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Statistical and Financial MetricsGood morning traders!
This time I want to share with you a little script that, thanks to the use of arrays, allows you to have interesting statistical and financial insights taken from the symbol on chart and compared to those of another symbol you desire (in this case the metrics taken from the perpetual future ETHUSDT are compared to those taken from the perpetual future BTCUSDT, used as a proxy for the direction of cryptocurrency market)
By enabling "prevent repainting", the data retrieved from the compared symbol won't be on real time but they will static since they will belong to the previous closed candle
Here are the metrics you can have by storing data from a variable period of candles (by default 51):
✓ Variance (of the symbol on chart in GREEN; of the compared symbol in WHITE)
✓ Standard Deviation (of the symbol on chart in OLIVE; of the compared symbol in SILVER)
✓ Yelds (of the symbol on chart in LIME; of the compared symbol in GRAY) → yelds are referred to the previous close, so they would be calculated as the the difference between the current close and the previous one all divided by the previous close
✓ Covariance of the two datasets (in BLUE)
✓ Correlation coefficient of the two datasets (in AQUA)
✓ β (in RED) → this insight is calculated in three alternative ways for educational purpose (don't worry, the output would be the same).
WHAT IS BETA (β)?
The BETA of an asset can be interpretated as the representation (in relative terms) of the systematic risk of an asset: in other terms, it allows you to understand how big is the risk (not eliminable with portfolio diversification) of an asset based on the volatilty of its yelds.
We say that this representation is made in relative terms since it is expressed according to the market portfolio: this portfolio is hypothetically the portfolio which maximizes the diversification effects in order to kill all the specific risk of that portfolio; in this way the standard deviation calculated from the yelds of this portfolio will represent just the not-eliminable risk (the systematic risk), without including the eliminable risk (the specific risk).
The BETA of an asset is calculated as the volatilty of this asset around the volatilty of the market portfolio: being more precise, it is the covariance between the yelds of the current asset and those of the market portfolio all divided by the variance of the yelds of market portfolio.
Covariance is calculated as the product between correlation coefficient, standard deviation of the first dataset and standard deviation of the second asset.
So, as the correlation coefficient and the standard deviation of the yelds of our asset increase (it means that the yelds of our asset are very similiar to those of th market portfolio in terms of sign and intensity and that the volatility of these yelds is quite high), the value of BETA increases as well
According to the Capital Asset Pricing Model (CAPM) promoted by William Sharpe (the guy of the "Sharpe Ratio") and Harry Markowitz, in efficient markets the yeld of an asset can be calculated as the sum between the risk-free interest rate and the risk premium. The risk premium of the specific asset would be the risk premium of the market portfolio multiplied with the value of beta. It is simple: if the volatility of the yelds of an asset around the yelds of market protfolio are particularly high, investors would ask for a higher risk premium that would be translated in a higher yeld.
In this way the expected yeld of an asset would be calculated from the linear expression of the "Security Market Line": r_i = r_f + β*(r_m-r_f)
where:
r_i = expected yeld of the asset
r_f = risk free interest rate
β = beta
r_m = yeld of market portfolio
I know that considering Bitcoin as a proxy of the market portfolio involved in the calculation of Beta would be an inaccuracy since it doesn't have the property of maximum diversification (since it is a single asset), but there's no doubt that it's tying the prices of altcoins (upward and downward) thanks to the relevance of its dominance in the capitalization of cryptocurrency market. So, in the lack of a good index of cryptocurrencies (as the FTSE MIB for the italian stock market), and as long the dominance of Bitcoin will persist with this intensity, we can use Bitcoin as a proxy of the market portfolio
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Consolidation Ranges [kingthies] Consolidation Range Analysis
Published by Eric Thies, January 2021
█ Indicator Summary
This tool calculates, analyzes and plots the visualization of a relative range over a given period of time
By adding to charts, users are enabled to see the impulsive nature of market cycles, along with their efforts to consolidate thereafter
The default period is 30, and should be adjusted to users preference
The default input is the current close price, on the chosen timeframe of the chart
█ Script Source
//
//@version=4
//© kingthies || This source code is subject to the terms of the Mozilla Public License 2.0 at mozilla.org
study("Consolidation Ranges ", shorttitle="CR ", overlay=true)
// !<------ User Inputs ----->
src = input(close, title='Range Input (Default set to Close'), lengthEMA=input(30,title='Length'),zoneToggle = input(true, title="Toggle Zone Highlights"), iCol = color.new(#FFFFFF, 100),
// !<---- Declarations & Calculations ---- >
trndUp = float(na),trndDwn = float(na), mid = float(na), e = ema(src, lengthEMA)
trndUp := src < nz(trndUp ) and src > trndDwn ? nz(trndUp ) : high, trndDwn := src < nz(trndUp ) and src > trndDwn ? nz(trndDwn ) : low, mid := avg(trndUp, trndDwn)
// !< ---- Plotting ----->
highRange = plot(trndUp == nz(trndUp ) ? trndUp : na, color=color.white, linewidth=2, style=plot.style_linebr, title="Top of Period Range")
lowRange = plot(trndDwn == nz(trndDwn ) ? trndDwn : na, color=color.white, linewidth=2, style=plot.style_linebr, title="Bottom of Period Range")
xzone = plot(zoneToggle ? src > e ? trndDwn : trndUp : na, color=iCol, style=plot.style_circles, linewidth=0, editable=false)
fill(highRange, xzone, color=color.lime,transp=70), fill(xzone, lowRange, color=color.red,transp=70)
//
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[fareid] Quick Backtest Framework█ OVERVIEW
This Framework allows Pine Coders to quickly code Study() based signal/strategy and validate its viability before proceed to code with more advance/complex customized rules for entry, exit, trailstop, risk management etc..
This is somewhat an upgraded version of my earlier personal template with different strategy used, cleaner code
and additional features.
█ USE CASES
- You have an idea for trade signal and need a quick way to verify its potential before writing lengthy/complicated code
- You found a study script for trading signal in public library and want to validate it profitability with minimum effort before including it in your trading playbook
█ FEATURES
- Alert: Ready to use alert function based on signals from your custom indicator.
- Visual Backtest: Auto-plot entry, stop-loss and take profit for simple strategy performance analysis
- Backtest Statistic: Provide basic key metrics based on backtest strategy
- BTE External Signal Protocol: Ready to use code that will supply required state to PineCoders Backtesting & Trading Engine if you wish to have more advance and sophisticated backtesting engine
Notes: All of the above features have On/Off toggle
█ Description & How To Use
This Framework consist of 5 Modules but you only need to edit the first 2 Modules:
Module1: Indicator
Module2: Framework Input Protocol
Module3: Alert
Module4: Backtest
Module5: Backtest & Trading Engine
Tips: The source-code includes collapsible block by module for easy navigating
Module1: Indicator:
-----------------------------------------------------------------------------------
Main Module. Place custom indicator input parameter/calculation/indicator plotting here
Sample Strategy: Double MACD Crossover
MACD Signal: 1st MACD Cross above signal line indicate Buy Signal
1st MACD Cross below signal line indicate Sell Signal
MACD Filter: 2nd MACD is above 0 line indicate Uptrend
2nd MACD is below 0 line indicate Downtrend
Module2: Framework Input Protocol:
-----------------------------------------------------------------------------------
Use this module to connect main indicator/signal calculated in Module1 to the rest of the framework's module
4 variables needed to be defined here:
1. Uptrend
2. Dntrend
3. BuySignal
4. SellSignal
i'm not sure how to place a code snippet here to show you example so in the source code i already put a comment in Module2 on which part u need to edit. I hope its pretty simple to use.
Module3: Alert Module Description:
-----------------------------------------------------------------------------------
As long as the variables in Module2 properly defined, the alert module is ready to use without any further modification.
Input:
Enable Alert --> Enable TV's alert and plot signal to chart
Alert Type --> Set to take Buy only, Sell only or Both alert
Module4: Backtest Module Description:
-----------------------------------------------------------------------------------
As long as the variables in Module2 properly defined, the backtest module is ready to use without any further modification.
Input:
Backtest Stat --> Enable Backtest Statistic Label
Backtest Visual --> Enable Backtest visual simulation
Backtest Type --> Set to take Buy only or Sell only or both
SL Type -->
ATR : Set SL in ATR times Multiplier below entry price
Fixed : Set SL in fixed point below entry point (in 'Dollar'). e.g. for Stocks -> 0.5 equals to 50cent while for EURUSD currency -> 0.005 equal to 50 pips
HiLo Bar: Set SL at highest/lowest wick of previous bar plus/minus Fixed point. e.g. EURUSD HiLo=3 and Fixed Point = 0.0005, buy trade will place SL 5 Pips below lowest of previous 3 bar
SL ATR Multi --> Set Lookback Period used for SL's ATR calculation
SL ATR Multi --> Set ATR Multiplier for SL
SL Fixed --> Set Fixed Level for SL
SL Bar --> Set Number of previous bar to check for SL placement
TP RR Ratio --> Set TP based on RR multiplier. e.g. 2 means TP level will be twice further from entry point compared to Entry-SL distance.
Notes: The point is for preliminary testing, so it only supports 1 trade at a time and no Trailing Stop
Module5: Backtest & Trading Engine Description:
-----------------------------------------------------------------------------------
As long as the variables in Module2 properly defined, the Pinecoders BTE module is ready to use without any further modification.
Input:
External Signal Protocol --> Set ESP State to send to "Backtesting & Trading Engine "
Signal With Filter --> Use this to send entry signal that already filtered by this study indicator (without stoploss level)
Signal Without Filter --> Use this to send raw entry signal that are NOT YET FILTERED by this study indicator (without stoploss level)
Signal and Stop With Filter --> Use this to send entry signal WITH StopLoss that already filtered by this study indicator (with stoploss level)
Signal and Stop Without Filter --> Use this to send raw entry signal WITH StopLoss that are NOT YET FILTERED by this study indicator (with stoploss level)
Notes: Backtesting & Trading Engine already have built-in Filter, Entries and Stop Level. e.g. Unselect all their filter state if only want to use custom filter and make sure send Signal with Filter (with or without SL level)
█ DISCLAIMER:
This framework main objective is to create my personal indicator template so that i just have to modify the indicator module for preliminary testing in future.
The sample strategy included are for educational purpose only. Use at your own risk
credit: LucF/PineCoders for a lot of his scripts that i use as a guide to complete this
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Long RSIThe RSI is a technical indicator generally used with the general setting being 14 days, and often shorter.
The accepted view is that a level of 70 indicates overbought conditions, and 30 indicates oversold conditions.
A short RSI setting will give signals quite often, and they might sometimes contradict each other.
As a individual investor, perhaps with a background in fundamental analysis, the RSI might be overlooked for other fundamental metrics.
But the idea here is that longer RSI settings can be used for investing.
The problem that arises is how to know when the indicator has reached a level that is either overbought or oversold.
This script solves that by using a specific look back period (selectable, but the standard is 1 year), and plotting the highest/lowest value that the RSI has had for that time period.
The idea is that a buy signal occurs when the indicator is at a 'historic' low, and a sell signal occurs when it at its 'historic' high.
Since you generally want to buy when the indicator is at its low, and has stopped decreasing, the script comes with a function that shows you when yesterdays value reached a historic low, but todays value is higher than yesterday.
This is shown by a color change of the background to green. The same is true, but opposite, for sell signals and then the background turns red.
THIS IS NOT TRADING ADVICE, AND YOU SHOULD ALWAYS DO YOUR OWN RESEARCH
GOOD LUCK AND HAPPY TRADING Indicator
Fundamental Metric to MarketThis script is supposed to be a quite basic way to find, from a fundamental standpoint, overvalue or undervalued stocks.
The script shows either Book to Market (inverse of P/B), EV /EBITDA, Earnings Yield (inverse of P/E) or Sales to Market (inverse of P/S).
For example, P/B is calculated as Close price / Book Value per share. As a contrarian investor you generally want to buy low P/B stocks and sell high P/B stocks. The problem is when a company has a negative Book Value. This might be the case when a company has written of a large amount of goodwill, which in turn wiped out their Book Value.
Instead you can use Book to Market, which is simply the inverse of P/B. It is calculated as Book Value per share / Close price. When using Book to Market you, generally, want to buy shares with high Book to Market values and sell those with low values. Because of the calculation, companies with negative Book Value will have negative Book to Market values and is therefore easy to identify.
The Fundamental Metric to Market indicator shows a colored background between the highest / lowest point of earlier values and the current value. When the background is mainly green the stock is probably undervalued, and the opposite is true when the background is red.
THIS IS NOT TRADING ADVICE, AND YOU SHOULD ALWAYS DO YOUR OWN RESEARCH
GOOD LUCK AND HAPPY TRADING Indicator
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