Beta -> The New SystemBeta → The New System 📊
Calculate and visualize your asset’s sensitivity to a benchmark over a rolling lookback period.
What is Beta? 🤔
Beta measures how much your asset moves in relation to a chosen benchmark. A Beta of 1 means it moves in perfect sync; above 1 means it’s more volatile (amplified moves), and below 1 means it’s less volatile (dampened moves). By tracking Beta you see if your asset is a risky rocket or a stable ship compared to the market. 🚀⚓️
Indicator Inputs ⚙️
Lookback Period ⏳
Number of bars (e.g. days) over which to compute rolling averages, covariance, and variance.
Benchmark Symbol 🏷️
The ticker of the market or index you want to compare against (e.g. BTCUSD, ETHUSD, an index).
How It Works 🧮
Fetch prices for both your asset and the benchmark at each bar.
Compute returns by calculating the percentage change from bar to bar.
Smooth returns with a simple moving average over the lookback period to get mean asset and benchmark returns.
Calculate covariance between asset and benchmark returns to see how they move together.
Calculate variance of the benchmark returns to measure its own volatility.
Divide covariance by variance (with a check to avoid division by zero)—that ratio is your Beta.
Plot & Interpretation 🎨
Line Color
Always blue for Beta, emphasizing volatility comparison.
Reference Line
A dashed gray line at Beta = 1 marks “market-level” sensitivity.
Reading Beta
β > 1 🟥
Asset tends to exaggerate benchmark moves—higher upside potential but larger downside risk.
β = 1 🟩
Asset moves in lockstep with your benchmark.
β < 1 🟦
Asset smooths out benchmark swings—less risk but also muted returns.
Pro Tips 💡
Combine Alpha + Beta: high Beta with positive Alpha can be great in up-markets but painful in drawdowns.
Monitor Beta shifts: a sudden jump could signal a regime change or new correlation dynamics.
Test different benchmarks: small-cap altcoins may track a broader crypto index differently than they track Bitcoin.
By keeping an eye on Beta in real time, you’ll understand not just how much you’re making, but how much market risk you’re taking on every trade.
Educational
Alpha -> The New SystemAlpha → The New System 📈
Calculate and visualize your asset’s Alpha relative to a chosen benchmark over a rolling lookback period.
What is Alpha? 🤔
Alpha measures the excess return of an asset compared to what would be predicted by its sensitivity (beta) to a benchmark. A positive Alpha means your asset is outperforming the benchmark after accounting for market moves; a negative Alpha means it’s underperforming.
Indicator Inputs ⚙️
Lookback Period ⏳
Number of bars (e.g. days) over which to compute rolling averages, covariance, and variance.
Benchmark Symbol 🏷️
The ticker of the market or index you want to compare against (default: BTCUSD).
How It Works 🧮
Fetch Benchmark Prices
Retrieve the close prices of your chosen benchmark on the same timeframe.
Compute Periodic Returns
Calculate the percent change each bar for both your asset and the benchmark.
Rolling Averages
Smooth those returns over the lookback period to get mean asset return and mean benchmark return.
Covariance & Variance
Covariance between asset and benchmark returns shows how they move together.
Variance of benchmark returns measures its own volatility.
Beta Calculation
Divide covariance by benchmark variance (with a check to avoid divide-by-zero). Beta indicates how sensitive your asset is to benchmark moves.
Alpha Calculation
Subtract (Beta × mean benchmark return) from mean asset return. The result is your asset’s average outperformance (or underperformance) per bar.
Plot & Interpretation 🎨
Line Color
🟢 Green when Alpha > 0 (asset is outperforming)
🔴 Red when Alpha < 0 (asset is underperforming)
Zero Line
A dashed gray line marks Alpha = 0 as the breakeven point.
Pro Tips 💡
A consistently positive Alpha suggests your strategy or selected asset adds value beyond market movements.
A negative Alpha may signal underperformance—time to reconsider allocation or strategy.
Use different benchmarks (e.g., ETH, total market cap, sector indices) to gauge performance in various market contexts.
Combine Alpha with Beta: a high Beta + positive Alpha means strong upside in bull markets, but watch out in downturns.
By tracking Alpha in real time, you’ll know at a glance whether your asset truly shines on its own or is merely riding the broader market wave. 🎯
Support & Resistance ZonesAdvanced Support & Resistance Detection Algorithm
This indicator identifies meaningful price levels by analyzing market structure using a proprietary statistical approach. Unlike traditional methods that rely on simple swing highs/lows or moving averages, this system dynamically detects zones where price has shown consistent interaction, revealing true areas of supply and demand.
Core Methodology
Price Data Aggregation
Collects highs and lows over a configurable lookback period.
Normalizes price data to account for volatility, ensuring levels remain relevant across different market conditions.
Statistical Significance Filtering
Rejection of random noise: Eliminates insignificant price fluctuations using adaptive thresholds.
Volume-weighted analysis (implied): Stronger reactions at certain price levels are given higher priority, even if volume data is unavailable.
Dynamic Level Extraction
Density-based S/R Zones: Instead of fixed swing points, the algorithm identifies zones where price has repeatedly consolidated.
Time decay adjustment: Recent price action has more influence, ensuring levels adapt to evolving market structure.
Strength Quantification
Each level is assigned a confidence score based on:
Touch frequency: How often price revisited the zone.
Reaction intensity: The magnitude of bounces/rejections.
Time relevance: Whether the level remains active or has been broken decisively.
Adaptive Level Merging & Pruning
Proximity-based merging: If two levels are too close (within a volatility-adjusted threshold), they combine into one stronger zone.
Decay mechanism: Old, untested levels fade away if price no longer respects them.
Why This Approach Works Better Than Traditional Methods
✅ No subjective drawing required – Levels are generated mathematically, removing human bias.
✅ Self-adjusting sensitivity – Works equally well on slow and fast-moving markets.
✅ Focuses on statistically meaningful zones – Avoids false signals from random noise.
✅ Non-repainting & real-time – Levels only update when new data confirms their validity.
How Traders Can Use These Levels
Support/Resistance Trading: Fade bounces off strong levels or trade breakouts with confirmation.
Confluence with Other Indicators: Combine with RSI, MACD, or volume profiles for higher-probability entries.
Stop Placement: Place stops just beyond key levels to avoid premature exits.
Technical Notes (For Advanced Users)
The algorithm avoids overfitting by dynamically adjusting zones sensitivity based on market conditions.
Unlike fixed pivot points, these levels adapt to trends, making them useful in both ranging and trending markets.
The strength percentage helps filter out weak levels—only trade those with a high score for better accuracy.
Note: Script takes some time to load.
📈 abusuhil bullish candles)"Abusuhil Bullish Candlestick Patterns" is a professional script that identifies key bullish reversal candlestick patterns and highlights them clearly on the chart.
The script includes the following bullish patterns:
Hammer
Bullish Engulfing
Morning Star
Piercing Line
Three White Soldiers
Three Inside Up
🔎 It also includes optional filtering conditions to improve the quality of signals, including:
Stochastic confirmation
Volume filter
Trend direction filter using the EMA
📢 Each pattern can be enabled or disabled individually, and alerts can be customized for each pattern separately.
This tool is ideal for traders looking to spot bullish price reversals with added confirmation logic.
RKB Rahul Slow Fast CrossoverThis is a custom indicator developed to complement the trading approach taught in our educational program, Bansal ka Bhrahmastra. This tool visually represents a unique price crossover strategy based on two distinct sets of moving averages—classified as "Fast" (Green) and "Slow" (Red). When certain conditions between these moving averages are met, the indicator highlights potential trend signals on the chart.
Core Features:
Dual-group Moving Average Crossovers: Detects dynamic interactions between fast and slow MA groups.
Signal Highlights: Marks potential bullish or bearish moments based on the strategy logic.
Customization: Adjustable parameters for both fast and slow MA settings.
Purpose:
This indicator is designed to assist users who are familiar with the concepts taught in the Bansal ka Bhrahmastra course. It serves as a visual aid to apply the taught strategy consistently, rather than a standalone signal generator.
Institutional Volume Toolkit & Automatic Wick @ MaxMaserati 2.0Institutional Volume Toolkit with Automatic Wick Analyzer @ MaxMaserati 2.0
Overview
This advanced technical indicator combines institutional volume analysis with precise wick detection to reveal hidden market dynamics driving price action. Designed for serious traders who recognize that volume is the fuel behind meaningful price movements, this toolkit identifies key support/resistance zones created by significant institutional activity while simultaneously analyzing candlestick wicks to detect buyer/seller dominance.
Key Features
Volume Defense Levels
Institutional Support/Resistance Detection: Automatically identifies price levels where significant volume occurred during swing points
Dynamic Strength Visualization: Defense level thickness varies based on relative volume importance
Volume % Labels: Clearly shows the strength of each defense level relative to average volume
Adaptive Coloring: Green for buyer defense (support), red for seller defense (resistance)
Wick Analysis System
Smart Wick Detection: Analyzes upper and lower wicks to identify real-time buying and selling pressure
Automatic Dominance Calculation: Determines whether buyers or sellers are dominant at key price points
Multi-Line Analysis: Displays 3-line sets at critical wick areas for precise entry/exit signals
Volume-Enhanced Signals: Combines wick analysis with volume comparison for stronger signals
Trading Applications
Support/Resistance Trading: Identify high-probability bounce or reversal zones created by institutional volume
Break & Retest Strategies: Trade with confidence when price retests broken volume levels
Divergence Detection: Spot divergences between price action and institutional commitment
Swing Trading Setup: Perfect for identifying optimal swing trade entries with favorable risk/reward
Trend Confirmation: Validate trend strength by assessing institutional participation
Setup Guidelines
Volume Defense Levels: Customize volume lookback and threshold to match your timeframe
Wick Analysis: Set filters (wick-to-body ratio, volume percentage, dominance) to eliminate noise
Visual Settings: Adjust colors and label sizes to match your chart preferences
Combine Timeframes: Use on higher timeframes for strategic levels, lower timeframes for entries
This indicator combines sophisticated volume analysis with candlestick psychology, providing a complete toolkit for identifying institutional activity where smart money is actively defending price levels. By revealing the hidden forces driving market movements, it offers a significant edge for traders seeking to align their positions with institutional flow.
SG AlgoThis Indicator is designed to help traders identify high-quality trade opportunities with minimal noise.
Features ✨
Visualizes dynamic market structure with customizable Trend and Control lines.
Highlights key price zones known as Entry Boxes, where potential trade setups may occur.
Displays precise Buy and Sell signals based on internal confluence of trend and price action.
Intuitive color-coded cues and visual markers for quick decision-making.
Built-in alert system for real-time notifications to keep you ahead of market moves.
Suitable for both intraday scalping and longer-term position trading — all without clutter or distractions.
How to Use Buy and Sell Signals 📈📉
This indicator provides clear Buy and Sell signals based on trend behavior and key price zones:
Buy Signal 🟢: When the system indicates a buy, consider entering a long position just above the current price action.
Place your stop loss 🛑 slightly below a major support level (called the Control level) to manage risk.
Sell Signal 🔴: When a sell signal appears, consider entering a short position just below the current price action.
Place your stop loss 🛑 slightly above the Control level to limit losses.
Alternative Entry Method: Breakouts Relative to Trend 🚀📉
You may also choose to trade breakouts:
Buy on Breakout 🚀: Enter long when price breaks above and CLOSES a CANDLE the main trend level, indicating bullish momentum.
Use the Control level below as your stop loss area 🛑.
Sell on Breakdown 📉: Enter short when price breaks below the main trend level, signaling bearish momentum.
Use the Control level above as your stop loss area 🛑.
✅ Entry:
Buy Setup: When a green triangle appears below a candle (Entry Box Confirmed Buy), it signals a potential bullish entry. Consider entering at the close of the signal candle or on a small retracement into the candle body.
Sell Setup: When a red triangle appears above a candle (Entry Box Confirmed Sell), it signals a potential bearish entry. Consider entering at the close of the signal candle or on a slight pullback.
📍Stop-Loss Placement:
For Buys: Place your stop-loss just below the recent swing low or the bottom of the green “Entry Box” zone.
For Sells: Place your stop-loss just above the recent swing high or the top of the red “Entry Box” zone.
🎯 Optional Take-Profit Strategy:
Use a 1:2 or 1:3 risk-reward ratio.
🛑 🛑 🛑 🛑 🛑 🛑 🛑 7 DAY FREE TRAIL PUBLIC USE 🛑 🛑 🛑 🛑 🛑 🛑 🛑 🛑
$50 per month
Golden Footprint View Pro v1.0 – Confirmed
//@version=5
indicator("Golden Footprint View Pro v1.0 – Confirmed", overlay=true)
// === INPUTS ===
deltaMultiplier = input.float(1.0, title="Delta Strength Multiplier")
showDeltaColoring = input.bool(true, title="Color Candles by Delta Strength?")
threshold = input.float(0.2, title="Delta Coloring Threshold (0-1)")
rsiPeriod = input.int(14, title="RSI Period")
cciPeriod = input.int(20, title="CCI Period")
showSignals = input.bool(true, title="Show Confirmed Entry Signals?")
// === DELTA CALCULATION ===
delta = volume * (close - open)
normalizedDelta = volume != 0 ? (delta * deltaMultiplier / volume) : 0.0
// === INDICATORS ===
rsi = ta.rsi(close, rsiPeriod)
cci = ta.cci(close, cciPeriod)
// === PRICE ACTION: Rejection Candle
upperWick = high - math.max(close, open)
lowerWick = math.min(close, open) - low
bodySize = math.abs(close - open)
isRejection = lowerWick > bodySize * 1.5 or upperWick > bodySize * 1.5
// === STRUCTURE BREAK LOGIC
prevHigh = ta.highest(close , 5)
prevLow = ta.lowest(close , 5)
bosUp = close > prevHigh
bosDown = close < prevLow
// === SIGNAL LOGIC
buySignal = showSignals and normalizedDelta > threshold and rsi < 40 and cci < -100 and isRejection and bosUp
sellSignal = showSignals and normalizedDelta < -threshold and rsi > 60 and cci > 100 and isRejection and bosDown
// === CANDLE COLORING ===
barcolor(showDeltaColoring and normalizedDelta > threshold ? color.lime :showDeltaColoring and normalizedDelta < -threshold ? color.red :showDeltaColoring ? color.gray : na)
// === SIGNAL PLOTS ===
plotshape(buySignal, location=location.belowbar, color=color.green, style=shape.labelup, text="BUY", size=size.small)
plotshape(sellSignal, location=location.abovebar, color=color.red, style=shape.labeldown, text="SELL", size=size.small)
// === DEBUG (Optional)
plotchar(delta, title="Delta", location=location.bottom, color=color.white, size=size.tiny, offset=-1)
Golden Pattern – Head & Shoulders v2.2//@version=5
indicator("Golden Pattern – Head & Shoulders v2.2", overlay=true)
enable_HS = input.bool(true, "Enable Head & Shoulders Detection")
show_targets = input.bool(true, "Show TP1/TP2/TP3 Levels")
min_dist = input.int(5, "Min Distance Between Points", minval=3)
sensitivity = input.float(1.5, "Deviation %", minval=0.1)
sl_buffer = input.float(0.5, "SL Buffer %")
// نقاط محورية
ph = ta.pivothigh(high, min_dist, min_dist)
pl = ta.pivotlow(low, min_dist, min_dist)
// تخزين الرأس والكتفين
var float sh1 = na
var float head = na
var float sh2 = na
var int sh1_bar = na
var int head_bar = na
var int sh2_bar = na
var float ish1 = na
var float ihead = na
var float ish2 = na
var int ish1_bar = na
var int ihead_bar = na
var int ish2_bar = na
// رأس وكتفين (بيع)
if not na(ph)
if na(sh1)
sh1 := ph
sh1_bar := bar_index
else if na(head) and ph > sh1 and bar_index - sh1_bar > min_dist
head := ph
head_bar := bar_index
else if na(sh2) and ph < head and math.abs(ph - sh1)/sh1 < sensitivity/100 and bar_index - head_bar > min_dist
sh2 := ph
sh2_bar := bar_index
else
sh1 := ph
sh1_bar := bar_index
head := na
sh2 := na
// رأس وكتفين معكوس (شراء)
if not na(pl)
if na(ish1)
ish1 := pl
ish1_bar := bar_index
else if na(ihead) and pl < ish1 and bar_index - ish1_bar > min_dist
ihead := pl
ihead_bar := bar_index
else if na(ish2) and pl > ihead and math.abs(pl - ish1)/ish1 < sensitivity/100 and bar_index - ihead_bar > min_dist
ish2 := pl
ish2_bar := bar_index
else
ish1 := pl
ish1_bar := bar_index
ihead := na
ish2 := na
// خطوط الرقبة
neckline_sell = (sh1 + sh2) / 2
neckline_buy = (ish1 + ish2) / 2
sell_break = enable_HS and not na(sh2) and close < neckline_sell and bar_index > sh2_bar
buy_break = enable_HS and not na(ish2) and close > neckline_buy and bar_index > ish2_bar
// TP / SL
depth_sell = head - neckline_sell
depth_buy = neckline_buy - ihead
tp1_sell = sell_break ? close - depth_sell : na
tp2_sell = sell_break ? close - depth_sell * 1.5 : na
tp3_sell = sell_break ? close - depth_sell * 2.0 : na
sl_sell = sell_break ? head + head * sl_buffer / 100 : na
tp1_buy = buy_break ? close + depth_buy : na
tp2_buy = buy_break ? close + depth_buy * 1.5 : na
tp3_buy = buy_break ? close + depth_buy * 2.0 : na
sl_buy = buy_break ? ihead - ihead * sl_buffer / 100 : na
// منع التكرار
var bool lastBuyPlotted = false
var bool lastSellPlotted = false
var bool plotBuySignal = false
var bool plotSellSignal = false
plotBuySignal := false
plotSellSignal := false
if buy_break and not lastBuyPlotted
plotBuySignal := true
lastBuyPlotted := true
lastSellPlotted := false
if sell_break and not lastSellPlotted
plotSellSignal := true
lastSellPlotted := true
lastBuyPlotted := false
// إشارات الدخول
plotshape(plotBuySignal, location=location.belowbar, style=shape.labelup, color=color.green, text="BUY")
plotshape(plotSellSignal, location=location.abovebar, style=shape.labeldown, color=color.red, text="SELL")
// رسم الأهداف (مع زر تحكم)
if plotBuySignal and show_targets
line.new(bar_index, tp1_buy, bar_index + 20, tp1_buy, color=color.green)
line.new(bar_index, tp2_buy, bar_index + 20, tp2_buy, color=color.teal)
line.new(bar_index, tp3_buy, bar_index + 20, tp3_buy, color=color.blue)
line.new(bar_index, sl_buy, bar_index + 20, sl_buy, color=color.red)
if plotSellSignal and show_targets
line.new(bar_index, tp1_sell, bar_index + 20, tp1_sell, color=color.green)
line.new(bar_index, tp2_sell, bar_index + 20, tp2_sell, color=color.teal)
line.new(bar_index, tp3_sell, bar_index + 20, tp3_sell, color=color.blue)
line.new(bar_index, sl_sell, bar_index + 20, sl_sell, color=color.red)
CSD, EC, ECSD & SPECIdentify Cliniq Model 5 elements. Identify EC with a line from previous H/L closed beyond, CSD, ECSD, and SPEC with markers. Or just use bar colors for SPEC.
Global M2 Money Supply (USD) (27 currencies)M2 for 27 currencies, converted into USD.
Does not constitute 100% of global M2, but ~90% accounted for.
Leverages Dylan LeClair's starting point, adds to it.
Bloomberg Financial Conditions Index (Proxy)The Bloomberg Financial Conditions Index (BFCI): A Proxy Implementation
Financial conditions indices (FCIs) have become essential tools for economists, policymakers, and market participants seeking to quantify and monitor the overall state of financial markets. Among these measures, the Bloomberg Financial Conditions Index (BFCI) has emerged as a particularly influential metric. Originally developed by Bloomberg L.P., the BFCI provides a comprehensive assessment of stress or ease in financial markets by aggregating various market-based indicators into a single, standardized value (Hatzius et al., 2010).
The original Bloomberg Financial Conditions Index synthesizes approximately 50 different financial market variables, including money market indicators, bond market spreads, equity market valuations, and volatility measures. These variables are normalized using a Z-score methodology, weighted according to their relative importance to overall financial conditions, and then aggregated to produce a composite index (Carlson et al., 2014). The resulting measure is centered around zero, with positive values indicating accommodative financial conditions and negative values representing tighter conditions relative to historical norms.
As Angelopoulou et al. (2014) note, financial conditions indices like the BFCI serve as forward-looking indicators that can signal potential economic developments before they manifest in traditional macroeconomic data. Research by Adrian et al. (2019) demonstrates that deteriorating financial conditions, as measured by indices such as the BFCI, often precede economic downturns by several months, making these indices valuable tools for predicting changes in economic activity.
Proxy Implementation Approach
The implementation presented in this Pine Script indicator represents a proxy of the original Bloomberg Financial Conditions Index, attempting to capture its essential features while acknowledging several significant constraints. Most critically, while the original BFCI incorporates approximately 50 financial variables, this proxy version utilizes only six key market components due to data accessibility limitations within the TradingView platform.
These components include:
Equity market performance (using SPY as a proxy for S&P 500)
Bond market yields (using TLT as a proxy for 20+ year Treasury yields)
Credit spreads (using the ratio between LQD and HYG as a proxy for investment-grade to high-yield spreads)
Market volatility (using VIX directly)
Short-term liquidity conditions (using SHY relative to equity prices as a proxy)
Each component is transformed into a Z-score based on log returns, weighted according to approximated importance (with weights derived from literature on financial conditions indices by Brave and Butters, 2011), and aggregated into a composite measure.
Differences from the Original BFCI
The methodology employed in this proxy differs from the original BFCI in several important ways. First, the variable selection is necessarily limited compared to Bloomberg's comprehensive approach. Second, the proxy relies on ETFs and publicly available indices rather than direct market rates and spreads used in the original. Third, the weighting scheme, while informed by academic literature, is simplified compared to Bloomberg's proprietary methodology, which may employ more sophisticated statistical techniques such as principal component analysis (Kliesen et al., 2012).
These differences mean that while the proxy BFCI captures the general direction and magnitude of financial conditions, it may not perfectly replicate the precision or sensitivity of the original index. As Aramonte et al. (2013) suggest, simplified proxies of financial conditions indices typically capture broad movements in financial conditions but may miss nuanced shifts in specific market segments that more comprehensive indices detect.
Practical Applications and Limitations
Despite these limitations, research by Arregui et al. (2018) indicates that even simplified financial conditions indices constructed from a limited set of variables can provide valuable signals about market stress and future economic activity. The proxy BFCI implemented here still offers significant insight into the relative ease or tightness of financial conditions, particularly during periods of market stress when correlations among financial variables tend to increase (Rey, 2015).
In practical applications, users should interpret this proxy BFCI as a directional indicator rather than an exact replication of Bloomberg's proprietary index. When the index moves substantially into negative territory, it suggests deteriorating financial conditions that may precede economic weakness. Conversely, strongly positive readings indicate unusually accommodative financial conditions that might support economic expansion but potentially also signal excessive risk-taking behavior in markets (López-Salido et al., 2017).
The visual implementation employs a color gradient system that enhances interpretation, with blue representing neutral conditions, green indicating accommodative conditions, and red signaling tightening conditions—a design choice informed by research on optimal data visualization in financial contexts (Few, 2009).
References
Adrian, T., Boyarchenko, N. and Giannone, D. (2019) 'Vulnerable Growth', American Economic Review, 109(4), pp. 1263-1289.
Angelopoulou, E., Balfoussia, H. and Gibson, H. (2014) 'Building a financial conditions index for the euro area and selected euro area countries: what does it tell us about the crisis?', Economic Modelling, 38, pp. 392-403.
Aramonte, S., Rosen, S. and Schindler, J. (2013) 'Assessing and Combining Financial Conditions Indexes', Finance and Economics Discussion Series, Federal Reserve Board, Washington, D.C.
Arregui, N., Elekdag, S., Gelos, G., Lafarguette, R. and Seneviratne, D. (2018) 'Can Countries Manage Their Financial Conditions Amid Globalization?', IMF Working Paper No. 18/15.
Brave, S. and Butters, R. (2011) 'Monitoring financial stability: A financial conditions index approach', Economic Perspectives, Federal Reserve Bank of Chicago, 35(1), pp. 22-43.
Carlson, M., Lewis, K. and Nelson, W. (2014) 'Using policy intervention to identify financial stress', International Journal of Finance & Economics, 19(1), pp. 59-72.
Few, S. (2009) Now You See It: Simple Visualization Techniques for Quantitative Analysis. Analytics Press, Oakland, CA.
Hatzius, J., Hooper, P., Mishkin, F., Schoenholtz, K. and Watson, M. (2010) 'Financial Conditions Indexes: A Fresh Look after the Financial Crisis', NBER Working Paper No. 16150.
Kliesen, K., Owyang, M. and Vermann, E. (2012) 'Disentangling Diverse Measures: A Survey of Financial Stress Indexes', Federal Reserve Bank of St. Louis Review, 94(5), pp. 369-397.
López-Salido, D., Stein, J. and Zakrajšek, E. (2017) 'Credit-Market Sentiment and the Business Cycle', The Quarterly Journal of Economics, 132(3), pp. 1373-1426.
Rey, H. (2015) 'Dilemma not Trilemma: The Global Financial Cycle and Monetary Policy Independence', NBER Working Paper No. 21162.
BTC/ETH Lot Size for Dexin - V1.0
█ Overview - This tool is specifically tailored for Delta Exchange India’s users.
I use this interactive tool before taking a position in the BTC’s futures perpetual market . With only 3 mouse clicks, I see all the necessary information, whether a Long or Short position.
A visual of Liquidation Price Level, Stop Loss Price Level, Entry Price Level, Break-even Price Level, and Take Profit Price Level can be immediately seen.
On the top right corner of the chart, which Leverage is to be used, No. of Lots to be taken, expected Profit amount, Loss amount, Brokerage Fees, Risk to Reward Ratios, and Return on Investment are shown, excluding brokerage travel. To get the correct answer in the table that suits your account and risk-taking appetite, the user needs to enter the account balance and Risk per trade.
It also does live tracking of the position, and alerts can be configured too.
█ How to Use
Load the indicator on an active chart.
In the Trading View, ensure that the Magnets is enabled (on the left panel). This will precisely select the price levels you want to choose from OHLC for a candle.
When you first load the tool on the bottom of the chart, you will see a blue box with text in white color guiding you on what you need to do.
Before the first click, the box shall prompt “On the signal candle, set the entry level, where the position would be executed”.
Once the entry price level is selected, the next prompt in the blue box shall be “Set the stop loss level where the position would be exited”. Thus, you need to click the stop loss price level.
Now that the two clicks of Entry and Stop Loss are already done, the last remaining is for the take profit. The last prompt shall be “Set the profit level where the position would be exited”. Therefore, you need to select your take-profit level
Finally, when all three points are selected, the tool shall draw trade zones.
The tool automatically determines whether it is a Long Position or Short Position from the Stop loss and take-profit price levels concerning the entry price level
If the take profit level is above the entry price, the stop must be below, and vice versa; otherwise, an error occurs.
You can change levels by dragging the handles that appear when you select the indicator, or by entering new values in the settings.
Once the position tool is on a chart, it will appear at the same levels on all symbols you use.
If you select the position tool on your chart and delete it, this will also delete the indicator from the chart. You will need to re-add it if you want to draw another position tool. You can add multiple instances of the indicator if you need a position tool on more than one of your charts.
█ Features
Display
The tool displays the following information as graphical visuals
The Liquidation to Stop Loss, Stop Loss to Entry, Entry to Break-even, and Entry to Take Profit zones shall be initiated from the entry candle point.
If you want to be from the candle that crossed the level at a different time from the entry candle, you may go to the settings and adjust the time accordingly. Please note that the time interval is 15 minutes, so at times you may not be able to see the graphical display; however, once the 15-minute time interval is over, you will see the graphical display on the chart.
The tool displays the following information in a tabulated manner
The first row indicates the Leverage that is best suited. The leverage selection by default is greater than or equal to the risk distance.
The second row indicates the number of lots that is computed in relation to the account balance, Risk appetite, Entry price, and Stop Loss price.
The third row indicates estimated profit considering taker's fees and is computed in relation to the number of Lots, Entry price, and Take-Profit price.
The fourth row indicates estimated loss considering taker's fees and is computed in relation to the number of Lots, Entry price, and Stop Loss price.
The fifth row indicates the actual Risk to Reward Ratio, ignoring the travel that pertains to fees.
The sixth row indicates actual Return on Investment, ignoring the travel that pertains to fees.
The intent is to allow the user to make an informed decision prior to taking a position by seeing “$/Rs.” or “% of R O I” or “R : R”.
In case the user wants to know beforehand what the expected charges are that need to be borne before taking a position, that too is made available in the seventh and eighth rows. Both sides' charges are made available for ready reference, irrespective of the outcome of the trade, the user knows the consequences beforehand.
█ Settings
'Trade Sizing'
The tool's input menu is divided into various parts. The first part is 'Trade Sizing'. The user needs to key in the exact number that appears in the Delta Exchange India account against 'Account Balance ($)'. The second thing the user needs to do is key in the 'Risk per Trade'. By default,t it is set to 0.25 and has a default stop change of 0.25. Alternatively, the user can key in any number (Whole number or Rational number) within 100 if that suits their risk management criterion.
'Trade Levels'
Allows users to manually set the Entry, Time, Stop Loss, and Take Profit Price Levels.
'Aggressive Mode Selection'
As the Liquidation zone is shown on the chart, if the user feels that the liquidation price level is too far from the stop loss, this option of 'Use Aggressive Leverage?' allows to increase the leverage, thus reducing the investment amount and in return increasing the Return on Investment %.
The second option in this category is 'Compute Lots based on invested Margin?' itself is self-explanatory, and thus the tabulated data shall be populating the data based on the number entered by the user against 'Margin to be invested ($)'. It is for the user to ensure that the estimated outcomes are within their risk management criterion.
'Conversion & Charges'
If the user wants to see the Profit, Loss, and Fees amount in 'Rs.', all that needs to be done is simply enable the 'Show P&L in Rs.?' The conversion shall take place considering 1 USD = 85 Rs. Same as that carried out by Delta Exchange India.
If the user wants to see the Brokerage Fees, all that needs to be done is simply enable the 'Show Brokerage Fees?'. On enabling this, the table shall show Profitable Trade's (PT) Fees and Lost Trade's (LT) Fees irrespective of the outcome of the trade. The intent is to allow the user to make informed decisions to avoid regrets or surprises at the end of the trade.
'Table'
The division of the input section is related to table position, font size and colors for text and background.
█ Alerts
Alerts can be configured by clicking 'More' (the three dots that appear when you place the cursor on the indicator title that appears on the top left corner of the chart). Alternatively, one can configure alerts by right-clicking on either of the two price levels - Stop Loss price level or Take Profit Price level. Upon right clicking, a window shall appear and the topmost line on that window shall display 'Add alert on ……….' The user can thus put alerts on either of the key levels, such as Stop Loss, Take Profit, and Break Even, or on all of them one by one.
RSI - SECUNDARIO - mauricioofsousaSecondary RSI – MGO
Reading the rhythm behind the price action
The Secondary RSI is a specialized oscillator developed as part of the MGO (Matriz Gráficos ON) methodology. It works as a refined strength filter, designed to complement traditional RSI readings by isolating the true internal rhythm of price action and reducing the influence of market noise.
While the standard RSI measures price momentum, the Secondary RSI focuses on identifying breaks in oscillatory balance—the moments when the market shifts from accumulation to distribution or from compression to expansion.
🎯 What the Secondary RSI highlights:
Internal imbalances in energy between buyers and sellers
Micro-divergences not visible on standard RSI
Areas of price fatigue or overextension that often precede reversals
Confirmation zones for MGO oscillatory events (RPA, RPB, RBA, RBB)
📊 Recommended use:
Combine with the Primary RSI for dual-layer validation
Use as a noise-reduction tool before entering trends
Ideal in medium timeframes (12H / 4H) where oscillatory patterns form clearly
🧠 How it works:
The Secondary RSI recalculates the momentum signal using a block-based interpretation (aligned with the MGO structure) instead of simply following raw candle data. It adapts to the periodic nature of price behavior and provides the trader with a more stable and reliable measure of true market strength.
RSI - PRIMARIO -mauricioofsousa
MGO Primary – Matriz Gráficos ON
The Blockchain of Trading applied to price behavior
The MGO Primary is the foundation of Matriz Gráficos ON — an advanced graphical methodology that transforms market movement into a logical, predictable, and objective sequence, inspired by blockchain architecture and periodic oscillatory phenomena.
This indicator replaces emotional candlestick reading with a mathematical interpretation of price blocks, cycles, and frequency. Its mission is to eliminate noise, anticipate reversals, and clearly show where capital is entering or exiting the market.
What MGO Primary detects:
Oscillatory phenomena that reveal the true behavior of orders in the book:
RPA – Breakout of Bullish Pivot
RPB – Breakout of Bearish Pivot
RBA – Sharp Bullish Breakout
RBB – Sharp Bearish Breakout
Rhythmic patterns that repeat in medium timeframes (especially on 12H and 4H)
Wave and block frequency, highlighting critical entry and exit zones
Validation through Primary and Secondary RSI, measuring the real strength behind movements
Who is this indicator for:
Traders seeking statistical clarity and visual logic
Operators who want to escape the subjectivity of candlesticks
Anyone who values technical precision with operational discipline
Recommended use:
Ideal timeframes: 12H (high precision) and 4H (moderate intensity)
Recommended assets: indices (e.g., NASDAQ), liquid stocks, and futures
Combine with: structured risk management and macro context analysis
Real-world performance:
The MGO12H achieved a 92% accuracy rate in 2025 on the NASDAQ, outperforming the average performance of major global quantitative strategies, with a net score of over 6,200 points for the year.
Market Map – AK_TradesMarket Map – AK_Trades
A clean, context-driven market structure tool built to enhance the Futures Scalping Signal.
🔹 Dynamic Support & Resistance (auto-adjusting, dashed lines)
🔹 Real-Time Trend Detection with EMA Background
🔹 Breakout Signals using ATR-based filters
🔹 Minimalist, powerful, and clutter-free
Disclaimer: This script is for educational and informational purposes only. It is not financial advice. Use at your own risk. The author assumes no responsibility for any trading losses incurred.
Trading Time Highlighter v2Check boxes for days of week.
Set the time you want to trade or backtest.
Adjust for UTC time.
GM
Multi-Pair MTF Crypto Strategy (Backtest Version)Multi-Pair MTF Crypto Scanner (Smart Long/Short Indicator)
This advanced TradingView indicator is designed for crypto traders seeking precise, risk-filtered signals across multiple pairs and timeframes. It combines institutional-grade signal logic with customizable risk management and clean visual labeling.
🔍 Core Features:
✅ Multi-Pair & Multi-Timeframe Scanning
Scans assets like BTC, ETH, SOL across timeframes (15m, 1H, 4H)
✅ Buy/Sell Signal Engine
Based on EMA 50/200 crossover, RSI, and volume spikes
✅ Dynamic Risk Management
Calculates Stop Loss (SL), Take Profit (TP), and Risk-Reward Ratio (RRR) using ATR
✅ RRR Filter
Signals only shown if RRR meets your defined minimum (default 1.5)
✅ Confirmation Mode
Optional setting to avoid premature signals by requiring bar-close confirmation
✅ Visual Trade Zones
Entry, SL, and TP levels plotted directly on chart
✅ Debug Mode
Shows labels when trades are rejected due to RRR filters
🧠 Ideal For:
Crypto scalpers, swing traders, and algorithmic signal testers
Traders focused on high probability entries with defined risk
📣 Alerts:
Real-time alerts for qualified BUY and SHORT signals
Configurable for automated webhook systems or mobile push
Open-source script
In true TradingView spirit, the creator of this script has made it open-source, so that traders can review and verify its functionality. Kudos to the author! While you can use it for free, remember that republishing the code is subject to our House Rules.
flydreams143
Disclaimer
The information and publications are not meant to be, and do not constitute, financial, investment, trading, or other types of advice or recommendations supplied or endorsed by TradingView. Read more in the Terms of Use.
Fidelity Sector Switching ProgramApproximate recreation of the "Fidelity Sector Fund Switching Program" based on Walter Deemer’s published methodology. Source: walterdeemer.com
This script analyzes Fidelity sector funds, calculates relative strength ratings, and ranks them by strength. It selects the top 3 funds for holding. Exit triggers:
Fund drops into the bottom half of all funds.
Fund falls below the S&P 500.
Fund falls below the money market rate (T-Bills).
strength_rating = (( (0.5 * 8) + (0.25 * 16) + (0.25 * 32) ) * 1000) - 1000
Notes :
Funds marked with " * * " are not official switching set but are included for long-term trend observation.
* 90d T-Bill rates are unavailable; TBIL ETF used as proxy.
* Script loads slowly due to required fund data volume.
• Minor output variations may occur if the Wednesday market is closed; script uses the next available close.
Intended Use & Disclaimer:
• Intended for educational and analytical use only. Not financial or investment advice.
• This 'program' may be at risk of Fidelity’s 90-day round-trip violation policy.
AI ALGO [SardarUmar]This PineScript code is a comprehensive trading strategy that combines trend identification, rejection signals, and profit target management. Here's a detailed breakdown:
Trend Identification
1. Supertrend: The code uses a Supertrend indicator with a weighted moving average (WMA) and exponential moving average (EMA) to smooth out the trend line.
2. Trend Direction: The trend direction is determined by the crossover and crossunder of the Supertrend line.
Rejection Signals
1. Bullish Rejection: A bullish rejection signal is generated when the price consolidates at the trend line and then moves above it.
2. Bearish Rejection: A bearish rejection signal is generated when the price consolidates at the trend line and then moves below it.
Profit Target Management
1. Stop Loss (SL): The stop loss level is calculated based on the Average True Range (ATR) and a specified multiplier.
2. Take Profit (TP) Levels: The code calculates multiple take profit levels (TP1, TP2, TP3) based on the stop loss distance and specified multipliers.
Alerts
1. Trend Change Alerts: Alerts are generated when the price crosses above or below the stop loss level, indicating a potential trend change.
2. Rejection Signal Alerts: Alerts are generated when the price rejects at the stop loss level, indicating a potential rejection signal.
3. TP Hit Alerts: Alerts are generated when the price reaches the take profit levels.
Visualizations
1. Trend Line: The trend line is plotted on the chart, with different colors for bullish and bearish trends.
2. Rejection Signals: Rejection signals are plotted as shapes on the chart.
3. Profit Target Levels: The profit target levels are plotted as lines on the chart.
Notes:
- This code is for educational purposes only and should not be used as is in live trading without thorough backtesting and validation.
- Traders should always use proper risk management techniques and position sizing when trading with automated systems.
The code seems well-structured and readable. However, it's essential to test and validate any trading strategy before using it in live markets.
