Lead-Lag Market Detector [CryptoSea]The Lead-Lag Market Detector is an advanced tool designed to help traders identify leading and lagging assets within a chosen market. This indicator leverages correlation analysis to rank assets based on their influence, making it ideal for traders seeking to optimise their portfolio or spot key market trends.
Key Features
Dynamic Asset Ranking: Utilises real-time correlation calculations to rank assets by their influence on the market, helping traders identify market leaders and laggers.
Customisable Parameters: Includes adjustable lookback periods and correlation thresholds to adapt the analysis to different market conditions and trading styles.
Comprehensive Asset Coverage: Supports up to 30 assets, offering broad market insights across cryptocurrencies, stocks, or other markets.
Gradient-Enhanced Table Display: Presents results in a colour-coded table, where assets are ranked dynamically with influence scores, aiding in quick visual analysis.
In the example below, the ranking highlights how assets tend to move in groups. For instance, BTCUSDT, ETHUSDT, BNBUSDT, SOLUSDT, and LTCUSDT are highly correlated and moving together as a group. Similarly, another group of correlated assets includes XRPUSDT, FILUSDT, APEUSDT, XTZUSDT, THETAUSDT, and CAKEUSDT. This grouping of assets provides valuable insights for traders to diversify or spread exposure.
If you believe one asset in a group is likely to perform well, you can spread your exposure into other correlated assets within the same group to capitalise on their collective movement. Additionally, assets like AVAXUSDT and ZECUSDT, which appear less correlated or uncorrelated with the rest, may offer opportunities to act as potential hedges in your trading strategy.
How it Works
Correlation-Based Scoring: Calculates pairwise correlations between assets over a user-defined lookback period, identifying assets with high influence scores as market leaders.
Customisable Thresholds: Allows traders to define a correlation threshold, ensuring the analysis focuses only on significant relationships between assets.
Dynamic Score Calculation: Scores are updated dynamically based on the timeframe and input settings, providing real-time insights into market behaviour.
Colour-Enhanced Results: The table display uses gradients to visually distinguish between leading and lagging assets, simplifying data interpretation.
Application
Portfolio Optimisation: Identifies influential assets to help traders allocate their portfolio effectively and reduce exposure to lagging assets.
Market Trend Identification: Highlights leading assets that may signal broader market trends, aiding in strategic decision-making.
Customised Trading Strategies: Adapts to various trading styles through extensive input settings, ensuring the analysis meets the specific needs of each trader.
The Lead-Lag Market Detector by is an essential tool for traders aiming to uncover market leaders and laggers, navigate complex market dynamics, and optimise their trading strategies with precision and insight.
Diversification
GDP BreakdownProvides an easy way for viewing the sub sections that make up a country's total GDP. Not all countries provide data for each subsector (Agriculture, Construction, Manufacturing, Mining, Public Administration, Services, Utilities). Only countries that provide complete data are able to be selected in the settings. If I've missed any please let me know in the comment section so they can be added. This is much easier than having to individually selecting each ticker for each country when looking to compare how diversified an economy is.
Diversified Investment EMA Cross Strategy SimulatorThis simulating indicator proves that even if you use a simple strategy, you can reduce your risk by diversifying your investments.
The strategy itself is simple.(only long)
Buy when 50 days EMA crosses over 200 days EMA.
Sell when 50 days EMA crosses under 200 days EMA.
Or, stop loss when the asset falls by 2% (eg).
Using this simple strategy on an asset is just a test of your luck.
However, this capital change graph shows that risk can be reduced by diversifying investment into eight assets rather than one asset.
Options
Total Assets Capital Change represents the sum of capital changes for 8 assets. The gray line is the initial capital.
Each Asset Capital Change represents all eight asset capital changes. In this case, the gray line is displayed as the initial capital divided by 8.
The rest of the options show a graph of capital change for each asset, showing when buys and sells occurred.
And set the start date, initial capital, stop loss %, and commission.
And select the 8 assets you want to invest in and you are ready to go. To effectively reduce risk, uncoupled assets would be better if possible.
The table in the lower right shows the selected asset and color.
Please enjoy the simulation.
Coefficient of Variation - EMA and SMA StDevYet another way to try and measure volatility. An alternative to using ATR is Standard Deviation, it can be used to measure volatility or what is also known as risk. SD measures how dispersed or far away the data is from the mean. It's commonly seen in risk management formulas or portfolio diversification formulas. The problem however is that the numbers that ATR and SD give off from one equity might not be relative to others or its own past. For example, SPY can give a large number despite not being as volatile as other equities while others being compared to can have smaller volatility numbers and still be more volatile looking.
A solution I thought of is to use percentages that are relatable to different equities. I found out another name for this idea comes from statistics and is known as coefficient of variation, also known as relative standard deviation. This helps see the volatility as a percentage and not just a number that only relates to what is being seen at the moment. I put in a border line on the zero level to see where zero is at but also to edit in case there is such a thing as a percentage number that can be too high or too low for volatility to be looked at if needed. The average and standard deviation formulas can use either simple moving average or exponential moving average.
