[BT] NedDavis Series: CPI Minus 5-Year Moving Average🟧 GENERAL
The script works on the Monthly Timeframe and has 2 main settings (explained in FEATURES ). It uses the US CPI data, reported by the Bureau of Labour Statistics.
🔹Functionality 1: The main idea is to plot the distance between the CPI line and the 5 year moving average of the CPI line. This technique in mathematics is called "deviation from the moving average". This technique is used to analyse how has CPI previously acted and can give clues at what it might do in the future. Economic historians use such analysis, together with specific period analysis to predict potential risks in the future (see an example of such analysis in HOW TO USE section. The mathematical technique is a simple subtraction between 2 points (CPI - 5yr SMA of CPI).
▶︎Interpretation for deviation from a moving average:
Positive Deviation: When the line is above its moving average, it indicates that the current value is higher than the average, suggesting potential strength or bullish sentiment.
Negative Deviation: Conversely, when the line falls below its moving average, it suggests weakness or bearish sentiment as the current value is lower than the average.
▶︎Applications:
Trend Identification: Deviations from moving averages can help identify trends, with sustained deviations indicating strong trends.
Reversal Signals: Significant deviations from moving averages may signal potential trend reversals, especially when combined with other technical indicators.
Volatility Measurement: Monitoring the magnitude of deviations can provide insights into market volatility and price movements.
Remember the indicator is applying this only for the US CPI - not the ticker you apply the indicator on!
🔹Functionality 2: It plots on a new pane below information about the Consumer Price Index. You can also find the information by plotting the ticker symbol USACPIALLMINMEI on TradingView, which is a Monthly economic data by the OECD for the CPI in the US. The only addition you would get from the indicator is the plot of the 5 year Simple Moving Average.
🔹What is the US Consumer Price Index?
Measures the change in the price of goods and services purchased by consumers;
Traders care about the CPI because consumer prices account for a majority of overall inflation. Inflation is important to currency valuation because rising prices lead the central bank to raise interest rates out of respect for their inflation containment mandate;
It is measured as the average price of various goods and services are sampled and then compared to the previous sampling.
Source: Bureau of Labor Statistics;
FEATURES OF INDICATOR
1) The US Consumer Price Index Minus the Five Year Moving Average of the same.
As shown on the picture above and explained in previous section. Here a more detailed view.
2) The actual US Consumer Price Index (Annual Rate of change) and the Five year average of the US Consumer Price Index. Explained above and shown below:
To activate 2) go into settings and toggle the check box.
HOW TO USE
It can be used for a fundamental analysis on the relationship between the stock market, the economy and the Feds decisions to hike or cut rates, whose main mandate is to control inflation over time.
I have created this indicator to show my analysis in this idea:
What does a First Fed Rate cut really mean?
CREDITS
I have seen such idea in the past posted by the institutional grade research of NedDavis and have recreated it for the TradingView platform, open-source for the community.
Davis
[TTI] NDR 63-Day QQQ-QQEW ROC% SpreadWelcome to the NDR 63-Day QQQ-QQEW ROC% Spread script! This script is a powerful tool that calculates and visualizes the 63-day Rate of Change (ROC%) spread between the QQQ and QQEW tickers. This script is based on the research conducted by Ned Davis Research (NDR), a renowned name in the field of investment strategy.
⚙️ Key Features:
👉Rate of Change Calculation: The script calculates the 63-day Rate of Change (ROC%) for both QQQ and QQEW tickers. The ROC% is a momentum oscillator that measures the percentage price change over a given time period.
👉Spread Calculation: The script calculates the spread between the ROC% of QQQ and QQEW. This spread can be used to identify potential trading opportunities.
👉Visual Representation: The script plots the spread on the chart, providing a visual representation of the ROC% spread. This can help traders to easily identify trends and patterns.
👉Warning Lines: The script includes warning lines at +600 and -600 levels. These lines can be used as potential thresholds for trading decisions.
Usage:
To use this script, simply add it to your TradingView chart. The script will automatically calculate the ROC% for QQQ and QQEW and plot the spread on the chart. You can use this information to inform your trading decisions.
🚨 Disclaimer:
This script is provided for educational purposes only and is not intended as investment advice. Trading involves risk and is not suitable for all investors. Please consult with a financial advisor before making any investment decisions.
🎖️ Credits:
This script is based on the research conducted by Ned Davis Research (NDR). All credit for the underlying methodology and concept goes to NDR.
Weighted Harrell-Davis Quantile Estimator with AbsoluteDeviation
QUANTILE ESTIMATORS
Weighted Harrell-Davis Quantile Estimator with Absolute Deviation Fences.
DISCLAIMER:
The Following indicator/code IS NOT intended to be a formal investment advice or recommendation by the author, nor should be construed as such. Users will be fully responsible by their use regarding their own trading vehicles/assets.
The following indicator was made for NON LUCRATIVE ACTIVITIES and must remain as is, following TradingView's regulations. Use of indicator and their code are published for work and knowledge sharing. All access granted over it, their use, copy or re-use should mention authorship(s) and origin(s).
WARNING NOTICE!
THE INCLUDED FUNCTION MUST BE CONSIDERED FOR TESTING. The models included in the indicator have been taken from open sources on the web and some of them has been modified by the author, problems could occur at diverse data sceneries, compiler version, or any other externality.
Purpose:
Weighted Quantiles or <> are quite difficult to find on must systems, also it's non-weighted approach are rarely used to estimate the location parameter of price distribution WICH IS NOT NORMAL, all this in favour of it's non-robust counterpart, the Arithmetic rolling Mean or <> and it's weighted variants like the WMA, VWAP, etc.
Also, a big drawback from this is that must statistics derived from Normal-Distribution parameter location (the Mean) definitely will not fit for an efficient, nor robust estimation for price distributions, so their moments like the standard deviation, kurtosis, skewness, etc. will not be the better tools to build derived algorithms or technical indicators among price/volume.
In an effort searching better statistical tools for price distributions, I found the excellent work of Andrey Akinshin that took me to port some of their Math research contributions for the compute benchmarking field , and bring it here at the TradingView ecosystem to take a shot at the price distribution crazy fields. For a better detail of what the weighted Harrell-Davis Quantile Estimator can do, who better than drink directly from the source at References:
References:
Weighted Quantile Estimators.
DoubleMAD outlier detector based on the Harrell-Davis quantile estimator.
Unbiased median absolute deviation based on the Harrell-Davis quantile estimator.
Quantile confidence intervals for weighted samples.
Licensing:
This work is licensed under a Attribution-NonCommercial-ShareAlike 4.0 International Copyright (c) 2021 (CC BY-NC-SA 4.0)
Copyright's & Mentions:
The Gamma Functions & Beta Probability Density Functions C# implementations by the Math.NET Numerics, part of the Math.NET Project.
The Regularized Incomplete (Left) Beta Function C# implementation by the SAMTools, htslib project.
The Weighted Harrell-Davis Quantile estimator ; C# & R implementations by Andrey Akinshin.
External PineScript code, methods, support & consultancy by @PineCoders staff with special mention for:
+ "ma sorter ('sort by array' example)- JD" by @Duyck.
+ Porting, mods, compilation and debugging for this script by @XeL_Arjona for the TradingView's @PineCoders community.
