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EPS Estimate Profile [SS]This is the EPS Estimate Profile indicator. What it does This indicator Collects all EPS estimates over the course of a lookback and BINS them (sorts them into 10 equal sized categories). Analyzes the returns from earnings releases based on the EPS estimate and the reaction. Calculates the number of bullish vs bearish responses that transpired based on the EPS estimate profile. Calculates the expected Open to High and Open to Low ATR based on the EPS estimate using regression. Toggle to actual EPS release to compare once earnings results are released. How to Use it This indicator can be used to gain insight into whether an earnings release will be received bullishly or bearishly based on the company's EPS estimate. The indicator allows you to see all historic estimates and how the market generally responded to those estimates, as well as a breakdown of how many times estimates in those ranges produced a bullish response or a bearish response to earnings. Examples Let's look at some examples: Here is MSFT. MSFT's last EPS estimate was 3.672. If we consult the table, we can see the average return associated with this estimate range is -4%. Now let's flip to the Daily timeframe and take a look: MSFT ended the day red and continued to sell into the coming days. Let's look at another example: MCDs. Last earnings estimate was 3.327, putting it at the top of the range with an average positive return of 4%. Let's look on the daily: We can see that the earnings had a huge, bullish effect on MCD, despite them coming in below their estimates. If we toggle the indicator to "Actual" EPS release, to see the profile of Actual earnings releases vs response, we get this: Since MCD under-performed, they were still at the top of the profile; but, we can see that the expected returns are more muted now, though still positive. And indeed, the reaction was still positive. Distinguishing % Bullish/Bearish to Avg Returns You will see the profile table displays both the average returns and the percent of bullish/bearish responses. In some cases, you will see that, despite a negative return, the profile reveals more bullish reactions than bearish. What does this mean? It means, despite there being more bullish responses, when bearish responses happen they tend to be more severe and profound, vs bullish responses likely are muted. This can alert you to potential downside risk and help you manage risk accordingly should you elect to trade the earnings release. ATR Prediction You will notice in the bottom right corner of the screen a secondary table that lists the predicted open to high ATR and open to low ATR. This is done using RAW EPS estimates (or raw ACTUAL estimates depending on which you select) and performing a regression to determine the expected ATR. This is only for reference, the analysis should focus around the historic profile of return estimates and actual return values. IMPORTANT NOTE: You MUST be on the Monthly timeframe to use this. Otherwise, you will get an error. If, on certain tickers with a huge history, such as MSFT and XOM or OXY, you get an error, you can simply reduce the lookback length to 80 and this will resolve the issue. Conclusion And that's the indicator! A blend of some light math and fundamentals! A real joy honestly. Hope you enjoy it!
Pine Script®指标
由Steversteves提供
Ben's BTC Macro Fair Value OscillatorBen's BTC Macro Fair Value Oscillator Overview The **BTC Macro Fair Value Oscillator** is a non-crypto fair value framework that uses macro asset relationships (equities, dollar, gold) to estimate Bitcoin's "macro-driven fair value" and identify mean-reversion opportunities. "Is BTC cheap or expensive right now?" on the 4 Hour Timeframe ONLY ### Key Features ✅ **Macro-driven**: Uses QQQ, DXY, XAUUSD instead of on-chain or crypto metrics ✅ **Dynamic weighting**: Assets weighted by rolling correlation strength ✅ **Mean-reversion signals**: Identifies when BTC is cheap/expensive vs macro ✅ **Validated parameters**: Optimized through 5-year backtest (Sharpe 6.7-9.9) ✅ **Visual transparency**: Live correlation panel, fair value bands, statistics ✅ **Non-repainting**: All calculations use confirmed historical data only ### What This Indicator Does - Builds a **synthetic macro composite** from traditional assets - Runs a **rolling regression** to predict BTC price from macro - Calculates **deviation z-score** (how far BTC is from macro fair value) - Generates **entry signals** when BTC is extremely cheap vs macro (dev < -2) - Generates **exit signals** when BTC returns to fair value (dev > 0) ### What This Indicator Is NOT ❌ Not a high-frequency trading system (sparse signals by design) ❌ Not optimized for absolute returns (optimized for Sharpe ratio) ❌ Not suitable as standalone trading system (best as overlay/confirmation) ❌ Not predictive of short-term price movements (mean-reversion timeframe: days to weeks) --- ## Core Concept ### The Premise Bitcoin doesn't trade in a vacuum. It's influenced by: - **Risk appetite** (equities: QQQ, SPX) - **Dollar strength** (DXY - inverse to risk assets) - **Safe haven flows** (Gold: XAUUSD) When macro conditions are "good for BTC" (risk-on, weak dollar, strong equities), BTC should trade higher. When macro conditions turn against it, BTC should trade lower. ### The Innovation Instead of looking at BTC in isolation, this indicator: 1. **Measures how strongly** BTC currently correlates with each macro asset 2. **Builds a weighted composite** of those macro returns (the "D" driver) 3. **Regresses BTC price on D** to estimate "macro fair value" 4. **Tracks the deviation** between actual price and fair value 5. **Signals mean reversion** when deviation becomes extreme ### The Edge The validated edge comes from: - **Extreme deviations predict future returns** (dev < -2 → +1.67% over 12 bars) - **Monotonic relationship** (more negative dev → higher forward returns) - **Works out-of-sample** (test Sharpe +83-87% better than training) - **Low correlation with buy & hold** (provides diversification value) --- ## Methodology ### Step 1: Macro Composite Driver D(t) The indicator builds a weighted composite of macro asset returns: **Process:** 1. Calculate **log returns** for BTC and each macro reference (QQQ, DXY, XAUUSD) 2. Compute **rolling correlation** between BTC and each reference over `corrLen` bars 3. **Weight each asset** by `|correlation|` if above `minCorrAbs` threshold, else 0 4. **Sign-adjust** weights (+1 for positive corr, -1 for negative) to handle inverse relationships 5. **Z-score normalize** each reference's returns over `fvWindow` 6. **Composite D(t)** = weighted sum of sign-adjusted z-scores **Formula:** ``` For each reference i: corr_i = correlation(BTC_returns, ref_i_returns, corrLen) weight_i = |corr_i| if |corr_i| >= minCorrAbs else 0 sign_i = +1 if corr_i >= 0 else -1 z_i = (ref_i_returns - mean) / std contrib_i = sign_i * z_i * weight_i D(t) = sum(contrib_i) / sum(weight_i) ``` **Key Insight:** D(t) represents "how good macro conditions are for BTC right now" in a normalized, correlation-weighted way. --- ### Step 2: Fair Value Regression Uses rolling linear regression to predict BTC price from D(t): **Model:** ``` BTC_price(t) = α + β * D(t) ``` **Calculation (Pine Script approach):** ``` corr_CD = correlation(BTC_price, D, fvWindow) sd_price = stdev(BTC_price, fvWindow) sd_D = stdev(D, fvWindow) cov = corr_CD * sd_price * sd_D var_D = variance(D, fvWindow) β = cov / var_D α = mean(BTC_price) - β * mean(D) fair_value(t) = α + β * D(t) ``` **Result:** A time-varying "macro fair value" line that adapts as correlations change. --- ### Step 3: Deviation Oscillator Measures how far BTC price has deviated from fair value: **Calculation:** ``` residual(t) = BTC_price(t) - fair_value(t) residual_std = stdev(residual, normWindow) deviation(t) = residual(t) / residual_std ``` **Interpretation:** - `dev = 0` → BTC at fair value - `dev = -2` → BTC is 2 standard deviations **cheap** vs macro - `dev = +2` → BTC is 2 standard deviations **rich** vs macro --- ### Step 4: Signal Generation **Long Entry:** `dev` crosses below `-2.0` (BTC extremely cheap vs macro) **Long Exit:** `dev` crosses above `0.0` (BTC returns to fair value) **No shorting** in default config (risk management choice - crypto volatility) --- ## How It Works ### Visual Components #### 1. Price Chart (Main Panel) **Fair Value Line (Orange):** - The estimated "macro-driven fair value" for BTC - Calculated from rolling regression on macro composite **Fair Value Bands:** - **±1σ** (light): 68% confidence zone - **±2σ** (medium): 95% confidence zone - **±3σ** (dark, dots): 99.7% confidence zone **Entry/Exit Markers:** - **Green "LONG" label** below bar: Entry signal (dev < -2) - **Red "EXIT" label** above bar: Exit signal (dev > 0) #### 2. Deviation Oscillator (Separate Pane) **Line plot:** - Shows current deviation z-score - **Green** when dev < -2 (cheap) - **Red** when dev > +2 (rich) - **Gray** when neutral **Histogram:** - Visual representation of deviation magnitude - Green bars = negative deviation (cheap) - Red bars = positive deviation (rich) **Threshold lines:** - **Green dashed at -2.0**: Entry threshold - **Red dashed at 0.0**: Exit threshold - **Gray solid at 0**: Fair value line #### 3. Correlation Panel (Top-Right) Shows live correlation and weighting for each macro asset: | Asset | Corr | Weight | |-------|------|--------| | QQQ | +0.45 | 0.45 | | DXY | -0.32 | 0.32 | | XAUUSD | +0.15 | 0.00 | | Avg \|Corr\| | 0.31 | 0.77 | **Reading:** - **Corr**: Current rolling correlation with BTC (-1 to +1) - **Weight**: How much this asset contributes to fair value (0 = excluded) - **Avg |Corr|**: Average correlation strength (should be > 0.2 for reliable signals) **Colors:** - Green/Red corr = positive/negative correlation - White weight = asset included, Gray = excluded (below minCorrAbs) #### 4. Statistics Label (Bottom-Right) ``` ━━━ BTC Macro FV ━━━ Dev: -2.34 Price: $103,192 FV: $110,500 Status: CHEAP ⬇ β: 103.52 ``` **Fields:** - **Dev**: Current deviation z-score - **Price**: Current BTC close price - **FV**: Current macro fair value estimate - **Status**: CHEAP (< -2), RICH (> +2), or FAIR - **β**: Current regression beta (sensitivity to macro) --- ## Installation & Setup ### TradingView Setup 1. Open TradingView and navigate to any **BTC chart** (BTCUSD, BTCUSDT, etc.) 2. Open **Pine Editor** (bottom panel) 3. Click **"+ New"** → **"Blank indicator"** 4. **Delete** all default code 5. **Copy** the entire Pine Script from `GHPT_optimized.pine` 6. **Paste** into the editor 7. Click **"Save"** and name it "BTC Macro Fair Value Oscillator" 8. Click **"Add to Chart"** ### Recommended Chart Settings **Timeframe:** 4h (validated timeframe) **Chart Type:** Candlestick or Heikin Ashi **Overlay:** Yes (indicator plots on price chart + separate pane) **Alternative Timeframes:** - Daily: Works but slower signals - 1h-2h: May work but not validated - < 1h: Not recommended (too noisy) ### Symbol Requirements **Primary:** BTC/USD or BTC/USDT on any exchange **Macro References:** Automatically fetched - QQQ (Nasdaq 100 ETF) - DXY (US Dollar Index) - XAUUSD (Gold spot) **Data Requirements:** - At least **90 bars** of history (warmup period) - Premium TradingView recommended for full historical data --- ## Reading the Indicator ### Identifying Signals #### Strong Long Signal (High Conviction) - ✅ Deviation < -2.0 (extreme undervaluation) - ✅ Avg |Corr| > 0.3 (strong macro relationships) - ✅ Price touching or below -2σ band - ✅ "LONG" label appears below bar **Interpretation:** BTC is extremely cheap relative to macro conditions. Historical data shows +1.67% average return over next 12 bars (48 hours at 4h timeframe). #### Moderate Long Signal (Lower Conviction) - ⚠️ Deviation between -1.5 and -2.0 - ⚠️ Avg |Corr| between 0.2-0.3 - ⚠️ Price approaching -2σ band **Interpretation:** BTC is cheap but not extreme. Consider as confirmation for other signals. #### Exit Signal - 🔴 Deviation crosses above 0 (returns to fair value) - 🔴 "EXIT" label appears above bar **Interpretation:** Mean reversion complete. Close long positions. #### Strong Short/Avoid Signal - 🔴 Deviation > +2.0 (extreme overvaluation) - 🔴 Avg |Corr| > 0.3 - 🔴 Price touching or above +2σ band **Interpretation:** BTC is expensive vs macro. Historical data shows -1.79% average return over next 12 bars. Consider exiting longs or reducing exposure. ### Regime Detection **Strong Regime (Reliable Signals):** - Avg |Corr| > 0.3 - Multiple assets weighted > 0 - Fair value line tracking price reasonably well **Weak Regime (Unreliable Signals):** - Avg |Corr| < 0.2 - Most weights = 0 (grayed out) - Fair value line diverging wildly from price - **Action:** Ignore signals until correlations strengthen
Pine Script®指标
由popularGnuofls7提供
Stablecoin to BTC Market Cap RatioThis indicator calculates the ratio of the combined market capitalization of USDT and USDC stablecoins to the market capitalization of BTC. Data is updated daily from TradingView's CRYPTOCAP sources. It is displayed as a line in a separate panel, allowing analysis of stablecoin liquidity dynamics relative to BTC. How to Use Add the indicator to any asset chart in TradingView. It is useful for assessing the potential buying power of stablecoins in the cryptocurrency market. High ratio values may signal accumulation of liquidity in stablecoins, often preceding growth in BTC or altcoins (bullish signal). Low values indicate a decrease in the role of stablecoins, which may be bearish. It is recommended to combine with other indicators, such as RSI or volumes, to confirm trends.
Pine Script®指标
由iCD_creator提供
WM & HS Radar (Block-Free)The W/M + H&S Radar automatically scans for double-top/double-bottom (M and W) and head-and-shoulders style reversal structures across any timeframe. How it works: Detects repeating pivot formations that resemble W (double bottom) or M (double top) structures. Draws neckline levels for each pattern and highlights potential breakout points. Confirms breakout validity when price closes beyond the neckline (optionally requiring a 1.2× volume surge). Generates alerts when a valid W Long or M Short trigger occurs. Best used on: 15m, 1h, or 4h charts to identify medium-term reversal entries. Recommended companion: Orion Daily HL + Volume indicator for higher-timeframe context. Alert Options: “W Long Trigger” → Bullish reversal breakout. “M Short Trigger” → Bearish reversal breakout. Usage Tip: Combine with your support/resistance zones and ATR-based stop sizing from your Money Momentum Tracker to validate A-setups only.
Pine Script®指标
由matthews86rx提供
Trappp's Advanced Multi-Timeframe Trading ToolkitTrappp's Advanced Multi-Timeframe Trading Toolkit This comprehensive trading script by Trappp provides a complete market analysis framework with multiple timeframe support and resistance levels. The indicator features: Key Levels: · Monthly (light blue dashed) and Weekly (gold dashed) levels for long-term context · Previous day high/low (yellow) with range display · Pivot-based support/resistance (pink dashed) · Premarket levels (blue) for pre-market activity Intraday Levels: · 1-minute opening candle (red) · 5-minute (white), 15-minute (green), and 30-minute (purple) session levels · All intraday levels extend right throughout the trading day Technical Features: · EMA 50/200 cross detection with alert labels · Candlestick pattern recognition near key levels · Smart proximity detection using ATR · Automatic daily/weekly/monthly updates Trappp's script is designed for traders who need immediate visual reference of critical price levels across multiple timeframes, helping identify potential breakouts, reversals, and pattern-based setups with clear, color-coded visuals for quick decision-making.
Pine Script®指标
由AntonioUA提供
Ethereum Sleepy Wallets – 6-Month DormancyWhat This Indicator Does It measures how many Ethereum addresses have been completely inactive for at least 6 months (≥ 180 days) — using official Glassnode and CryptoQuant on-chain metrics. This reveals deep conviction among long-term ETH holders Core Concept: Direct 6-Month Dormancy The indicator uses two precise on-chain signals: Total Unique ETH Addresses From GLASSNODE:ETH_ADDRESSES or CRYPTOQUANT:ETH_TOTAL_ADDRESSES Counts every address ever used on Ethereum Addresses Inactive ≥ 180 Days From GLASSNODE:ETH_ADDRESSES_GREATER_THAN_180_DAYS Counts every address that has not sent or received ETH in 6+ months Sleepy ETH = Dormant ≥ 180 Days Sleepy Ratio % = (Sleepy / Total) × 100 This is not an estimate — it’s direct, real dormancy. Why 6-Month Dormancy Matters Short-term activity (7-day) = noise from DeFi, NFTs, trading 180-day inactivity = true HODLing — coins untouched through entire market cycles Historically: Rising dormancy → supply drying up → bullish pressure Falling dormancy → long-term holders selling → bearish warning How It Works (Step-by-Step) Fetches daily data from Glassnode (Pro+) or CryptoQuant (free) Selects real data if available; otherwise uses robust fallback Calculates raw sleepy wallets = addresses inactive ≥ 180 days Smooths the signal with a 21-day simple moving average (SMA) to filter noise Computes Sleepy Ratio % for instant conviction reading Displays live info table with exact values on every bar How to Use It Signal Interpretation Suggested Action Sleepy Ratio > 75% and rising Extreme long-term HODLing Strong accumulation — buy/hold Smooth Sleepy trending up Dormancy growing over 21 days Bullish supply shock forming Sleepy Ratio < 68% and falling Long-term coins re-entering circulation Caution — possible distribution Smooth Sleepy dropping fast HODLers breaking after 6+ months Bearish warning — consider exits Use on Daily (D) or Weekly (W) charts for clean, reliable signals. Pro+ vs Free Mode Mode Data Source Accuracy Pro+ (Glassnode ON) Real 180-day dormancy metric 100% precise Free (Glassnode OFF) CryptoQuant + price-scaled estimate ~80% historical correlation Toggle in settings: Use Glassnode Data What Makes This Indicator Original First open-source script to directly plot Ethereum’s 6-month dormancy using official ADDRESSES_GREATER_THAN_180_DAYS No fake math — uses true inactivity, not active address subtraction Dual-source logic ensures usability on any TradingView plan Dual output: raw sleepy count + 21-day SMA for precision and trend Live info table shows real-time values and data source
Pine Script®指标
由bristleconewealth提供
Macro Valuation Oscillator (MVO)Macro Valuation Oscillator (MVO) is a macro-relative-strength indicator that compares the current valuation of an asset against three key benchmarks: Gold, USD, and Bond. It helps visualize how the asset performs in relative macro terms over time. When the MVO line for Gold (yellow) moves below the neutral zone (0), it reflects relative weakness against gold. When it rises above +80, it indicates relative strength or potential overheating compared to gold. The same concept applies to USD (blue) and Bond (purple) lines. The indicator highlights macro-rotation behavior, showing periods when assets outperform (green) or underperform (red) in relative value. It is mainly intended for daily charts, providing a clear visual framework for assessing long-term macro relationships and timing within broader market cycles.
Pine Script®指标
由Uttaya提供
Sector Analysis [SS]Introducing the most powerful sector analysis tool/indicator available, to date, in Pine! This is a whopper indicator, so be sure to read carefully to ensure you understand its applications and uses! First of all, because this is a whopper, let's go over the key functional points of the indicator. The indicator compares the 11 main sector ETFs against whichever ticker you are looking at. The functions include the following: Ability to pull technicals from the sectors, such as RSI, Stochastic and Z-Score; Ability to look at the correlation of the sector ETF to the current ticker you are looking at. Ability to calculate the R2 value between the ticker you are looking at and each sector. The ability to run a Two Tailed T-Test against the log returns of the Ticker of interest and the Sector (to analyze statistically significant returns between sectors/tickers). The ability to analyze the distribution of returns across all sector ETFs. The ability to pull buying and selling volume across all sector ETFs. The ability to create an integrated moving average using a sector ETF to predict the expected close range of a ticker of interest. These are the highlight functions. Below, I will go more into them, what they mean and how to use them. Pulling Technicals This is pretty straight forward. You can pull technicals, such as RSI, Stochastic and Z-Score from all the sector ETFs and view them in a table. See below for the example: Pulling Correlation In order to see which sector your ticker of interest follows more closely, we need to look first at correlation and then at R2. The correlation will look at the immediate relationship over a specified time. A highly positive value, indicates a strong, symbiotic relationship, which the sector and the ticker follow each other. This would be represented by a correlation of 0.8 or higher. A strong negative correlation, such as -0.8 or lower, indicates that the sector and the ticker are completely opposite. When one goes up, the other goes down and vice versa. You can adjust your correlation assessment length directly in the settings menu: If you want to use a sector ETF to find the expected range for a ticker of interest, it is important to locate the highest, POSITIVE, correlation value. Here are the results for MSFT at a correlation lookback of 200: In this example, we can see the best relationship is with the ETF XLK. Analysis of R2 R2 is an important metric. It essentially measures how much of the variance between 2 tickers are explained by a simple, linear relationship. A high R2 means that a huge degree of variance can be explained between the 2 tickers. A low R2 means that it cannot and that the 2 tickers are likely not integrated or closely related. In general, if you want to use the sector ETF to find the mean and trading range and identify over-valuation/over-extension and under-extension statistically, you need to see both a high correlation and a high R-Squared. These 2 metrics should be analyzed together. Let's take a look at MSFT: Here, despite the correlation implying that XLK was the ticker we should use to analyze, when we look at the R Squared, we see actually, we should be using XLI. XLI has a strong positive relationship with MSFT, albeit a bit less than XLK, but the R2 is solid, > 0.9, indicating the XLI explains much of MSFT's variance. Two Tailed T-Test A two tailed T-test analyzes whether there is a statistically significant difference between 2 different groups, or in our case, tickers. The T-Test is conducted on the log returns of the ticker of interest and the sector. You then can see the P value results, whether it is significant or not. Let's look at MSFT again: Looking at this, we can see there is no statistically significant difference in returns between MSFT and any of the sectors. We can also see the SMA of the log returns for more detailed comparison. If we were to observe a significant finding on the T-Test metrics, this would indicate that one sector either outperforms or underperforms your ticker to a statistically significant degree! If you stumble upon this, you would check the average log returns to compare against the average returns of your ticker of interest, to see whether there is better performance or worse performance from the sector ETF vs. your ticker of interest. Analyzing the Distribution The indicator will also analyze the distribution of returns. This is an interesting option as it can help you ascertain risk. Normally distributed returns imply mean reverting behavviour. Deviations from that imply trending behaviour with higher risk expectancy. If we look at the distribution statistics currently over the last 200 trading days, here are the results: Here, we can see all show signs of trending, as none of the returns are normally distributed. The highest risk sectors are XLK and XLY. Why are they the highest risk? Because the indicator has found a heavy right tailed distribution, indicated sudden and erratic mean reversion/losses are possible. Creating an MA Now for the big bonus of the indicator! The indicator can actually create a regression based range from closely correlated sectors, so you can see, in sectors that are strongly correlated to your ticker, whether your ticker is over-bought, oversold or has mean reverted. Let's look at MSFT using XLI, our previously identified sector with a high correlation and high R2 value: The results are pretty impressive. You can see that MSFT has rode the mean of the sector on the daily timeframe for quite some time. Each time it over extended itself above the sector implied range, it mean reverted. Currently, if you were to trade based on Pairs or statistics, MSFT is no trade as it is currently trading at its sector mean. If you are a visual person, you can have the indicator plot the mean reversion points directly: Green represents a bullish mean reversion and red a bearish mean reversion. Concluding Remarks If you like pair trading, following the link between sectors and tickers or want a more objective way to determine whether a ticker is over-bought or oversold, this indicator can help you. In addition to doing this, the indicator can provide risk insights into different sectors by looking at the distribution, as well as identify under-performing sectors or tickers. It can also shed light on sectors that may be technically over-bought or oversold by looking at Z-Score, stochastics and RSI. Its a whopper and I really hope you find it helpful and useful! Thanks everyone for reading and checking this out! Safe trades!
Pine Script®指标
由Steversteves提供
1
Sesiones Globales 🌍 Londres / Wall Street / Tokio / SydneyA clean visualization of the four main trading sessions — all shown in Argentina time (UTC−3) for easier global market tracking. 🕒 Sessions covered: London 🇬🇧 — 05:00 to 13:30 Wall Street 🇺🇸 — 11:30 to 18:00 Tokyo 🇯🇵 — 21:00 to 03:00 Sydney 🇦🇺 — 20:00 to 02:00 ✨ Features: Soft background colors for each market session (non-intrusive and chart-friendly) “OPEN” and “CLOSE” labels in matching session colors Correct weekend handling — Tokyo and Sydney extend into early Saturday mornings (no false sessions shown) Works on any asset — BTC, SP500, FX, or indices Designed for dark charts and visual clarity 🎯 Why use it: See where global liquidity overlaps, detect volatility zones, and plan your trades around real session activity — especially helpful for BTC and SP500 traders following institutional flow. 💡 Tip: All times are set to Argentina (UTC−3) by default. Adjust manually if you prefer another timezone.
Pine Script®指标
由ktrimboli94提供
Crypto Futures Basis Tracker (Annualized)🧩 What is Basis Arbitrage Basis arbitrage is a market-neutral trading strategy that exploits the price difference between a cryptocurrency’s spot and its futures markets. When futures trade above spot (called contango), traders can buy spot and short futures, locking in a potential yield. When futures trade below spot (backwardation), the reverse applies — short spot and go long futures. The yield earned (or cost paid) by holding this position until expiry is called the basis. Expressing it as an annualized percentage allows comparison across different contract maturities. ⚙️ How the Indicator Works This tool calculates the annualized basis for up to 10 cryptocurrency futures against a chosen spot price. You select one spot symbol (e.g., BITSTAMP:BTCUSD) and up to 10 futures symbols (e.g., DERIBIT:BTCUSD07X2025, DERIBIT:BTCUSD14X2025, etc.). The script automatically computes the days-to-expiry (DTE) and the annualized basis for each future. A table displays for each contract: symbol, expiry date, DTE, last price, and annualized basis (%) — making it easy to compare the forward curve across maturities. ⚠️ Risks and Limitations While basis arbitrage is often considered low-risk, it’s not risk-free: Funding and financing costs can erode returns, especially when borrowing or using leverage. Exchange or counterparty risk — if one leg of the trade fails (e.g., exchange default, margin liquidation), the hedge breaks. Execution and timing risk — the basis can tighten or invert before both legs are opened. Liquidity differences — thin futures may have large bid-ask spreads or slippage. Use this indicator for analysis and monitoring, not as an automated trading signal. Disclaimer: Please remember that past performance may not be indicative of future results. Due to various factors, including changing market conditions, the strategy may no longer perform as well as in historical backtesting. This post and the script don't provide any financial advice.
Pine Script®指标
由QuantNomad提供