived from price data. The t3_function calculates the Tilson T3 indicator by applying a series of exponential moving averages to a combined price metric and then blending these results with specific coefficients derived from an input factor.
The script accepts several user inputs that toggle the use of the T3 filter, select the buy signal method, and set parameters like lengths and volume factors for two variations of the T3 calculation. Two T3 lines, T3 and T32, are computed with different parameters, and their colors change dynamically (green/red for T3 and blue/purple for T32) based on whether the lines are trending upward or downward. Depending on the selected signal method, the script generates buy signals either when T32 crosses over T3 or when the closing price is above T3, and similarly, sell signals are generated on the respective conditions for crossing under or closing below. Finally, the indicator plots the T3 lines on the chart, adds visual buy/sell markers, and sets alert conditions to notify users when the respective trading signals occur.
The user has the ability to tune the parameters using TP/SL, date timerames for analyses, and the actual parameters of the T3 function including the buy/sell signal! Lastly, the user has the option of trading this long, short, or both!
Let me know your thoughts and check out the backtest report!
