RiskCalc FX & GoldRiskCalc FX & Gold is a multi-market position sizing tool designed to help you manage risk quickly and accurately. With this script, simply enter your account capital, the percentage of risk you wish to take, and your stop in ticks. Depending on the selected market—Forex or XAUUSD—the script automatically adjusts its calculations:
Forex: Assumes 1 lot equals 100,000 units.
XAUUSD: Assumes 1 lot equals 100 ounces.
The script calculates your risk in dollars and, using a fixed value of 1 USD per tick per lot, determines the ideal position size in both lots and total contracts. Results are displayed in a clear, centralized table at the top of the chart for real-time decision-making.
Perfect for traders operating across multiple markets who need an automated and consistent approach to risk management.
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Sovereign Gold Hodlers Script for comparing nations and their gold, with options including:
Default Comparing the Price of Gold
Use Relative Valuations price / prior price
Measure Reserves/Price
GDP/Gold Price
GDP/Gold Reserves
Given the state of the world I thought it'd be good do see where countries stand, how much real money they hold. I think gold is going to play an increased role in trade between economies in the near future.
Gold Silver SpreadGold silver Spread
Different Between Gold & Silver Price
Find Spread Opportunity
Gold Vs Silver Strength Strategy
Gold Reader by MarketReaderGold Reader is an indicator created for gold trading only. It is the result of deeplearning and cluster 2 step analysis. These analysis highligth specific intra-days patterns.
Pattern 1 is a full bearish day, pattern 4 a full bullish day.
Pattern 2 is an accumulation - manipulation - and bearish impulsion day
Pattern 3 is an accumulation - manipulation - and bullish impulsion day
The indicator draws 6 boxes.
-The orange box (high of pattern 1) correspond to the time and price where the high of the day is likely to form if we are in a pattern 1.
-The purple box (low of pattern 4) correspond to the time and price where the low of the day is likely to form if we are in a pattern 4.
-The red box (high of pattern 2) correspond to the time and price where the high of the day is likely to form if we are in a pattern 2.
-The blue box (low of pattern 3) correspond to the time and price where the low of the day is likely to form if we are in a pattern 3.
The 2 gray box correspond to the high probability of high of a bull day and low of a bear day. It is good area for a end of the day reversal.
ORZ= optimal reversal zone. It is a specific pattern for New York continuation of London session in case of pattern 1 and 4.
{Gunzo} Stock to Flow (Gold, Silver, Dollar, Bitcoin)This indicator displays the Stock to Flow (S2F) ratio for popular commodities (Gold, Silver, Dollar, Euro, Bitcoin, Ethereum) in order to
compare them and determine which ones could be a good Store of Value (SoV).
OVERVIEW :
Stock to Flow is a popular indicator used to predict commodities scarcity. It evaluates the total stock of a commodity against the total amount that can be produced during a year. This model supposes that if scarcity is increasing, the price is going to increase.
This model has been used over the last years on Bitcoin to determine if the asset was undervalued or overvalued, and even make prediction models on the future price.
This script is going to focus on the Stock to Flow ratio (total stock/amount produced) to compare the following assets over time :
Mining resources (mined) for Gold and Silver
Cryptos assets (mined) for Bitcoin and Ethereum
FIAT currencies (banknotes printed) for Dollar and Euro
CALCULATION :
The calculation of the Stock to Flow ratio evaluates the total stock of a commodity produced against the production made for a specific year. The data is calculated on a yearly basis, then interpolated to get monthly or daily values.
DATA ORIGIN :
The main information needed to calculate the Stock to Flow ratio is the "yearly production" of a commodity. I tried to retrieve that information from the most reliable sources :
for Gold from research on www.gold.org
for Silver from research on www.silverinstitute.org
for Ethereum from research on etherscan.io
for Bitcoin from data source "QUANDL:BCHAIN/TOTBC" from www.quandl.com
for Dollar from research on www.federalreserve.gov
for Euro from research on www.ecb.europa.eu
SETTINGS :
Smoothing for interpolated data : Smoothing factor for assets that are calculated yearly and then interpolated (Gold, Silver, Dollar, Euro, and Ethereum)
Smoothing for non interpolated data : Smoothing factor for assets that are calculated daily and not interpolated (Bitcoin)
Display asset names : Display assets names in a colored rectangle on the right side of the chart
Display asset values : Display assets Stock to Flow ratio in a colored rectangle on the right side of the chart
Display key events for assets : Display important events for the assets at the bottom of the chart using the same color as the assets lines (for example Orange diamond is a Bitcoin halving). Please refer to the script code for the details of all events.
USAGE :
This script can be used on any asset available on TradingView as the data used is either static or external.
However I recommend using it the Gold asset from currency.com as the depth of the chart will be bigger (since 1980s).
It is recommended to used this script on the monthly timeframe as the chart data is calculated yearly and then interpolated.
Lumber to Gold ratioDISCRIPTION:-
Lumber to gold ratio helps to predict up upcomming market correction as investors are flocking towards safe heaven.
USE CASE SCENARIO:-
If the ratio is above the zero horizontal line it is a risk of scenario
If the ratio plunge below zero it might show imminent market correction.
Swing or scalping GOLD [RickAtwood] Swing or scalping - automatically determine the currently active trends. Various moving averages are used. It is also designed for any type of trader from scalping to swing.
The key 3 moving averages are designed to identify support and resistance. If the price bounces off them, boldly open and place a stop of 10-20 pips(currency pairs)
Functional
buy ---> green candles
sell ----> red candles
There are alerts for buy and sell based on crossovers
If the price is above the cloud then buy. If the price is below the cloud then sell. The main thing is to open deals only at the very beginning when the price starts to leave the cloud. Also, your stops will be minimal.
When testing this system, we opened 750 trades manually. Success rate of 71% for currency pairs and for gold
P.s If you have any questions about how to open, how to close deals. Always write to me, I will help you) Success to all.
Portfolio and Risk Management: Gold Based Net Growth CoefficientHello, if our topic is stocks, whatever signal we get, we have to divide and reduce the risk.
Apart from the risk, we need inflation-free figures to detect a clear growth.
Gold is one of the most successful tools to beat inflation in this regard in the historical context.
When the economy is good, we have to beat both commodities and inflation.
For this purpose, I found it appropriate to develop a net growth factor free from gold growth.
Investors need several stocks with a high growth rate and as much risk-free as possible.
Personally, I think that the science of portfolio and risk management will last a lifetime and should continue.
I think this subject is a research and development subject.(R & D)
My research and publications on this matter will continue publicly.
I wish everyone a good day.
NOTE : You can determine the return in the time period you want to look back by adjusting the period in the rate you want from the menu.
The standard value is 200 days. (1 year)
Deviation from the futures market for GOLDThis indicator shows the deviation from the gold futures market.
ANN MACD GOLD (XAUUSD)This script aims to establish artificial neural networks with gold data.(4H)
Details :
Learning cycles: 329818
Training error: 0.012767 ( Slightly above average but negligible.)
Input columns: 19
Output columns: 1
Excluded columns: 0
Training example rows: 300
Validating example rows: 0
Querying example rows: 0
Excluded example rows: 0
Duplicated example rows: 0
Input nodes connected: 19
Hidden layer 1 nodes: 5
Hidden layer 2 nodes: 1
Hidden layer 3 nodes: 0
Output nodes: 1
Learning rate: 0.7000
Momentum: 0.8000
Target error: 0.0100
NOTE : Alarms added.
And special thanks to dear wroclai for his great effort.
Deep learning series will continue . Stay tuned! Regards.
Gold CorrelationsGold has correlations with many trading pairs such as silver, oil, euro, yen, usd, aud, spx, nekkai and many more to go.
In this script i have added GOLD, SILVER, currency of US, EUROPE and JAPAN.
NOTE : More corelations will be added soon.
The corelations will ranged from 0 to 100 denoting the strength.
It is an modified indicator. To be more precise, the raw data is converted to unbounded range according to their strength and then converted to bounded range of 0 to 100.
HOW TO USE
As we can see in the first vertical black line, US started going up and after the second vertical black line, Gold, silver, Europe, Japan started to go downwards.
We can see a nice correlation and call it a nice short.
In future will be adding more correlations.
Gap finder (gold minds)This tool highlights where gaps happens and outlines in the chart where the gap zones are. If there is a gap up there is a green line, a gap down it is red. The gap zone is highlighted in blue. You can choose the size of your gap with the input menu to the desired size. Feel free to ask comment below. Made for the Gold Minds group
DOW / GOLD RatioHere's a new version with color goodness and using CL1! as the gold spot source (longer history).
Goldbach Time Indicator🔧 Key Fixes Applied:
1. Time Validation & Bounds Checking:
Hour/Minute Bounds: Ensures hours stay 0-23, minutes stay 0-59
Edge Case Handling: Prevents invalid time calculations from causing missing data
UTC Conversion Safety: Better handling of timezone edge cases
2. Enhanced Value Validation:
NA Checking: Validates all calculated values before using them
Goldbach Detection: Only flags valid, non-NA values as Goldbach hits
Plot Safety: Prevents plotting invalid or NA values that could cause gaps
3. Improved Plot Logic:
Core Level Colors: Blue for core levels (29,35,71,77), yellow/lime/orange for regular hits
Debug Mode Enhanced: Shows all calculations with gray dots when enabled
Better Filtering: Only plots positive, valid values for minus calculations
4. Background vs Dots Issue:
The large green/blue background you see suggests the indicator is detecting Goldbach times correctly, but the dots weren't plotting due to validation issues. This should now be fixed.
Gold ValuationGold Value Index
The Gold Value Index (GVI) is a macro-driven oscillator that estimates the relative value of gold based on real-time movements in the US Dollar Index (DXY) and the 10-Year US Treasury Yield (US10Y). It helps traders contextualize gold’s price within broader macroeconomic pressure — identifying when gold may be over- or undervalued relative to these key drivers.
How It Works – Macro Inputs:
DXY (US Dollar Index): Typically moves inversely to gold. A rising dollar suggests downward pressure on gold value.
US10Y Yield: Higher yields increase the opportunity cost of holding gold, often leading to weaker gold prices.
Both inputs are Z-score normalized and inverted to reflect their typical negative correlation with gold. When combined, they form a single, scaled index from 0 (undervalued) to 100 (overvalued).
Why Use This Tool?
Gold reacts to macro forces as much as technical ones. The GVI blends these inputs into a clear, visual gauge to:
Anticipate mean-reversion setups.
Avoid emotionally-driven trades in extreme macro conditions.
Enhance timing by understanding gold's macro context.
Important Notes:
Data sources include ICEUS:DXY and TVC:US10Y via TradingView.
Code is protected — this is a private, invite-only script.
Goldman Sachs Risk Appetite ProxyRisk appetite indicators serve as barometers of market psychology, measuring investors' collective willingness to engage in risk-taking behavior. According to Mosley & Singer (2008), "cross-asset risk sentiment indicators provide valuable leading signals for market direction by capturing the underlying psychological state of market participants before it fully manifests in price action."
The GSRAI methodology aligns with modern portfolio theory, which emphasizes the importance of cross-asset correlations during different market regimes. As noted by Ang & Bekaert (2002), "asset correlations tend to increase during market stress, exhibiting asymmetric patterns that can be captured through multi-asset sentiment indicators."
Implementation Methodology
Component Selection
Our implementation follows the core framework outlined by Goldman Sachs research, focusing on four key components:
Credit Spreads (High Yield Credit Spread)
As noted by Duca et al. (2016), "credit spreads provide a market-based assessment of default risk and function as an effective barometer of economic uncertainty." Higher spreads generally indicate deteriorating risk appetite.
Volatility Measures (VIX)
Baker & Wurgler (2006) established that "implied volatility serves as a direct measure of market fear and uncertainty." The VIX, often called the "fear gauge," maintains an inverse relationship with risk appetite.
Equity/Bond Performance Ratio (SPY/IEF)
According to Connolly et al. (2005), "the relative performance of stocks versus bonds offers significant insight into market participants' risk preferences and flight-to-safety behavior."
Commodity Ratio (Oil/Gold)
Baur & McDermott (2010) demonstrated that "gold often functions as a safe haven during market turbulence, while oil typically performs better during risk-on environments, making their ratio an effective risk sentiment indicator."
Standardization Process
Each component undergoes z-score normalization to enable cross-asset comparisons, following the statistical approach advocated by Burdekin & Siklos (2012). The z-score transformation standardizes each variable by subtracting its mean and dividing by its standard deviation: Z = (X - μ) / σ
This approach allows for meaningful aggregation of different market signals regardless of their native scales or volatility characteristics.
Signal Integration
The four standardized components are equally weighted and combined to form a composite score. This democratic weighting approach is supported by Rapach et al. (2010), who found that "simple averaging often outperforms more complex weighting schemes in financial applications due to estimation error in the optimization process."
The final index is scaled to a 0-100 range, with:
Values above 70 indicating "Risk-On" market conditions
Values below 30 indicating "Risk-Off" market conditions
Values between 30-70 representing neutral risk sentiment
Limitations and Differences from Original Implementation
Proprietary Components
The original Goldman Sachs indicator incorporates additional proprietary elements not publicly disclosed. As Goldman Sachs Global Investment Research (2019) notes, "our comprehensive risk appetite framework incorporates proprietary positioning data and internal liquidity metrics that enhance predictive capability."
Technical Limitations
Pine Script v6 imposes certain constraints that prevent full replication:
Structural Limitations: Functions like plot, hline, and bgcolor must be defined in the global scope rather than conditionally, requiring workarounds for dynamic visualization.
Statistical Processing: Advanced statistical methods used in the original model, such as Kalman filtering or regime-switching models described by Ang & Timmermann (2012), cannot be fully implemented within Pine Script's constraints.
Data Availability: As noted by Kilian & Park (2009), "the quality and frequency of market data significantly impacts the effectiveness of sentiment indicators." Our implementation relies on publicly available data sources that may differ from Goldman Sachs' institutional data feeds.
Empirical Performance
While a formal backtest comparison with the original GSRAI is beyond the scope of this implementation, research by Froot & Ramadorai (2005) suggests that "publicly accessible proxies of proprietary sentiment indicators can capture a significant portion of their predictive power, particularly during major market turning points."
References
Ang, A., & Bekaert, G. (2002). "International Asset Allocation with Regime Shifts." Review of Financial Studies, 15(4), 1137-1187.
Ang, A., & Timmermann, A. (2012). "Regime Changes and Financial Markets." Annual Review of Financial Economics, 4(1), 313-337.
Baker, M., & Wurgler, J. (2006). "Investor Sentiment and the Cross-Section of Stock Returns." Journal of Finance, 61(4), 1645-1680.
Baur, D. G., & McDermott, T. K. (2010). "Is Gold a Safe Haven? International Evidence." Journal of Banking & Finance, 34(8), 1886-1898.
Burdekin, R. C., & Siklos, P. L. (2012). "Enter the Dragon: Interactions between Chinese, US and Asia-Pacific Equity Markets, 1995-2010." Pacific-Basin Finance Journal, 20(3), 521-541.
Connolly, R., Stivers, C., & Sun, L. (2005). "Stock Market Uncertainty and the Stock-Bond Return Relation." Journal of Financial and Quantitative Analysis, 40(1), 161-194.
Duca, M. L., Nicoletti, G., & Martinez, A. V. (2016). "Global Corporate Bond Issuance: What Role for US Quantitative Easing?" Journal of International Money and Finance, 60, 114-150.
Froot, K. A., & Ramadorai, T. (2005). "Currency Returns, Intrinsic Value, and Institutional-Investor Flows." Journal of Finance, 60(3), 1535-1566.
Goldman Sachs Global Investment Research (2019). "Risk Appetite Framework: A Practitioner's Guide."
Kilian, L., & Park, C. (2009). "The Impact of Oil Price Shocks on the U.S. Stock Market." International Economic Review, 50(4), 1267-1287.
Mosley, L., & Singer, D. A. (2008). "Taking Stock Seriously: Equity Market Performance, Government Policy, and Financial Globalization." International Studies Quarterly, 52(2), 405-425.
Oppenheimer, P. (2007). "A Framework for Financial Market Risk Appetite." Goldman Sachs Global Economics Paper.
Rapach, D. E., Strauss, J. K., & Zhou, G. (2010). "Out-of-Sample Equity Premium Prediction: Combination Forecasts and Links to the Real Economy." Review of Financial Studies, 23(2), 821-862.
The Price of Hard MoneyIf we calculate “the price of hard money” (the market capitalization weighted price of gold plus Bitcoin); we get this chart.
Since 2017, Bitcoin’s share of hard money growth has been increasing, we can see it visibly on the gold chart by a widening delta between the price of hard money and the Gold price. We can also see some interesting technical behaviours.
In 2021, Hard Money broke out and held this breakout above the 2011 Gold high. Only later in 2022 did a correction of 20% occur – typical of Golds historic volatility in periods of inflation and high interest rates.
Hard Money is at major support and we have evidence for a fundamental shift in investor capital flows away from gold and into Bitcoin.
This Indicator is useful:
- To track the market capitalization of Gold (estimated), Bitcoin and combined market capitalization of Hard Money.
- To track the price action and respective change in investor flows from Gold to Bitcoin .
Provided Bitcoin continues to suck more value out of gold with time, this chart will be useful for tracking price action of the combined asset classes into the years to come.
Golden Ratio Fibonacci Multipliers Top Detector [UO]Fibonacci levels that show the critical top and bottom levels. There is no way to miss the top and bottom. And a top detector.
Also the most important SMA lines (SMA 50, 200), EMA21. Those are the most frequently used lines by traders.
This indicator is based on the work of www.tradingview.com
His work set me thinking. Could I also see the bottom using Fibonacci numbers? Yes, of course.
My favorite timeframes with this indicator are 6H, 1D, 3D.
Intensively used for BTC and BNB. And useful for any other coin.
Golden Cross KAMAThe usage is very easy. When the line is green you can open long position, when the line is red you can open short position and when it's black just check by yourself.
Usually I use it with RSI and Bollinger Bands , in order to determine when the signal is strong or weak.
Just play with fastest and slowest SC to adjust the smoothness.
Golden Launch Pad🔰 Golden Launch Pad
This indicator identifies high-probability bullish setups by analyzing the relationship between multiple moving averages (MAs). A “Golden Launch Pad” is formed when the following five conditions are met simultaneously:
📌 Launch Pad Criteria (all must be true):
MAs Are Tightly Grouped
The selected MAs must be close together, measured using the Z-score spread — the difference between the highest and lowest Z-scores of the MAs.
Z-scores are calculated relative to the average and standard deviation of price over a user-defined window.
This normalizes MA distance based on volatility, making the signal adaptive across different assets.
MAs Are Bullishly Stacked
The MAs must be in strict ascending order: MA1 > MA2 > MA3 > ... > MA(n).
This ensures the short-term trend leads the longer-term trend — a classic sign of bullish structure.
All MAs Have Positive Slope
Each MA must be rising, based on a lookback period that is a percentage of its length (e.g. 30% of the MA’s bars).
This confirms momentum and avoids signals during sideways or weakening trends.
Price Is Above the Fastest MA
The current close must be higher than the first (fastest) moving average.
This adds a momentum filter and reduces false positives.
Price Is Near the MA Cluster
The current price must be close to the average of all selected MAs.
Proximity is measured in standard deviations (e.g. within 1.0), ensuring the price hasn't already made a large move away from the setup zone.
⚙️ Customization Options:
Use 2 to 6 MAs for the stack
Choose from SMA, EMA, WMA, VWMA for each MA
Adjustable Z-score window and spread threshold
Dynamic slope lookback based on MA length
Volatility-adjusted price proximity filter
🧠 Use Case:
This indicator helps traders visually and systematically detect strong continuation setups — often appearing before breakouts or sustained uptrends. It works well on intraday, swing, and positional timeframes across all asset classes.
For best results, combine with volume, breakout structure, or multi-timeframe confirmation.
