Adjust Asset for Future Interest (Brazil)Este script foi criado para ajustar o preço de um ativo com base na taxa de juros DI11!, que reflete a expectativa do mercado para os juros futuros. O objetivo é mostrar como o valor do ativo seria influenciado se fosse diretamente ajustado pela variação dessa taxa de juros.
Como funciona?
Preço do Ativo
O script começa capturando o preço de fechamento do ativo que está sendo visualizado no gráfico. Esse é o ponto de partida para o cálculo.
Taxa de Juros DI11!
Em seguida, ele busca os valores diários da taxa DI11! no mercado. Esta taxa é uma referência de juros de curto prazo, usada para ajustes financeiros e projeções econômicas.
Fator de Ajuste
Com a taxa de juros DI11!, o script calcula um fator de ajuste simples:
Fator de Ajuste
=
1
+
DI11
100
Fator de Ajuste=1+
100
DI11
Esse fator traduz a taxa percentual em um multiplicador aplicado ao preço do ativo.
Cálculo do Ativo Ajustado
Multiplica o preço do ativo pelo fator de ajuste para obter o valor ajustado do ativo. Este cálculo mostra como o preço seria se fosse diretamente influenciado pela variação da taxa DI11!.
Exibição no Gráfico
O script plota o preço ajustado do ativo como uma linha azul no gráfico, com maior espessura para facilitar a visualização. O resultado é uma curva que reflete o impacto teórico da taxa de juros DI11! sobre o ativo.
Utilidade
Este indicador é útil para entender como as taxas de juros podem influenciar ativos financeiros de forma hipotética. Ele é especialmente interessante para analistas que desejam avaliar a relação entre o mercado de renda variável e as condições de juros no curto prazo.
This script was created to adjust the price of an asset based on the DI11! interest rate, which reflects the market's expectation for future interest rates. The goal is to show how the asset's value would be influenced if it were directly adjusted by the variation of this interest rate.
How does it work?
Asset Price
The script starts by capturing the closing price of the asset that is being viewed on the chart. This is the starting point for the calculation.
DI11! Interest Rate
The script then searches for the daily values of the DI11! rate in the market. This rate is a short-term interest reference, used for financial adjustments and economic projections.
Adjustment Factor
With the DI11! interest rate, the script calculates a simple adjustment factor:
Adjustment Factor
=
1
+
DI11
100
Adjustment Factor=1+
100
DI11
This factor translates the percentage rate into a multiplier applied to the asset's price.
Adjusted Asset Calculation
Multiplies the asset price by the adjustment factor to obtain the adjusted asset value. This calculation shows how the price would be if it were directly influenced by the variation of the DI11! rate.
Display on the Chart
The script plots the adjusted asset price as a blue line on the chart, with greater thickness for easier visualization. The result is a curve that reflects the theoretical impact of the DI11! interest rate on the asset.
Usefulness
This indicator is useful for understanding how interest rates can hypothetically influence financial assets. It is especially interesting for analysts who want to assess the relationship between the equity market and short-term interest rate conditions.
Cerca negli script per "curve"
CauchyTrend [InvestorUnknown]The CauchyTrend is an experimental tool that leverages a Cauchy-weighted moving average combined with a modified Supertrend calculation. This unique approach provides traders with insight into trend direction, while also offering an optional ATR-based range analysis to understand how often the market closes within, above, or below a defined volatility band.
Core Concepts
Cauchy Distribution and Gamma Parameter
The Cauchy distribution is a probability distribution known for its heavy tails and lack of a defined mean or variance. It is characterized by two parameters: a location parameter (x0, often 0 in our usage) and a scale parameter (γ, "gamma").
Gamma (γ): Determines the "width" or scale of the distribution. Smaller gamma values produce a distribution more concentrated near the center, giving more weight to recent data points, while larger gamma values spread the weight more evenly across the sample.
In this indicator, gamma influences how much emphasis is placed on values closer to the current price versus those further away in time. This makes the resulting weighted average either more reactive or smoother, depending on gamma’s value.
// Cauchy PDF formula used for weighting:
// f(x; γ) = (1/(π*γ)) *
f_cauchyPDF(offset, gamma) =>
numerator = gamma * gamma
denominator = (offset * offset) + (gamma * gamma)
pdf = (1 / (math.pi * gamma)) * (numerator / denominator)
pdf
A chart showing different Cauchy PDFs with various gamma values, illustrating how gamma affects the weight distribution.
Cauchy-Weighted Moving Average (CWMA)
Using the Cauchy PDF, we calculate normalized weights to create a custom Weighted Moving Average. Each bar in the lookback period receives a weight according to the Cauchy PDF. The result is a Cauchy Weighted Average (cwm_avg) that differs from typical moving averages, potentially offering unique sensitivity to price movements.
// Summation of weighted prices using Cauchy distribution weights
cwm_avg = 0.0
for i = 0 to length - 1
w_norm = array.get(weights, i) / sum_w
cwm_avg += array.get(values, i) * w_norm
Supertrend with a Cauchy Twist
The indicator integrates a modified Supertrend calculation using the cwm_avg as its reference point. The Supertrend logic typically sets upper and lower bands based on volatility (ATR), and flips direction when price crosses these bands.
In this case, the Cauchy-based average replaces the usual baseline, aiming to capture trend direction via a different weighting mechanism.
When price closes above the upper band, the trend is considered bullish; closing below the lower band signals a bearish trend.
ATR Stats Range (Optional)
Beyond the fundamental trend detection, the indicator optionally computes ATR-based stats to understand price distribution relative to a volatility corridor centered on the cwm_avg line:
Volatility Range:
Defined as cwm_avg ± (ATR * atr_mult), this range creates upper and lower bands. Turning on atr_stats computes how often the daily close falls: Within the range, Above the upper ATR boundary, Below the lower ATR boundary, Within the range but above cwm_avg, Within the range but below cwm_avg
These statistics can help traders gauge how the market behaves relative to this volatility envelope and possibly identify if the market tends to revert to the mean or break out more often.
Backtesting and Performance Metrics
The code is integrated with a backtesting library that allows users to assess strategy performance historically:
Equity Curve Calculation: Compares CauchyTrend-based signals against the underlying asset.
Performance Metrics Table: Once enabled, displays key metrics such as mean returns, Sharpe Ratio, Sortino Ratio, and more, comparing the strategy to a simple Buy & Hold approach.
Alerts and Notifications
The indicator provides Alerts for key events:
Long Alert: Triggered when the trend flips bullish.
Short Alert: Triggered when the trend flips bearish.
Customization and Calibration
Important: The default parameters are not optimized for any specific instrument or time frame. Traders should:
Adjust the length and gamma parameters to influence how sharply or broadly the cwm_avg reacts to price changes.
Tune the atr_len and atr_mult for the Supertrend logic to better match the asset’s volatility characteristics.
Experiment with atr_stats on/off to see if that additional volatility distribution information provides helpful insights.
Traders may find certain sets of parameters that align better with their preferred trading style, risk tolerance, or asset volatility profile.
Disclaimer: This indicator is for educational and informational purposes only. Past performance in backtesting does not guarantee future results. Always perform due diligence, and consider consulting a qualified financial advisor before trading.
Volume Weighted Jurik Moving AverageThe Jurik Moving Average (JMA) is a smoothing indicator that is designed to improve upon traditional moving averages by reducing lag while enhancing responsiveness to price movements. It was created by Jurik Research and is often used to track trends with greater accuracy and minimal delay. The JMA is based on a combination of **exponential smoothing** and **phase adjustments**, making it more adaptable to varying market conditions compared to standard moving averages like SMA (Simple Moving Average) or EMA (Exponential Moving Average).
The core advantage of the JMA lies in its ability to adjust to price changes without excessively lagging, which is a common issue with traditional moving averages. It incorporates a **phase parameter** that can be adjusted to smooth out the signal further or make it more responsive to recent price action. This phase adjustment allows traders to fine-tune the JMA's sensitivity to the market, optimizing it for different timeframes and trading strategies.
How JMA Works and Benefits of Adding Volume Weight
The JMA works by applying a **smoothing process** to price data while allowing for adjustments through its phase and power parameters. These parameters help control the degree of smoothness and responsiveness. The result is a curve that follows price trends closely but with less lag than traditional moving averages.
Adding **volume weighting** to the JMA enhances its ability to reflect market activity more accurately. Just like the **Volume-Weighted Moving Average (VWMA)**, volume-weighting adjusts the moving average based on the strength of trading volume, meaning that price movements with higher volume will have a greater influence on the JMA. This can help traders identify trends that are supported by significant market participation, making the moving average more reliable.
The benefit of a volume-weighted JMA is that it responds more effectively to price movements that occur during periods of high trading volume, which are often considered more significant. This can help traders avoid false signals that may occur during low-volume periods when price changes may not reflect true market sentiment. By incorporating volume into the calculation, the JMA becomes more aligned with real market conditions, enhancing its effectiveness for trend identification and decision-making.
MadTrend [InvestorUnknown]The MadTrend indicator is an experimental tool that combines the Median and Median Absolute Deviation (MAD) to generate signals, much like the popular Supertrend indicator. In addition to identifying Long and Short positions, MadTrend introduces RISK-ON and RISK-OFF states for each trade direction, providing traders with nuanced insights into market conditions.
Core Concepts
Median and Median Absolute Deviation (MAD)
Median: The middle value in a sorted list of numbers, offering a robust measure of central tendency less affected by outliers.
Median Absolute Deviation (MAD): Measures the average distance between each data point and the median, providing a robust estimation of volatility.
Supertrend-like Functionality
MadTrend utilizes the median and MAD in a manner similar to how Supertrend uses averages and volatility measures to determine trend direction and potential reversal points.
RISK-ON and RISK-OFF States
RISK-ON: Indicates favorable conditions for entering or holding a position in the current trend direction.
RISK-OFF: Suggests caution, signaling RISK-ON end and potential trend weakening or reversal.
Calculating MAD
The mad function calculates the median of the absolute deviations from the median, providing a robust measure of volatility.
// Function to calculate the Median Absolute Deviation (MAD)
mad(series float src, simple int length) =>
med = ta.median(src, length) // Calculate median
abs_deviations = math.abs(src - med) // Calculate absolute deviations from median
ta.median(abs_deviations, length) // Return the median of the absolute deviations
MADTrend Function
The MADTrend function calculates the median and MAD-based upper (med_p) and lower (med_m) bands. It determines the trend direction based on price crossing these bands.
MADTrend(series float src, simple int length, simple float mad_mult) =>
// Calculate MAD (volatility measure)
mad_value = mad(close, length)
// Calculate the MAD-based moving average by scaling the price data with MAD
median = ta.median(close, length)
med_p = median + (mad_value * mad_mult)
med_m = median - (mad_value * mad_mult)
var direction = 0
if ta.crossover(src, med_p)
direction := 1
else if ta.crossunder(src, med_m)
direction := -1
Trend Direction and Signals
Long Position (direction = 1): When the price crosses above the upper MAD band (med_p).
Short Position (direction = -1): When the price crosses below the lower MAD band (med_m).
RISK-ON: When the price moves further in the direction of the trend (beyond median +- MAD) after the initial signal.
RISK-OFF: When the price retraces towards the median, signaling potential weakening of the trend.
RISK-ON and RISK-OFF States
RISK-ON LONG: Price moves above the upper band after a Long signal, indicating strengthening bullish momentum.
RISK-OFF LONG: Price falls back below the upper band, suggesting potential weakness in the bullish trend.
RISK-ON SHORT: Price moves below the lower band after a Short signal, indicating strengthening bearish momentum.
RISK-OFF SHORT: Price rises back above the lower band, suggesting potential weakness in the bearish trend.
Picture below show example RISK-ON periods which can be identified by “cloud”
Note: Highlighted areas on the chart indicating RISK-ON and RISK-OFF periods for both Long and Short positions.
Implementation Details
Inputs and Parameters:
Source (input_src): The price data used for calculations (e.g., close, open, high, low).
Median Length (length): The number of periods over which the median and MAD are calculated.
MAD Multiplier (mad_mult): Determines the distance of the upper and lower bands from the median.
Calculations:
Median and MAD are recalculated each period based on the specified length.
Upper (med_p) and Lower (med_m) Bands are computed by adding and subtracting the scaled MAD from the median.
Visual representation of the indicator on a price chart:
Backtesting and Performance Metrics
The MadTrend indicator includes a Backtesting Mode with a performance metrics table to evaluate its effectiveness compared to a simple buy-and-hold strategy.
Equity Calculation:
Calculates the equity curve based on the signals generated by the indicator.
Performance Metrics:
Metrics such as Mean Returns, Standard Deviation, Sharpe Ratio, Sortino Ratio, and Omega Ratio are computed.
The metrics are displayed in a table for both the strategy and the buy-and-hold approach.
Note: Due to the use of labels and plot shapes, automatic chart scaling may not function ideally in Backtest Mode.
Alerts and Notifications
MadTrend provides alert conditions to notify traders of significant events:
Trend Change Alerts
RISK-ON and RISK-OFF Alerts - Provides real-time notifications about the RISK-ON and RISK-OFF states for proactive trade management.
Customization and Calibration
Default Settings: The provided default settings are experimental and not optimized. They serve as a starting point for users.
Parameter Adjustment: Traders are encouraged to calibrate the indicator's parameters (e.g., length, mad_mult) to suit their specific trading style and the characteristics of the asset being analyzed.
Source Input: The indicator allows for different price inputs (open, high, low, close, etc.), offering flexibility in how the median and MAD are calculated.
Important Notes
Market Conditions: The effectiveness of the MadTrend indicator can vary across different market conditions. Regular calibration is recommended.
Backtest Limitations: Backtesting results are historical and do not guarantee future performance.
Risk Management: Always apply sound risk management practices when using any trading indicator.
Honest Volatility Grid [Honestcowboy]The Honest Volatility Grid is an attempt at creating a robust grid trading strategy but without standard levels.
Normal grid systems use price levels like 1.01;1.02;1.03;1.04... and place an order at each of these levels. In this program instead we create a grid using keltner channels using a long term moving average.
🟦 IS THIS EVEN USEFUL?
The idea is to have a more fluid style of trading where levels expand and follow price and do not stick to precreated levels. This however also makes each closed trade different instead of using fixed take profit levels. In this strategy a take profit level can even be a loss. It is useful as a strategy because it works in a different way than most strategies, making it a good tool to diversify a portfolio of trading strategies.
🟦 STRATEGY
There are 10 levels below the moving average and 10 above the moving average. For each side of the moving average the strategy uses 1 to 3 orders maximum (3 shorts at top, 3 longs at bottom). For instance you buy at level 2 below moving average and you increase position size when level 6 is reached (a cheaper price) in order to spread risks.
By default the strategy exits all trades when the moving average is reached, this makes it a mean reversion strategy. It is specifically designed for the forex market as these in my experience exhibit a lot of ranging behaviour on all the timeframes below daily.
There is also a stop loss at the outer band by default, in case price moves too far from the mean.
What are the risks?
In case price decides to stay below the moving average and never reaches the outer band one trade can create a very substantial loss, as the bands will keep following price and are not at a fixed level.
Explanation of default parameters
By default the strategy uses a starting capital of 25000$, this is realistic for retail traders.
Lot sizes at each level are set to minimum lot size 0.01, there is no reason for the default to be risky, if you want to risk more or increase equity curve increase the number at your own risk.
Slippage set to 20 points: that's a normal 2 pip slippage you will find on brokers.
Fill limit assumtion 20 points: so it takes 2 pips to confirm a fill, normal forex spread.
Commission is set to 0.00005 per contract: this means that for each contract traded there is a 5$ or whatever base currency pair has as commission. The number is set to 0.00005 because pinescript does not know that 1 contract is 100000 units. So we divide the number by 100000 to get a realistic commission.
The script will also multiply lot size by 100000 because pinescript does not know that lots are 100000 units in forex.
Extra safety limit
Normally the script uses strategy.exit() to exit trades at TP or SL. But because these are created 1 bar after a limit or stop order is filled in pinescript. There are strategy.orders set at the outer boundaries of the script to hedge against that risk. These get deleted bar after the first order is filled. Purely to counteract news bars or huge spikes in price messing up backtest.
🟦 VISUAL GOODIES
I've added a market profile feature to the edge of the grid. This so you can see in which grid zone market has been the most over X bars in the past. Some traders may wish to only turn on the strategy whenever the market profile displays specific characteristics (ranging market for instance).
These simply count how many times a high, low, or close price has been in each zone for X bars in the past. it's these purple boxes at the right side of the chart.
🟦 Script can be fully automated to MT5
There are risk settings in lot sizes or % for alerts and symbol settings provided at the bottom of the indicator. The script will send alert to MT5 broker trying to mimic the execution that happens on tradingview. There are always delays when using a bridge to MT5 broker and there could be errors so be mindful of that. This script sends alerts in format so they can be read by tradingview.to which is a bridge between the platforms.
Use the all alert function calls feature when setting up alerts and make sure you provide the right webhook if you want to use this approach.
Almost every setting in this indicator has a tooltip added to it. So if any setting is not clear hover over the (?) icon on the right of the setting.
Equilibrium Candles + Pattern [Honestcowboy]The Equilibrium Candles is a very simple trend continuation or reversal strategy depending on your settings.
How an Equilibrium Candle is created:
We calculate the equilibrium by measuring the mid point between highest and lowest point over X amount of bars back.
This now is the opening price for each bar and will be considered a green bar if price closes above equilibrium.
Bars get shaded by checking if regular candle close is higher than open etc. So you still see what the normal candles are doing.
Why are they useful?
The equilibrium is calculated the same as Baseline in Ichimoku Cloud. Which provides a point where price is very likely to retrace to. This script visualises the distance between close and equilibrium using candles. To provide a clear visual of how price relates to this equilibrium point.
This also makes it more straightforward to develop strategies based on this simple concept and makes the trader purely focus on this relationship and not think of any Ichimoku Cloud theories.
Script uses a very simple pattern to enter trades:
It will count how many candles have been one directional (above or below equilibrium)
Based on user input after X candles (7 by default) script shows we are in a trend (bg colors)
On the first pullback (candle closes on other side of equilibrium) it will look to enter a trade.
Places a stop order at the high of the candle if bullish trend or reverse if bearish trend.
If based on user input after X opposite candles (2 by default) order is not filled will cancel it and look for a new trend.
Use Reverse Logic:
There is a use reverse logic in the settings which on default is turned on. It will turn long orders into short orders making the stop orders become limit orders. It will use the normal long SL as target for the short. And TP as stop for the short. This to provide a means to reverse equity curve in case your pair is mean reverting by nature instead of trending.
ATR Calculation:
Averaged ATR, which is using ta.percentile_nearest_rank of 60% of a normal ATR (14 period) over the last 200 bars. This in simple words finds a value slightly above the mean ATR value over that period.
Big Candle Exit Logic:
Using Averaged ATR the script will check if a candle closes X times that ATR from the equilibrium point. This is then considered an overextension and all trades are closed.
This is also based on user input.
Simple trade management logic:
Checks if the user has selected to use TP and SL, or/and big candle exit.
Places a TP and SL based on averaged ATR at a multiplier based on user Input.
Closes trade if there is a Big Candle Exit or an opposite direction signal from indicator.
Script can be fully automated to MT5
There are risk settings in % and symbol settings provided at the bottom of the indicator. The script will send alert to MT5 broker trying to mimic the execution that happens on tradingview. There are always delays when using a bridge to MT5 broker and there could be errors so be mindful of that. This script sends alerts in format so they can be read by tradingview.to which is a bridge between the platforms.
Use the all alert function calls feature when setting up alerts and make sure you provide the right webhook if you want to use this approach.
There is also a simple buy and sell alert feature if you don't want to fully automate but still get alerts. These are available in the dropdown when creating an alert.
Almost every setting in this indicator has a tooltip added to it. So if any setting is not clear hover over the (?) icon on the right of the setting.
The backtest uses a 4% exposure per trade and a 10 point slippage. I did not include a commission cause I'm not personaly aware what the commissions are on most forex brokers. I'm only aware of minimal slippage to use in a backtest. Trading conditions vary per broker you use so always pay close attention to trading costs on your own broker. Use a full automation at your own risk and discretion and do proper backtesting.
Advanced Economic Indicator by USCG_VetAdvanced Economic Indicator by USCG_Vet
tldr:
This comprehensive TradingView indicator combines multiple economic and financial metrics into a single, customizable composite index. By integrating key indicators such as the yield spread, commodity ratios, stock indices, and the Federal Reserve's QE/QT activities, it provides a holistic view of the economic landscape. Users can adjust the components and their weights to tailor the indicator to their analysis, aiding in forecasting economic conditions and market trends.
Detailed Description
Overview
The Advanced Economic Indicator is designed to provide traders and investors with a powerful tool to assess the overall economic environment. By aggregating a diverse set of economic indicators and financial market data into a single composite index, it helps identify potential turning points in the economy and financial markets.
Key Features:
Comprehensive Coverage: Includes 14 critical economic and financial indicators.
Customizable Components: Users can select which indicators to include.
Adjustable Weights: Assign weights to each component based on perceived significance.
Visual Signals: Clear plotting with threshold lines and background highlights.
Alerts: Set up alerts for when the composite index crosses user-defined thresholds.
Included Indicators
Yield Spread (10-Year Treasury Yield minus 3-Month Treasury Yield)
Copper/Gold Ratio
High Yield Spread (HYG/IEF Ratio)
Stock Market Performance (S&P 500 Index - SPX)
Bitcoin Performance (BLX)
Crude Oil Prices (CL1!)
Volatility Index (VIX)
U.S. Dollar Index (DXY)
Inflation Expectations (TIP ETF)
Consumer Confidence (XLY ETF)
Housing Market Index (XHB)
Manufacturing PMI (XLI ETF)
Unemployment Rate (Inverse SPY as Proxy)
Federal Reserve QE/QT Activities (Fed Balance Sheet - WALCL)
How to Use the Indicator
Configuring the Indicator:
Open Settings: Click on the gear icon (⚙️) next to the indicator's name.
Inputs Tab: You'll find a list of all components with checkboxes and weight inputs.
Including/Excluding Components
Checkboxes: Check or uncheck the box next to each component to include or exclude it from the composite index.
Default State: By default, all components are included.
Adjusting Component Weights:
Weight Inputs: Next to each component's checkbox is a weight input field.
Default Weights: Pre-assigned based on economic significance but fully adjustable.
Custom Weights: Enter your desired weight for each component to reflect your analysis.
Threshold Settings:
Bearish Threshold: Default is -1.0. Adjust to set the level below which the indicator signals potential economic downturns.
Bullish Threshold: Default is 1.0. Adjust to set the level above which the indicator signals potential economic upswings.
Setting the Timeframe:
Weekly Timeframe Recommended: Due to the inclusion of the Fed's balance sheet data (updated weekly), it's best to use this indicator on a weekly chart.
Changing Timeframe: Select 1W (weekly) from the timeframe options at the top of the chart.
Interpreting the Indicator:
Composite Index Line
Plot: The blue line represents the composite economic indicator.
Movement: Observe how the line moves relative to the threshold lines.
Threshold Lines
Zero Line (Gray Dotted): Indicates the neutral point.
Bearish Threshold (Red Dashed): Crossing below suggests potential economic weakness.
Bullish Threshold (Green Dashed): Crossing above suggests potential economic strength.
Background Highlights
Red Background: When the composite index is below the bearish threshold.
Green Background: When the composite index is above the bullish threshold.
No Color: When the composite index is between the thresholds.
Understanding the Components
1. Yield Spread
Description: The difference between the 10-year and 3-month U.S. Treasury yields.
Economic Significance: An inverted yield curve (negative spread) has historically preceded recessions.
2. Copper/Gold Ratio
Description: The price ratio of copper to gold.
Economic Significance: Copper is tied to industrial demand; gold is a safe-haven asset. The ratio indicates risk sentiment.
3. High Yield Spread (HYG/IEF Ratio)
Description: Ratio of high-yield corporate bonds (HYG) to intermediate-term Treasury bonds (IEF).
Economic Significance: Reflects investor appetite for risk; widening spreads can signal credit stress.
4. Stock Market Performance (SPX)
Description: S&P 500 Index levels.
Economic Significance: Broad measure of U.S. equity market performance.
5. Bitcoin Performance (BLX)
Description: Bitcoin Liquid Index price.
Economic Significance: Represents risk appetite in speculative assets.
6. Crude Oil Prices (CL1!)
Description: Front-month crude oil futures price.
Economic Significance: Influences inflation and consumer spending.
7. Volatility Index (VIX)
Description: Market's expectation of volatility (fear gauge).
Economic Significance: High VIX indicates market uncertainty; inverted in the indicator to align directionally.
8. U.S. Dollar Index (DXY)
Description: Value of the U.S. dollar relative to a basket of foreign currencies.
Economic Significance: Affects international trade and commodity prices; inverted in the indicator.
9. Inflation Expectations (TIP ETF)
Description: iShares TIPS Bond ETF prices.
Economic Significance: Reflects market expectations of inflation.
10. Consumer Confidence (XLY ETF)
Description: Consumer Discretionary Select Sector SPDR Fund prices.
Economic Significance: Proxy for consumer confidence and spending.
11. Housing Market Index (XHB)
Description: SPDR S&P Homebuilders ETF prices.
Economic Significance: Indicator of the housing market's health.
12. Manufacturing PMI (XLI ETF)
Description: Industrial Select Sector SPDR Fund prices.
Economic Significance: Proxy for manufacturing activity.
13. Unemployment Rate (Inverse SPY as Proxy)
Description: Inverse of the SPY ETF price.
Economic Significance: Represents unemployment trends; higher inverse SPY suggests higher unemployment.
14. Federal Reserve QE/QT Activities (Fed Balance Sheet - WALCL)
Description: Total assets held by the Federal Reserve.
Economic Significance: Indicates liquidity injections (QE) or withdrawals (QT); impacts interest rates and asset prices.
Customization and Advanced Usage
Adjusting Weights:
Purpose: Emphasize components you believe are more predictive or relevant.
Method: Increase or decrease the weight value next to each component.
Example: If you think the yield spread is particularly important, you might assign it a higher weight.
Disclaimer
This indicator is for educational and informational purposes only. It is not financial advice. Trading and investing involve risks, including possible loss of principal. Always conduct your own analysis and consult with a professional financial advisor before making investment decisions.
Hyperbolic Tangent Volatility Stop [InvestorUnknown]The Hyperbolic Tangent Volatility Stop (HTVS) is an advanced technical analysis tool that combines the smoothing capabilities of the Hyperbolic Tangent Moving Average (HTMA) with a volatility-based stop mechanism. This indicator is designed to identify trends and reversals while accounting for market volatility.
Hyperbolic Tangent Moving Average (HTMA):
The HTMA is at the heart of the HTVS. This custom moving average uses a hyperbolic tangent transformation to smooth out price fluctuations, focusing on significant trends while ignoring minor noise. The transformation reduces the sensitivity to sharp price movements, providing a clearer view of the underlying market direction.
The hyperbolic tangent function (tanh) is commonly used in mathematical fields like calculus, machine learning and signal processing due to its properties of “squashing” inputs into a range between -1 and 1. The function provides a non-linear transformation that can reduce the impact of extreme values while retaining a certain level of smoothness.
tanh(x) =>
e_x = math.exp(x)
e_neg_x = math.exp(-x)
(e_x - e_neg_x) / (e_x + e_neg_x)
The HTMA is calculated by applying a non-linear transformation to the difference between the source price and its simple moving average, then adjusting it using the standard deviation of the price data. The result is a moving average that better tracks the real market direction.
htma(src, len, mul) =>
tanh_src = tanh((src - ta.sma(src, len)) * mul) * ta.stdev(src, len) + ta.sma(src, len)
htma = ta.sma(tanh_src, len)
Important Note: The Hyperbolic Tangent function becomes less accurate with very high prices. For assets priced above 100,000, the results may deteriorate, and for prices exceeding 1 million, the function may stop functioning properly. Therefore, this indicator is better suited for assets with lower prices or lower price ratios.
Volatility Stop (VolStop):
HTVS employs a Volatility Stop mechanism based on the Average True Range (ATR). This stop dynamically adjusts based on market volatility, ensuring that the indicator adapts to changing conditions and avoids false signals in choppy markets.
The VolStop follows the price, with a higher ATR pushing the stop farther away to avoid premature exits during volatile periods. Conversely, when volatility is low, the stop tightens to lock in profits as the trend progresses.
The ATR Length and ATR Multiplier are customizable, allowing traders to control how tightly or loosely the stop follows the price.
pine_volStop(src, atrlen, atrfactor) =>
if not na(src)
var max = src
var min = src
var uptrend = true
var float stop = na
atrM = nz(ta.atr(atrlen) * atrfactor, ta.tr)
max := math.max(max, src)
min := math.min(min, src)
stop := nz(uptrend ? math.max(stop, max - atrM) : math.min(stop, min + atrM), src)
uptrend := src - stop >= 0.0
if uptrend != nz(uptrend , true)
max := src
min := src
stop := uptrend ? max - atrM : min + atrM
Backtest Mode:
HTVS includes a built-in backtest mode, allowing traders to evaluate the indicator's performance on historical data. In backtest mode, it calculates the cumulative equity curve and compares it to a simple buy and hold strategy.
Backtesting features can be adjusted to focus on specific signal types, such as Long Only, Short Only, or Long & Short.
An optional Buy and Hold Equity plot provides insight into how the indicator performs relative to simply holding the asset over time.
The indicator includes a Hints Table, which provides useful recommendations on how to best display the indicator for different use cases. For example, when using the overlay mode, it suggests displaying the indicator in the same pane as price action, while backtest mode is recommended to be used in a separate pane for better clarity.
The Hyperbolic Tangent Volatility Stop offers traders a balanced approach to trend-following, using the robustness of the HTMA for smoothing and the adaptability of the Volatility Stop to avoid whipsaw trades during volatile periods. With its backtesting features and alert system, this indicator provides a comprehensive toolkit for active traders.
Uptrick: Market MoodsThe "Uptrick: Market Moods" indicator is an advanced technical analysis tool designed for the TradingView platform. It combines three powerful indicators—Relative Strength Index (RSI), Average True Range (ATR), and Bollinger Bands—into one cohesive framework, aimed at helping traders better understand and interpret market sentiment. By capturing shifts in the emotional climate of the market, it provides a holistic view of market conditions, which can range from calm to stressed or even highly excited. This multi-dimensional analysis tool stands apart from traditional single-indicator approaches by offering a more complete picture of market dynamics, making it a valuable resource for traders looking to anticipate and react to changes in market behavior.
The RSI in the "Uptrick: Market Moods" indicator is used to measure momentum. RSI is an essential component of many technical analysis strategies, and in this tool, it is used to identify potential market extremes. When RSI values are high, they indicate an overbought condition, meaning the market may be approaching a peak. Conversely, low RSI values suggest an oversold condition, signaling that the market could be nearing a bottom. These extremes provide crucial clues about shifts in market sentiment, helping traders gauge whether the current emotional state of the market is likely to result in a reversal. This understanding is pivotal in predicting whether the market is transitioning from calm to stressed or from excited to overbought.
The Average True Range adds another layer to this analysis by offering insights into market volatility. Volatility is a key factor in understanding the mood of the market, as periods of high volatility often reflect high levels of excitement or stress, while low volatility typically indicates a calm, steady market. ATR is calculated based on the range of price movements over a given period, and the higher the value, the more volatile the market is. The "Uptrick: Market Moods" indicator uses ATR to dynamically gauge volatility levels, helping traders understand whether the market is currently moving in a way that aligns with its emotional mood. For example, an increase in ATR accompanied by an RSI value that indicates overbought conditions could suggest that the market is in a highly excited state, with the potential for either strong momentum continuation or a sharp reversal.
Bollinger Bands complement these tools by providing visual cues about price volatility and the range within which the market is likely to move. Bollinger Bands plot two standard deviations away from a simple moving average of the price. This banding technique helps traders visualize how far the price is likely to deviate from its average over a certain period. The "Uptrick: Market Moods" indicator uses Bollinger Bands to establish price boundaries and identify breakout conditions. When prices break above the upper band or below the lower band, it often signals that the market is either highly stressed or excited. This breakout condition serves as a visual representation of the market mood, alerting traders to moments when prices are moving beyond typical ranges and when significant emotional shifts are occurring in the market.
Technically, the "Uptrick: Market Moods" indicator has been developed using TradingView’s Pine Script language, a highly efficient language for building custom indicators. It employs functions like ta.rsi, ta.atr, and ta.sma to perform the necessary calculations. The use of these built-in functions ensures that the calculations are both accurate and efficient, allowing the indicator to operate in real-time without lagging, even in volatile market conditions. The ta.rsi function is used to compute the Relative Strength Index, while ta.atr calculates the Average True Range, and ta.sma is used to smooth out price data for the Bollinger Bands. These functions are applied dynamically within the script, allowing the "Uptrick: Market Moods" indicator to respond to changes in market conditions in real time.
The user interface of the "Uptrick: Market Moods" indicator is designed to provide a visually intuitive experience. The market mood is color-coded on the chart, making it easy for traders to identify whether the market is calm, stressed, or excited at a glance. This feature is especially useful for traders who need to make quick decisions in fast-moving markets. Additionally, the indicator includes an interactive table that updates in real-time, showing the most recent mood state and its frequency. This provides valuable statistical insights into market behavior over specific time frames, helping traders track the dominant emotional state of the market. Whether the market is in a prolonged calm state or rapidly transitioning through moods, this real-time feedback offers actionable data that can help traders adjust their strategies accordingly.
The RSI component of the "Uptrick: Market Moods" indicator helps detect the speed and direction of price movements, offering insight into whether the market is approaching extreme conditions. By providing signals based on overbought and oversold levels, the RSI helps traders decide whether to enter or exit positions. The ATR element acts as a volatility gauge, dynamically adjusting traders’ expectations in response to changes in market volatility. Meanwhile, the Bollinger Bands help identify trends and potential breakout conditions, serving as an additional confirmation tool that highlights when the price has moved beyond normal boundaries, indicating heightened market excitement or stress.
Despite the robust capabilities of the "Uptrick: Market Moods" indicator, it does have limitations. In markets affected by sudden shifts, such as those driven by major news events or external economic factors, the indicator’s performance may not always be reliable. These external factors can cause rapid mood swings that are difficult for any technical analysis tool to fully anticipate. Additionally, the indicator’s complexity may pose a learning curve for novice traders, particularly those who are unfamiliar with the concepts of RSI, ATR, and Bollinger Bands. However, with practice, traders can become proficient in using the tool to its full potential, leveraging the insights it provides to better navigate market shifts.
For traders seeking a deeper understanding of market sentiment, the "Uptrick: Market Moods" indicator is an invaluable resource. It is recommended for those dealing with medium to high volatility instruments, where understanding emotional shifts can offer a strategic advantage. While it can be used on its own, integrating it with other forms of analysis, such as fundamental analysis and additional technical indicators, can enhance its effectiveness. By confirming signals with other tools, traders can reduce the likelihood of false signals and improve their overall trading strategy.
To further enhance the accuracy of the "Uptrick: Market Moods" indicator, it can be integrated with volume-based tools like Volume Profile or On-Balance Volume (OBV). This combination allows traders to confirm the moods identified by the indicator with volume data, providing additional confirmation of market sentiment. For example, when the market is in an excited mood, an increase in trading volume could reinforce the reliability of that signal. Conversely, if the market is stressed but volume remains low, traders may want to proceed with caution. Using multiple indicators together creates a more comprehensive trading approach, helping traders better manage risk and make informed decisions based on multiple data points.
In conclusion, the "Uptrick: Market Moods" indicator is a powerful and unique addition to the suite of technical analysis tools available on TradingView. It provides traders with a multi-dimensional view of market sentiment by combining the analytical strengths of RSI, ATR, and Bollinger Bands into a single tool. Its ability to capture and interpret the emotional mood of the market makes it an essential tool for traders seeking to gain an edge in understanding market behavior. While the indicator has certain limitations, particularly in rapidly shifting markets, its ability to provide real-time insights into market sentiment is a valuable asset for traders of all experience levels. Used in conjunction with other tools and sound trading practices, the "Uptrick: Market Moods" indicator offers a comprehensive solution for navigating the complexities of financial markets.
Power MarketPower Market Indicator
Description: The Power Market Indicator is designed to help traders assess market strength and make informed decisions for entering and exiting positions. This innovative indicator provides a comprehensive view of the evolution of Simple Moving Averages (SMA) over different periods and offers a clear measure of market strength through a total score.
Key Features:
Multi-Period SMA Analysis:
Calculates Simple Moving Averages (SMA) for 10 different periods ranging from 10 to 100.
Provides detailed analysis by comparing the current closing price with these SMAs.
Market Strength Measurement:
Assesses market strength by calculating a total score based on the relationship between the closing price and the SMAs.
The total score is displayed as a histogram with distinct colors for positive and negative values.
Smoothed Curve for Better View:
A smoothing of the total score is applied using a 5-period Simple Moving Average to represent the overall trend more smoothly.
Dynamic Information Table:
Real-time display of the maximum and minimum values among the SMAs, as well as the difference between these values, providing valuable insights into the variability of moving averages.
Visual Reference Lines:
Horizontal lines at zero, +50, and -50 for easy evaluation of key score levels.
How to Use the Indicator:
Position Entries: Use high positive scores to identify buying opportunities when market strength is strong.
Position Exits: Negative scores may signal market weakness, allowing you to exit positions or wait for a better opportunity.
Data Analysis: The table helps you understand the variability of SMAs, offering additional context for your trading decisions.
This powerful tool provides an in-depth view of market dynamics and helps you navigate your trading strategies with greater confidence. Embrace the Power Market Indicator and optimize your trading decisions today!
Auto Signal Buy/SellAuto Signal Buy/Sell with Time Filter and Dynamic ZLEMA (GMT+2) 🌟
Are you looking for an indicator that combines efficiency and simplicity while integrating advanced elements like SuperTrend, ZLEMA (Zero Lag EMA), and a MACD DEMA for clear and precise buy/sell signals? 📈 Introducing Auto Signal Buy/Sell, the ultimate indicator designed for intraday and swing traders, optimized for market hours in GMT+2.
🛠️ Key Features:
- **Advanced SuperTrend**: Follow the dominant trend with a robust SuperTrend, adjustable to your preferences (customizable multiplier and period).
- **Dynamic ZLEMA**: Get a zero-lag EMA curve with a visual signal. Additionally, the ZLEMA turns blue when it’s nearly flat, helping you easily spot market consolidation phases.
- **MACD DEMA**: An enhanced version of the traditional MACD, using the Double EMA to capture more responsive buy/sell cross signals. 📊
- **Buy/Sell Signals**: Visual arrows clearly indicate potential entry and exit points on your chart, filtered by MACD crossovers and the SuperTrend trend.
- **Smart Time Filter (GMT+2)**: This script adapts to trading hours (customizable) and only displays signals during trading hours. The background turns light blue when the market is closed, preventing confusion during inactivity periods. 🕒
⚙️ Full Customization:
- Adjustable trading hours (default 9 AM to 5 PM in GMT+2) with dynamic background indicating when markets are closed.
- Flexible settings for SuperTrend, ZLEMA, and MACD DEMA to suit any strategy.
🎯 Why Choose This Indicator?
- Optimized for maximum precision with advanced algorithms like ZLEMA and DEMA.
- Easy to use: it provides clear, visual signals directly on the chart—no need to decipher complex indicators.
- A complete intraday and swing indicator that combines trend analysis and signal filtering with precise market hours.
🚀 Boost Your Trading!
Add this indicator to your toolkit and enhance your decision-making. Thanks to its intuitive interface and clear visual signals, you can trade with confidence. 💡
Don't forget to like 👍 and comment if you find this indicator useful! Your feedback helps us continue improving such tools. 🚀
📌 How to Use:
1. Add the indicator to your chart.
2. Adjust the SuperTrend and ZLEMA settings to suit your needs.
3. Follow the buy/sell signals and watch for the light blue background outside of trading hours.
4. Trade effectively and stay in control, even during consolidation phases.
Dynamic Volume RSI (DVRSI) [QuantAlgo]Introducing the Dynamic Volume RSI (DVRSI) by QuantAlgo 📈✨
Elevate your trading and investing strategies with the Dynamic Volume RSI (DVRSI) , a powerful tool designed to provide clear insights into market momentum and trend shifts. This indicator is ideal for traders and investors who want to stay ahead of the curve by using volume-responsive calculations and adaptive smoothing techniques to enhance signal clarity and reliability.
🌟 Key Features:
🛠 Customizable RSI Settings: Tailor the indicator to your strategy by adjusting the RSI length and price source. Whether you’re focused on short-term trades or long-term investments, DVRSI adapts to your needs.
🌊 Adaptive Smoothing: Enable adaptive smoothing to filter out market noise and ensure cleaner signals in volatile or choppy market conditions.
🎨 Dynamic Color-Coding: Easily identify bullish and bearish trends with color-coded candles and RSI plots, offering clear visual cues to track market direction.
⚖️ Volume-Responsive Adjustments: The DVRSI reacts to volume changes, giving greater significance to high-volume price moves and improving the accuracy of trend detection.
🔔 Custom Alerts: Stay informed with alerts for key RSI crossovers and trend changes, allowing you to act quickly on emerging opportunities.
📈 How to Use:
✅ Add the Indicator: Set up the DVRSI by adding it to your chart and customizing the RSI length, price source, and smoothing options to fit your specific strategy.
👀 Monitor Visual Cues: Watch for trend shifts through the color-coded plot and candles, signaling changes in momentum as the RSI crosses key levels.
🔔 Set Alerts: Configure alerts for critical RSI crossovers, such as the 50 line, ensuring you stay on top of potential market reversals and opportunities.
🔍 How It Works:
The Dynamic Volume RSI (DVRSI) is a unique indicator designed to provide more accurate and responsive signals by incorporating both price movement and volume sensitivity into the RSI framework. It begins by calculating the traditional RSI values based on a user-defined length and price source, but unlike standard RSI tools, the DVRSI applies volume-weighted adjustments to reflect the strength of market participation.
The indicator dynamically adjusts its sensitivity by factoring in volume to the RSI calculation, which means that price moves backed by higher volumes carry more weight, making the signal more reliable. This method helps identify stronger trends and reduces the risk of false signals in low-volume environments. To further enhance accuracy, the DVRSI offers an adaptive smoothing option that allows users to reduce noise during periods of market volatility. This adaptive smoothing function responds to market conditions, providing a cleaner signal by reducing erratic movements or price spikes that could lead to misleading signals.
Additionally, the DVRSI uses dynamic color-coding to visually represent the strength of bullish or bearish trends. The candles and RSI plots change color based on the RSI values crossing critical thresholds, such as the 50 level, offering an intuitive way to recognize trend shifts. Traders can also configure alerts for specific RSI crossovers (e.g., above 50 or below 40), ensuring that they stay informed of potential trend reversals and significant market shifts in real-time.
The combination of volume sensitivity, adaptive smoothing, and dynamic trend visualization makes the DVRSI a robust and versatile tool for traders and investors looking to fine-tune their market analysis. By incorporating both price and volume data, this indicator delivers more precise signals, helping users make informed decisions with greater confidence.
Disclaimer:
The Dynamic Volume RSI is designed to enhance your market analysis but should not be used as a sole decision-making tool. Always consider multiple factors before making any trading or investment decisions. Past performance is not indicative of future results.
Dynamic Jurik RSX w/ Fisher Transform█ Introduction
The Dynamic Jurik RSX with Fisher Transform is a powerful and adaptive momentum indicator designed for traders who seek a non-laggy view of price movements. This script is based on the classic Jurik RSX (Relative Strength Index). It also includes features such as the dynamic overbought and oversold limits, the Inverse Fisher Transform, trend display, slope calculations, and the ability to color extremes for better clarity.
█ Key Features:
• RSX: The Relative Strength Index (RSX) in this script is based on Jurik’s RSX, which is smoother than the traditional RSI and aims to reduce noise and lag. This script calculates the RSX using an exponential smoothing technique and adaptive adjustments.
• Inverse Fisher Transform: This script can optionally apply the Inverse Fisher Transform to the RSX, which helps to normalize the RSX values, compressing them between -1 and 1. The inverse transformation makes it easier to spot extreme values (overbought and oversold conditions) by enhancing the visual clarity of those extremes. It also smooths the curve over a user-defined period in hopes of providing a more consistent signal.
• Dynamic Limits: The dynamic overbought and oversold limits are calculated based on the RSX's recent high and low values. The limits adjust dynamically depending on market conditions, making them more relevant to current price action.
• Slope Display: The slope of the RSX is calculated as the rate of change between the current and previous RSX value. The slope is displayed as dots when the slope exceeds the threshold designated by the user, providing visual cues for momentum shifts.
• Trend Coloring: Optionally, the user can also enable a trend-based display. It is simply based on current value of RSX versus the previous one. If RSX is rising then the trend is bullish, if not, then the trend is bearish.
• Coloring Extremes: Users can configure the RSX to color the chart when prices enter extreme conditions, such as overbought or oversold zones, providing visual cues for market reversals.
█ Attached Chart Notes:
• Top Panel: Enabled dynamic limits, Trend display, standard Jurik RSX with 20 lookback period, and Slope display.
• Middle Panel: Enabled dynamic limits, Extremes display, and standard Jurik RSX with 20 lookback period.
• Bottom Panel: Enabled dynamic limits, Trend display, Inverse Fisher Transform with 14 lookback period and 9 smoothing period. and Slope display.
█ Credits:
Special thanks to Everget for providing the original script. The script was also slightly modified based on updates from outside sources.
█ Disclaimer:
This script is for educational purposes only and should not be considered financial advice. Always conduct your own research and consult a professional before making any trading decisions.
Gaussian Acceleration ArrayIndicators play a role in analyzing price action, trends, and potential reversals. Among many of these, velocity and acceleration have held a significant place due to their ability to provide insight into momentum and rate of change. This indicator takes the old calculation and tweaks it with gaussian smoothing and logarithmic function to ensure proper scaling.
A Brief on Velocity and Acceleration: The concept of velocity in trading refers to the speed at which price changes over time, while acceleration is the rate of change(ROC) of velocity. Early momentum indicators like the RSI and MACD laid foundation for understanding price velocity. However, as markets evolve so do we as technical analysts, we seek the most advanced tools.
The Acceleration/Deceleration Oscillator, introduced by Bill Williams, was one of the early attempts to measure acceleration. It helped gauge whether the market was gaining or losing momentum. Over time more specific tools like the "Awesome Oscillator"(AO) emerged, which has a set length on the datasets measured.
Gaussian Functions: Named after the mathematician Carl Friedrich Gauss, the Gaussian function describes a bell-shaped curve, often referred to as the "normal distribution." In trading these functions are applied to smooth data and reduce noise, focusing on underlying patterns.
The Gaussian Acceleration Array leverages this function to create a smoothed representation of market acceleration.
How does it work?
This indicator calculates acceleration based the highs and lows of each dataset
Once the weighted average for velocity is determined, its rate of change essentially becomes the acceleration
It then plots multiple lines with customizable variance from the primary selected length
Practical Tips:
The Gaussian Acceleration Array offers various customizable parameters, including the sample period, smoothing function, and array variance. Experiment with these settings to tailor it to preferred timeframes and styles.
The color-coded lines and background zones make it easier to interpret the indicator at a glance. The backgrounds indicate increasing or decreasing momentum simply as a visual aid while the lines state how the velocity average is performing. Combining this with other tools can signal shifts in market dynamics.
Gaussian Kernel Smoothing EMAGaussian Kernel Smoothing EMA
The Gaussian Kernel Smoothing EMA integrates the exponential moving average with kernel smoothing techniques to refine the trend tool. Kernel smoothing is a non-parametric technique used to estimate a smooth curve from a set of data points. It is particularly useful in reducing noise and capturing the underlying structure of data. The smoothed value at each point is calculated as a weighted average of neighboring points, with the weights determined by a kernel function.
The Gaussian kernel is a popular choice in kernel smoothing due to its properties of being smooth, symmetric, and having infinite support. This function gives higher weights to data points closer to the target point and lower weights to those further away, resulting in a smooth and continuous estimate. Since price isn't normally distributed a logarithmic transformation is performed to remove most of its skewness to be able to fit the Gaussian kernel.
This indicator also has a bandwidth, which in kernel smoothing controls the width of the window over which the smoothing is performed. It determines how much influence nearby data points have on the smoothed value. In this indicator, the bandwidth is dynamically adjusted based on the standard deviation of the log-transformed prices so that the smoothing adapts to the underlying variability and potential volatility.
Bandwidth Factor: The bandwidth factor in this indicator is used to adjust the degree of the smoothing applied to the MA. In kernel smoothing, Bandwidth controls the width of the window over which the smoothing is applied. It determines how many data points around a central point are considered when calculating a smooth value. A smaller bandwidth results in less smoothing, while a larger bandwidth smooths out more noise, leading to a broader, more general trend.
Moving Average Z-Score Suite [BackQuant]Moving Average Z-Score Suite
1. What is this indicator
The Moving Average Z-Score Suite is a versatile indicator designed to help traders identify and capitalize on market trends by utilizing a variety of moving averages. This indicator transforms selected moving averages into a Z-Score oscillator, providing clear signals for potential buy and sell opportunities. The indicator includes options to choose from eleven different moving average types, each offering unique benefits and characteristics. It also provides additional features such as standard deviation levels, extreme levels, and divergence detection, enhancing its utility in various market conditions.
2. What is a Z-Score
A Z-Score is a statistical measurement that describes a value's relationship to the mean of a group of values. It is measured in terms of standard deviations from the mean. For instance, a Z-Score of 1.0 means the value is one standard deviation above the mean, while a Z-Score of -1.0 indicates it is one standard deviation below the mean. In the context of financial markets, Z-Scores can be used to identify overbought or oversold conditions by determining how far a particular value (such as a moving average) deviates from its historical mean.
3. What moving averages can be used
The Moving Average Z-Score Suite allows users to select from the following eleven moving averages:
Simple Moving Average (SMA)
Hull Moving Average (HMA)
Exponential Moving Average (EMA)
Weighted Moving Average (WMA)
Double Exponential Moving Average (DEMA)
Running Moving Average (RMA)
Linear Regression Curve (LINREG) (This script can be found standalone )
Triple Exponential Moving Average (TEMA)
Arnaud Legoux Moving Average (ALMA)
Kalman Hull Moving Average (KHMA)
T3 Moving Average
Each of these moving averages has distinct properties and reacts differently to price changes, allowing traders to select the one that best fits their trading style and market conditions.
4. Why Turning a Moving Average into a Z-Score is Innovative and Its Benefits
Transforming a moving average into a Z-Score is an innovative approach because it normalizes the moving average values, making them more comparable across different periods and instruments. This normalization process helps in identifying extreme price movements and mean-reversion opportunities more effectively. By converting the moving average into a Z-Score, traders can better gauge the relative strength or weakness of a trend and detect potential reversals. This method enhances the traditional moving average analysis by adding a statistical perspective, providing clearer and more objective trading signals.
5. How It Can Be Used in the Context of a Trading System
In a trading system, it can be used to generate buy and sell signals based on the Z-Score values. When the Z-Score crosses above zero, it indicates a potential buying opportunity, suggesting that the price is above its mean and possibly trending upward. Conversely, a Z-Score crossing below zero signals a potential selling opportunity, indicating that the price is below its mean and might be trending downward. Additionally, the indicator's ability to show standard deviation levels and extreme levels helps traders set profit targets and stop-loss levels, improving risk management and trade planning.
6. How It Can Be Used for Trend Following
For trend-following strategies, it can be particularly useful. The Z-Score oscillator helps traders identify the strength and direction of a trend. By monitoring the Z-Score and its rate of change, traders can confirm the persistence of a trend and make informed decisions to enter or exit trades. The indicator's divergence detection feature further enhances trend-following by identifying potential reversals before they occur, allowing traders to capitalize on trend shifts. By providing a clear and quantifiable measure of trend strength, this indicator supports disciplined and systematic trend-following strategies.
No backtests for this indicator due to the many options and ways it can be used,
Enjoy
FX Index Curve Oscillator (FICO)We can approximate the TVC:DXY with simple multiplication, rather than using geometric weighted averages; the values will be different, but the charts will look almost the same. Because we can make a "good enough" version of DXY, we can also extend this concept to the other major currencies:
AUD - Yellow
CAD - Red
CHF - Orange
EUR - Purple
GBP - Green
JPY - White
NZD - Lime green
USD - Blue
This indicator works by constructing an "index" for each currency, performing a lookback to figure out the rate of change, and then smoothing the values. These values are fed through an oscillator to normalize them between -1.00 and +1.00, before finally being smoothed again. Interestingly, using HMA to smooth them the second time will cause the values to leak past 1.00, which we can also use as a signal.
If you want to change the values, I find that the biggest difference comes from the lookback and oscillator settings; the MA/smoothing is probably good enough. The default settings are for doing forex trades on the daily chart. Other timeframes are possible, but I could not find any settings that work. It might also be possible to use a similar approach on other assets (crypto, metals, indexes, etc) but I have not tried yet.
In my own testing, what I found to be a good approach is to look for a currency to be above +1 and another to be below -1, and then look for color changes; ideally this will happen on the same bar/candle.
You can also consider two line crosses, breaking above or below 1, etc as other entry signals. I find that price will either move immediately, or take a candle or two to retrace and then start moving.
Happy trading!
Unfortunately, the indicator pane can get quite crowded; if you're testing for a single currency pair, you may want to disable some of the plotted lines:
MarketcapDefinition
This indicator was designed to reveal the relationship between the price of the product and its market value. The red average marketcap line that appears on the chart is the line. And the further up this line moves from the chart, the more it shows that there is a mismatch between the price and the market value. So what does this incompatibility mean? There are purchases of the product, but since the supply of the product into circulation is constantly increasing, it means that these purchases are not reflected in the price, which means there is inflation.
The main purpose of our indicator is to calculate inflation of the product. It is the understanding of whether or not the amount of supply put into circulation in response to the investment is reflected in the product price while increasing the market value.
Attention: Transactions are made based on the data received via CRYPTOCAP. In cases where this data cannot be received, the "UNSUPPORTED SOURCE" warning is displayed. You can use Settings to change the source from which data can be retrieved.
Labels
The labels are explained one by one below.
MARKETCAP: Shows the current market value.
ATH MARKETCAP: Shows the highest market value of all time.
MARKETCAP RATIO: It gives the ratio between the highest level and the lowest level of the market value.
PRICE RATIO: Gives the ratio between the highest level and the lowest level of the crypto price.
ALL INFLATION PERCENT: It refers to the percentage of all inflation that has developed so far. It is also the percentage difference between market value and price.
MONTHLY INFLATION PERCENT: It refers to the monthly estimated inflation percentage.
CIRCULATING SUPPLY: It refers to the estimated circulation supply of the product.
Best Use
It should bring to mind the idea that the further the indicator curve moves away from the price, the higher the inflation will be. In order for a product to reach its previous peak, its market value must normally increase by the "MARKETCAP RATIO" value and the "PRICE RATIO" value. This should make you think that this product needs more investment to reach its former peak. And it is necessary to be careful when purchasing such products.
Disclaimer
This indicator is for informational purposes only and should be used for educational purposes only. You may lose money if you rely on this to trade without additional information. Use at your own risk.
Version
v1.0
MathTransformLibrary "MathTransform"
Auxiliary functions for transforming data using mathematical and statistical methods
scaler_zscore(x, lookback_window)
Calculates Z-Score normalization of a series.
Parameters:
x (float) : : floating point series to normalize
lookback_window (int) : : lookback period for calculating mean and standard deviation
Returns: Z-Score normalized series
scaler_min_max(x, lookback_window, min_val, max_val, empiric_min, empiric_max, empiric_mid)
Performs Min-Max scaling of a series within a given window, user-defined bounds, and optional midpoint
Parameters:
x (float) : : floating point series to transform
lookback_window (int) : : int : optional lookback window size to consider for scaling.
min_val (float) : : float : minimum value of the scaled range. Default is 0.0.
max_val (float) : : float : maximum value of the scaled range. Default is 1.0.
empiric_min (float) : : float : user-defined minimum value of the input data. This means that the output could exceed the `min_val` bound if there is data in `x` lesser than `empiric_min`. If na, it's calculated from `x` and `lookback_window`.
empiric_max (float) : : float : user-defined maximum value of the input data. This means that the output could exceed the `max_val` bound if there is data in `x` greater than `empiric_max`. If na, it's calculated from `x` and `lookback_window`.
empiric_mid (float) : : float : user-defined midpoint value of the input data. If na, it's calculated from `empiric_min` and `empiric_max`.
Returns: rescaled series
log(x, base)
Applies logarithmic transformation to a value, base can be user-defined.
Parameters:
x (float) : : floating point value to transform
base (float) : : logarithmic base, must be greater than 0
Returns: logarithm of the value to the given base, if x <= 0, returns logarithm of 1 to the given base
exp(x, base)
Applies exponential transformation to a value, base can be user-defined.
Parameters:
x (float) : : floating point value to transform
base (float) : : base of the exponentiation, must be greater than 0
Returns: the result of raising the base to the power of the value
power(x, exponent)
Applies power transformation to a value, exponent can be user-defined.
Parameters:
x (float) : : floating point value to transform
exponent (float) : : exponent for the transformation
Returns: the value raised to the given exponent, preserving the sign of the original value
tanh(x, scale)
The hyperbolic tangent is the ratio of the hyperbolic sine and hyperbolic cosine. It limits an output to a range of −1 to 1.
Parameters:
x (float) : : floating point series
scale (float)
sigmoid(x, scale, offset)
Applies the sigmoid function to a series.
Parameters:
x (float) : : floating point series to transform
scale (float) : : scaling factor for the sigmoid function
offset (float) : : offset for the sigmoid function
Returns: transformed series using the sigmoid function
sigmoid_double(x, scale, offset)
Applies a double sigmoid function to a series, handling positive and negative values differently.
Parameters:
x (float) : : floating point series to transform
scale (float) : : scaling factor for the sigmoid function
offset (float) : : offset for the sigmoid function
Returns: transformed series using the double sigmoid function
logistic_decay(a, b, c, t)
Calculates logistic decay based on given parameters.
Parameters:
a (float) : : parameter affecting the steepness of the curve
b (float) : : parameter affecting the direction of the decay
c (float) : : the upper bound of the function's output
t (float) : : time variable
Returns: value of the logistic decay function at time t
Linear Regression Channel Slow And Fast (Multi time frame)Linear Regression Channels are useful measure for technical and quantitative analysis in financial markets that help identifying trends and trend direction. The use of standard deviation gives traders ideas as to when prices are becoming overbought or oversold relative to the long term trend
The basis of a linear regression channel
Linear Regression Line – is a line drawn according to the least-squares statistical technique which produces a best-fit line that cuts through the middle of price action, a line that best fits all the data points of interest. The resulting fitted model can be used to summarize the data, to predict unobserved values from the same system. Linear Regression Line then present basis for the channel calculations
The linear regression channel
2. Upper Channel Line – A line that runs parallel to the Linear Regression Line and is usually one to two standard deviations above the Linear Regression Line.
3. Lower Channel Line – This line runs parallel to the Linear Regression Line and is usually one to two standard deviations below the Linear Regression Line.
Unlike Fibonacci Channels and Andrew’s Pitchfork, Linear Regression Channels are calculated using statistical methods, both for the regression line (as expressed above) and deviation channels. Upper and Lower channel lines are presenting the idea of bell curve method, also known as a normal distribution and are calculated using standard deviation function.
A standard deviation include 68% of the data points, two standard deviations include approximately 95% of the data points and any data point that appears outside two standard deviations is very rare.
It is often assumed that the data points will move back toward the average, or regress and channels would allow us to see when a security is overbought or oversold and ready to revert to the mean
please note : Over time, the price will move up and down, and the linear regression channel will experience changes as old prices fall off and new prices appear
Papercuts Recency CandlesPapercuts Recency Candles
V0.8 by Joel Eckert @PapercutsTrading
***This is currently an experimental visual exploratory concept.***
*** Experimental tools should only be explored by fellow coders and experienced traders.***
DESCRIPTION:
As coders, how can we seamlessly transition between actual and smoothed price data sets as data ages?
This is a visual experiment to see if and how data can be smoothly transitioned from one value to another over a set number of candles. If we visualize a chart in 3 zones, a head, a body, and a tail we can start to understand how this could work. The head zone would represent the first data set of actual asset prices. The body zone would represent the transition period from the first to the to the second data set. Last, the tail zone would represent the second data set made of a Hull Moving Average of the asset.
CONCEPT:
It is conceived that data and position precision constantly shift as they decay or age, therefore making older price levels act more like price regions or zones vs exact price points. This is what I am calling Recency.
This indicator utilizes the concept of "Recency" to explore the possibility of a new style of candle. It aims to maintain accurately on recent prices action but loosen up accuracy on older price action. The very nature of this requires ALTERING HISTORICAL DATA within the body zone or transition candles to achieve the effect. It is similar to trying to merge a line chart type with a candle chart type.
This experiment of using recency for candles was to create candles that stay more accurate near current price but fade away into a simple line as they age out, resulting in a simplified view of the big picture which consists of older price action.
This experimental design theoretically will help you stay focused only on what is currently unfolding and to minimize distractions from older price nuances.
USAGE:
WHO:
This is not recommended for new traders or novices that are unfamiliar with standard tools. Standardized tools should always be used to get grounded and build a foundation.
Active traders who are familiar with trading comfortably should experiment with this to see if they find it interesting or usable.
Pine coders may find this concept interesting enough, and may adapt the idea to other elements of their own scripts if they find it interesting… I just ask they give credit where credit is due.
HOW:
The best way to visualize how this works is to do the following:
Load it on a chart.
Turn off Standard candles in Chart Setting of the current window. I actually just turn off the bodies and borders, and dim the old wicks as I like the way the old wicks look when left alone with these new candles.
Enable chart replay at a faster speed, like 3x, and play back the chart to watch the behavior of the candles.
You’ll be able to see how the head of the candle type preserves OHLC, and indicates direction but as the candle starts to age it progressively flowers into the HMA
While it plays back try adjusting settings to see how they affect behavior.
You can see the data average in real-time which often reveals how unstable actual price noise really is.
The head candle diagonals indicate the candle body direction.
SETTINGS:
Coloring: You can choose your own bullish or bearish colors to match your scheme.
Price Line: The price line is colored according to the trend and
Head Length: These candles are true to the source high and low. They remain slightly brighter than transition candles. We have a max of 50 to keep things responsive.
Time Decay Length: This is the amount of candles it takes to transition to the tail. Max is 300 to keep things responsive.
Decay Continuity: This forces transition candles to complete the HMA curve instead of creating gaps when conforming to it. The best way to visualize this feature is to run a 3x replay of an asset, and toggle the result on and off. On is preferred.
Tail HMA Length: This is the smoothing amount for the resulting HMA stepline that calculates every close, but has a delayed draw until after the transition candles. You can optionally turn off the delayed visibility to help with comprehension.
Tail HMA Weight: This is simply an option to make the tail thicker or thinner. This also adjusts the border on the head candles to help them stand out.
Show Side Bias Dots: Default true: Draws a dot when bias to one side changes to help keep you on the right side of trade. Side bias is simply the alignment of 3 moving averages in one direction.
IMPORTANT NOTES:
You'll have to turn off or dim the standard candles in your view "Chart Settings" to see this properly.
Be aware that since the candles are based on boxes and utilize the “recency concept”, which means their data decays and changes as it ages. This results in a cleaner chart overall, but exact highs and lows will be averaged out as the data decays, forming a Hull Moving Average stepline of your defined length once decay has finished.
SUMMARY OF HOW IT WORKS:
First it takes candle information and creates unique boxes that represent each candle based on the high and low. It utilizes boxes because standard candles once written, cannot be later altered or removed… which is a key element for this effect to work.
Next it creates a second box and line from open to close for the body of the Head candles. This indicates direction at a glance.
As candles age beyond the defined distance of the “Head” they enter the "Body" aka "Time Decay" zone. Here the accuracy of the high and low will be averaged down using an incremental factor of the HMA, defined by "Time Decay Length" amount of candles.
The resulting tail is an HMA of Tail HMA Length. This tail is always calculate at close, but is not drawn instantly. The draw is delayed so that there is not overlapping data, and this makes the effect look more elegant.
There are also two EMAs within the script that do nothing but help candle coloring and help provide a trade side bias. When both EMA's and the HMA align, a side bias is defined. Only when the side bias changes will a new dot is formed.
Head candles have been simplified from previous versions to be easier to read at a a glance.
Turn of the Month Strategy [Honestcowboy]The end of month effect is a well known trading strategy in the stock market. Quite simply, most stocks go up at the end of the month. What's even better is that this effect spills over to the next phew days of the next month.
In this script we backtest this theory which should work especially well on SP500 pair.
By default the strategy buys 2 days before the end of each month and exits the position 3 days into the next month.
The strategy is a long only strategy and is extremely simple. The SP500 is one of the #1 assets people use for long term investing due to it's "9.8%" annualised return. However as a trader you want the best deal possible. This strategy is only inside the market for about 25% of the time while delivering a similar return per exposure with a lower drawdown.
Here are some hypothesis why turn of the month effect happens in the stock markets:
Increased inflow from savings accounts to stocks at end of month
Rebalancing of portfolios by fund managers at end of month
The timing of monthly cash flows received by pension funds, which are reinvested in the stock market.
The script also has some inputs to define how many days before end of the month you want to buy the asset and how long you want to hold it into the next month.
It is not possible to buy the asset exactly on this day every month as the market closes on the weekend. I've added some logic where it will check if that day is a friday, saturdady or sunday. If that is the case it will send the buy signal on the end of thursday, this way we enter on the friday and don't lose that months trading opportunity.
The backtest below uses 4% exposure per trade as to show the equity curve more clearly and because of publishing rules. However, most fund managers and investors use 100% exposure. This way you actually risk money to earn money. Feel free to adjust the settings to your risk profile to get a clearer picture of risks and rewards before implementing in your portfolio.
Crypto Realized Profits/Losses Extremes [AlgoAlpha]🌟🚀 Introducing the Crypto Realized Profits/Losses Extremes Indicator by AlgoAlpha 🚀🌟
Unlock the potential of cryptocurrency markets with our cutting-edge On-Chain Pine Script™ indicator, designed to highlight extreme realized profit and loss zones! 🎯📈
Key Features:
✨ Realized Profits/Losses Calculation: Uses real-time data from the blockchain to monitor profit and loss realization events.
📊 Multi-Crypto Compatibility: The Indicator is compatible on other Crypto tickers besides Bitcoin.
⚙️ Customizable Sensitivity: Adjust the look-back period, normalization period, and deviation thresholds to tailor the indicator to your trading style.
🎨 Visual Enhancements: Choose from a variety of colors for up and down trends, and toggle extreme profit/loss overlay for easy viewing.
🔔 Integrated Alerts: Set up alerts for high and extreme profit or loss conditions, helping you stay ahead of significant market movements.
🔍 How to Use:
🛠 Add the Indicator: Add the indicator to favorites. Customize settings like period lengths and deviation thresholds according to your needs.
📊 Market Analysis: Monitor the main oscillator and the bands to understand current profit and loss extremes in the market. When the oscillator is at the upper band, this means that the market is doing really well and traders/investors will be likely to take profit and cause a reversal. The opposite is true when the oscillator reaches the lower band. The main oscillator can also be used for trend analysis.
🔔 Set Alerts: Configure alerts to notify you when the market enters a zone of high profit or loss, or during trend changes, enabling timely decisions without constant monitoring.
How It Works:
The indicator calculates a normalized area under the RSI curve applied on on-chain data regarding the number of wallets in profit. It employs a custom "src" variable that aggregates data from the blockchain about profit and loss addresses, adapting to intraday or longer timeframes as needed. The main oscillator plots this normalized area, while the upper and lower bands are plotted based on a deviation metric to identify extreme conditions. Colored fills between these bands visually denote these zones. For interaction, the indicator plots bubbles for extreme profits or losses and provides optional bar coloring to reflect the current market trend.
🚀💹 Enjoy a comprehensive, customizable, and visually engaging tool that helps you stay ahead in the fast-paced crypto market!
