Blockunity US Market Liquidity (BML)Get a clear view of US market liquidity and monitor its status at a glance to anticipate movements on risky assets.
The Idea
The BML aggregates and analyzes total USD market liquidity in trillions of dollars. It is used to monitor the liquidity of the USD market. When liquidity is good, all is well. If liquidity is low, the US will maneuver and sell treasury bills (debt) to replenish its treasury, which can lead to bearish pressure on markets, particularly those considered risky, such as Bitcoin.
How to Use
The indicator is very easy to use, there's nothing special about it. This tool is mainly intended to be used as fundamental information, and not for active trading.
Elements
The US Market Liquidity has several distinct components:
FED Balance Sheet
The Fed credits member banks’ Fed accounts with money, and in return, banks sell the Fed US Treasuries and/or US Mortgage-Backed Securities. This is how the Fed “prints” money to juice the financial system.
US Treasury General Account
The US Treasury General Account (TGA) balances with the NY Fed. When it decreases, it means the US Treasury is injecting money into the economy directly and creating activity. When it increases, it means the US Treasury is saving money and not stimulating economic activity. The TGA also increases when the Treasury sells bonds. This action removes liquidity from the market as buyers must pay for their bonds with dollars.
Overnight Reverse Repurchase Agreements
A reverse repurchase agreement (known as Reverse Repo or RRP) is a transaction in which the New York Fed under the authorization and direction of the Federal Open Market Committee sells a security to an eligible counterparty with an agreement to repurchase that same security at a specified price at a specific time in the future.
Earnings Remittances Due to the Treasury
The Federal Reserve Banks remit residual net earnings to the US Treasury after providing for the costs of operations, payment of dividends, and the amount necessary to maintain each Federal Reserve Bank’s allotted surplus cap. Positive amounts represent the estimated weekly remittances due to the US Treasury. Negative amounts represent the cumulative deferred asset position, which is incurred during a period when earnings are not sufficient to provide for the cost of operations, payment of dividends, and maintaining surplus.
Settings
Several parameters can be defined in the indicator configuration. You can:
Choose the smoothing and timeframe to be used in the plot.
Set the EMA lookback period and display it or not. This affects the color of the main plot.
Set the period to be taken into account when calculating the variation rate in the table.
Select the data to be taken into account in the calculation.
Activate or not the barcolor.
Lastly, you can modify all table parameters.
Statistics
Fundamental Analysis [TrendX_]__________xXx__________ INTRODUCTION __________xXx__________
Fundamental Analysis indicator employs a two-pronged approach to estimate the fair value of a security. This utilizes both relative valuation and intrinsic valuation methods, aiming to achieve a comprehensive understanding of the company's worth.
__________xXx__________ FEATURES AND USAGES __________xXx__________
1 - RELATIVE VALUATION:
Relative valuation takes a company's average financial ratios over a specific number of periods into account.
Price-to-Earnings Ratio (PE Ratio): This metric compares the company's current stock price to its earnings per share. A higher PE ratio indicates investors are willing to pay more for each dollar of earnings, potentially suggesting a growth expectation.
Price-to-Book Ratio (PB Ratio): This metric compares the company's current stock price to its book value per share. A higher PB ratio suggests the market values the company's assets more highly than their accounting book value.
Modified-PE-PB-Growth: This is the modified version for the PE and PB forward. Apply the company's average historical ROE growth rate to PE ratio. Similarly, apply the company's projected ROA growth rate to the industry average PB ratio to arrive at an adjusted PB ratio.
Enterprise Value (EV)/Earnings Before Interest, Taxes, Depreciation, and Amortization (EBITDA) Multiple: This metric compares the company's enterprise value (market capitalization + debt - cash) to its EBITDA. It provides a valuation measure that considers the company's capital structure.
2 - INTRINSIC VALUATION:
Intrinsic valuation attempts to estimate the inherent value of a company based on its future cash flow generation potential. This approach focuses on the company's long-term fundamentals rather than its current market price.
Discounted Cash Flow (DCF): This method discounts the company's projected future free cash flows to their present value. It requires forecasting future cash flows, a discount rate, and a terminal growth rate. The present value of these future cash flows represents the company's intrinsic value.
Dividend Discount Model (DDM): This method assumes the company's value is based on its ability to distribute future dividends to shareholders. It discounts the company's expected future dividends to their present value, providing another estimate of intrinsic value.
Graham Number: Developed by Benjamin Graham, this method utilizes a formula based on a company's earnings per share and book value per share to estimate its intrinsic value. The number 22.5, embedded within this formula, serves as a normalization factor, embodying an ‘ideal’ PE of 15 and PB of 1.5. This approach provides a conservative estimate of a company’s intrinsic value, offering a safety margin for investors.
Net-Nets: Net-Nets refer to micro-to-small companies trading at a price less than 67% of their net current asset value, which is calculated by subtracting current liabilities from current assets. This conservative approach, deeply rooted in the principles of value investing, essentially implies that these companies are undervalued to the extent that their market price is less than their liquidation value.
*** The color of each valuation toolkit’s background is determined UNDERVALUE (above current price) in Turquoise Green color and OVERVALUE (below acceptable rate) in Pink color.
3 - FINANCIAL METRICS
The financial metrics will provide a holistic view of company's financial health, efficiency, risk profile, and growth prospects
Efficiency Metrics:
Net Margin: This metric measures the percentage of each dollar of revenue remaining as profit after accounting for all operating expenses. A higher net margin indicates a company's efficiency in converting sales into profit.
Dividend Yield: This metric represents the annual dividend payment per share divided by the current stock price. It reflects the portion of a company's earnings distributed to shareholders as dividends. A higher dividend yield suggests a focus on shareholder returns.
Fraud Detection Metrics:
Beneish M-score (M-score): This metric is a statistical model used to identify potential accounting manipulations. A higher M-score indicates a greater likelihood of fraudulent activity. It's crucial to analyze the M-score along with other financial information.
Profitability and Growth Metrics:
Piotroski F-score (F-score): This metric assesses a company's financial health and profitability based on nine criteria. A higher F-score suggests a more robust and potentially higher-growth company.
Quick Ratio: This metric measures a company's ability to meet its short-term obligations (due within a year) using its most liquid assets (cash and equivalents, marketable securities, and accounts receivable). A higher quick ratio indicates a stronger short-term liquidity position.
Inventory Ratio: This metric measures how long it takes a company to sell its inventory on average. A lower inventory ratio suggests efficient inventory management and potentially lower holding costs.
Risk Metrics:
Risk-Free Rate (Risk-Free): This metric represents the theoretical rate of return on a risk-free investment, often approximated by the 10-year Treasury Constant Maturity Rate. It serves as a benchmark for evaluating the return required for riskier assets like stocks.
Beta: This metric measures a stock's volatility relative to the overall market (often represented by its market index). A beta of 1 indicates the stock's price movement mirrors the market. A beta greater than 1 suggests the stock is more volatile than the market, and vice versa.
Growth Metrics:
Capital Asset Pricing Model (CAPM): This model estimates the expected return on a stock based on its beta, the risk-free rate, and the market risk premium. CAPM helps determine if a stock is potentially overvalued or undervalued.
Weighted Average Cost of Capital (WACC): This metric represents the average cost of capital a company uses to finance its operations (equity and debt). A lower WACC suggests a company can access capital at a cheaper rate, potentially leading to higher profitability.
Compound Annual Growth Rate (CAGR): This metric calculates the average annual growth rate of a stock price over a specific period. It provides an indication of the historical price appreciation.
Additional:
Sustainable Growth Rate (Growth const.): This metric estimates the maximum long-term growth rate a company can sustain based on its internal resources (retained earnings) and industry growth.
Value at Risk (VaR): This metric estimates the maximum potential loss a stock price might experience over a given timeframe with a certain confidence level. It helps assess the downside risk associated with an investment.
*** The color of each metric’s background is determined above acceptable rate in Turquoise Green color and below acceptable rate in Pink color
__________xXx__________ CONCLUSION__________xXx__________
Fundamental analysis plays a critical role in empowering both investors and traders to navigate the dynamic stock markets. By delving deeper into a company's underlying financial health, future prospects, and competitive landscape, this approach fosters informed decision-making that leads to risk reduction and profit optimization. The Fundamental Analysis can serve as a cornerstone for investors and traders alike, offering a myriad of benefits.
For investors, it is instrumental in risk reduction, as it enables the assessment of a company’s fair value through financial statements, competitive advantages, and growth potential. This critical evaluation aids in avoiding overvalued stocks and spotting undervalued opportunities. Moreover, it fosters a long-term focus, steering investors towards decisions that reflect a company’s long-term prospects, thus supporting a buy-and-hold strategy that resonates with enduring investment objectives. Additionally, a profound comprehension of a company’s fundamentals bolsters investor confidence, ensuring that investment choices are grounded in solid data rather than speculative market noise.
Traders, on the other hand, can leverage fundamental analysis to pinpoint short-term opportunities by staying abreast of a company’s imminent catalysts such as financial health, efficiency, risk profile, or growth prospects. This knowledge allows them to anticipate market movements and seize fleeting chances for profit. It also provides informed insights for establishing entry and exit points, identifying companies poised for robust growth or those facing potential downturns, which is crucial for strategizing trades, including short selling. Importantly, by concentrating on fundamental data, traders can mitigate emotional decision-making, fostering a disciplined approach to trading that curtails the risks associated with impulsive, emotion-driven errors.
__________xXx__________ DISCLAIMER__________xXx__________
Past performance is not necessarily indicative of future results. Numerous factors and inherent uncertainties can influence the outcome of any endeavor, and predicting future events with certainty is impossible.
Trading and Investing inherently carries risk, and the majority of traders experience losses. This indicator is provided solely for informational and educational purposes and does not constitute financial advice.
Therefore, always exercise caution and independent judgment when making investment decisions based on any form of past performance analysis, including this indicator's results.
RWEDT Weighted Moving Average Overview:
The RWEDT MA, which is short for rolling, weighted, exponential, double exponential, and triple exponential, is a group of moving averages that were subjected to a log transformation to deal with the skewness of price, and the weight of each of these moving averages was also used for calculating the standard deviations from the mean.
Clearing a misunderstanding on Standard Deviation Bands and Moving Averages
Bands, such as standard deviation bands, are frequently misinterpreted as indicators of support and resistance levels or as "mean-reverting" indicators." However, this is not their intended purpose. Bands are statistical tools that provide ranges within which price (in this case) movements are expected to occur based on historical data. Deviations beyond these bands suggest a decrease in confidence in the model rather than a reversal back to a moving average or a "support/resistance level."
Example : Assuming you correctly applied a log transformation to your standard deviation bands to remove the right skew, and assuming your data closely resembles a normal distribution or some other type of symmetrical distribution, then the probability of a value being in the 2 standard deviation range is around 95%. This does not mean it will reject or go up, or mean revert. The price won't bounce from -2 STDEV 95% of the time; that is incorrect. It just tells you that around 95% of the values will be within the 2 SD range.
Moving averages, including the ones in this indicator, are often misinterpreted as signals of trend reversals or levels of "bouncing." What moving averages actually tell you is what the expected value is. It does not show where you expect the price to be in the future; it tells you that based on the lookback, the expected value is in the center, and the confidence you have in the estimate is the confidence interval or the standard deviation range.
Example: Let's say you enter a trade with a positive expected value (expecting the price to drift up), and we have the limits set at 95%. What it tells you is that as long as the price stays within the limits, you can be 95% certain the model isn't completely random. As the price moves further away from the average, or expected value, it tells you that the model is less likely to be correct.
RWEDT MA
This indicator comes with 5 moving averages, each log transformed to reduce the skewness and asymmetry of price as much as possible
Rolling
Weighted
Exponential
Double Exponential
Triple Exponential
The band standard deviation can be adjusted, and the standard deviations have the weight of all of the moving averages that are present in the indicator. The weight is not customizable.
Why this indicator is useful:
This indicator can tell you what the expected value is. Above the moving average signifies a positive expected value, and below the moving average signifies a negative expected value. As previously stated above, the price moving further from the expected value lets you know that you should have less confidence that the model is "correct," and you could see this as taking profits as the price deviates further from the expected value.
The importance of log-transforming prices for standard deviations and moving averages.
Symmetry: Logarithmic transformations can help achieve symmetry in the distribution of price data. Stock prices, for example, exhibit some type of right-skewed distribution, where large positive price movements are more common than large negative movements. Price also can't go below 0 but can go towards positive infinity, so having a right-skew makes sense; all the outliers will be towards infinity, while all the average occurrences are "near" 0.
Stabilizing Variance: Price data typically exhibit heteroscedasticity, meaning that the variance of price movements changes over time. Log transformations can stabilize the variance and make it more consistent across different price levels. This is important for ensuring that the variability in price moves is not disproportionately influenced by extreme values.
Statistical Assumptions: Many retail indicators like Bollinger Bands use the standard deviation and moving average models of a normal distribution to attempt to model price, whose distribution more closely resembles some type of right-skew distribution. Even with the log-transformation, it still won't always resemble a perfect symmetrical distribution, and you still should not use it for mean reversion. You can still use it to understand the expected value and whether or not you should have confidence in your model.
Index investingThe Index Investing indicator simplifies decision-making for adding to Index ETF's Long-term investments. By utilizing a percentage discount methodology, it highlights potential opportunities to enhance portfolios. This straightforward tool aids in identifying favorable moments to invest based on calculated price discounts from selected reference points, making the process more systematic and less subjective.
🔶 SETTINGS
Reference Price: Choose between 'All-Time-High' or 'Start of the Year' as the basis for calculating discount levels. This allows for flexibility in strategy depending on market conditions or investment philosophy.
Discount 1 %, Discount 2 %, Discount 3 %: These inputs define the percentage below the reference price at which buy signals are generated. They represent strategic entry points at discounted prices.
🔶 Default Parameters
The default parameters of 4.13%, 8.26%, and 12.39% for the discount levels are chosen based on the average 5-year return of the NSE:NIFTY Index, which stands at approximately 12.39%. By dividing this return into three parts, we obtain a structured approach to capturing potential upside at varying levels of market retracement, providing a logical basis for the selected default values.
Users have the flexibility to modify these parameters, tailoring the indicator to fit their unique approach and market outlook.
🔶 How Levels Are Calculated
Discount levels are calculated using the formula: Discount Price = Reference Price * (1 - Discount %) . This succinct approach establishes specific entry points below the chosen reference, such as an all-time high or the year's start price.
🔶 How Are the Buy Labels Generated
Buy signals are generated when the market price(Low of the candle) crosses under any of the defined discount levels. Each level has a corresponding buy label ('Buy 1', 'Buy 2', 'Buy 3'), which is activated upon the price crossing below the specified discount level and is only reset at the beginning of a new year or upon reaching a new reference high, ensuring signals are not repetitive for the same price level.
🔶 Other Features
Alerts: The indicator provides alerts for each buy signal, notifying potential entry points at their defined discount levels. The alert triggers only once per candle.
Year Marker: A vertical line with an accompanying label marks the start of each trading year on the chart. This feature aids in visualizing the temporal context of buy signals and reference price adjustments.
Range PercentageRange Percentage is a simple indicator utility to clearly display and dynamically alert on where a chosen series falls between two bounds, either series themselves or constant values.
To set up, select between series or value for upper and lower bounds. Only the chosen options will be used by the indicator, though you may enter the non-selected option. Configure the thresholds if you wish to use them for visual display or alerting. If you only care about the background color, disable both thresholds and the percentage line and move the indicator into the main pane.
Some sample use cases:
Coloring background on a zoomed-in chart to show to show price change relative to the entire value of an asset, not just the range selected on the y-axis
Get alerts which adjust dynamically as price approaches another series or dynamic value
Determine at a glance where a price falls between your identified support/resistance lines, no matter where you zoom or scroll
Compare relative gain of two assets
Identify trends of a price closing closer to low or high over time
This indicator is often most useful in conjunction with other indicators which produce a plotted series output and can save a lot of time thinking or interpreting. Its usefulness to a trader depends entirely on the rationale for choosing a lower/upper bound and sample series that are meaningful to that trader.
Kalman Filter by TenozenAnother useful indicator is here! Kalman Filter is a quantitative tool created by Rudolf E. Kalman. In the case of trading, it can help smooth out the price data that traders observe, making it easier to identify underlying trends. The Kalman Filter is particularly useful for handling price data that is noisy and unpredictable. As an adaptive-based algorithm, it can easily adjust to new data, which makes it a handy tool for traders operating in markets that are prone to change quickly.
Many people may assume that the Kalman Filter is the same as a Moving Average, but that is not the case. While both tools aim to smooth data and find trends, they serve different purposes and have their own sets of advantages and disadvantages. The Kalman Filter provides a more dynamic and adaptive approach, making it suitable for real-time analysis and predictive capabilities, but it is also more complex. On the other hand, Moving Averages offer a simpler and more intuitive way to visualize trends, which makes them a popular choice among traders for technical analysis. However, the Moving Average is a lagging indicator and less adaptive to market change, if it's adjusted it may result in overfitting. In this case, the Kalman Filter would be a better choice for smoothing the price up.
I hope you find this indicator useful! It's been an exciting and extensive journey since I began diving into the world of finance and trading. I'll keep you all updated on any new indicators I discover that could benefit the community in the future. Until then, take care, and happy trading! Ciao.
MTF TREND-PANEL-(AS)
0). INTRODUCTION: "MTF TREND-PANEL-(AS)" is a technical tool for traders who often perform multi-timeframe analysis.
This simple tool is meant for traders who wish to monitor and keep track of trend directions simultaneously on various timeframes, ranging from 1MIN to 3MONTHS (or other - 'DIFF')
script enhances decision-making efficiency and provides a clearer picture of market condition by integrating multiple timeframe analysis into a single panel.
1). WARNING!:
-script doesn't make any calculations on its own really but is more of a tool for traders to remember what is happening on other time frames
- use tooltips to navigate settings easier
2). MAIN OPTIONS:
- Keeps track of up to 7 timeframes. (NUMBER of TimeFrames setting, from 1-7)
- Customizable Display: Choose to display nothing, upward/downward arrows, or a range indication for each timeframe.
- timeframe options: '1-MIN','5-MIN','15-MIN','30-MIN','1H','4H','1D','1W','1M','3M','DIFF'
- Color Coding: Define your preferred colors for each timeframe
- set position of the table and size of text (Position/text)
- Personal Touch: Add your own trading maxim or motto for inspiration to show up when SHOW TEXT is turned on
3. )OPTIONS:
-NUMBER of TimeFrames setting: from 1-7 - how many rows to show
-SHOW TABLE: Toggle to display or hide the trend table panel.
-SHOW TEXT: Show or hide your personalized trading maxim.
-SHOW TREND: Enable to display trend direction arrows.
-SHOW_CLRS: Turn on to activate color coding for each timeframe.
-position/text size for table
-settings for each timeframe:color,time,trend
-place to type ur own text
5). How to Use the Script:
-After adding the script to your chart, use the 'NUMBER of TimeFrames' setting to select how many timeframes you want to track (1 to 7).
-Customize the appearance of each timeframe row using the color and arrow options.
-For trend analysis, the script offers arrows to indicate upward, downward, or ranging markets.
-decide what trend dominates particular TF (using other tools - script does not calculate trend on its own )
- mark trends on panel to keep track of all TF
-Enable or disable various features like the table panel, trader maxim, and color coding using the ON/OFF options.
6). just in case:
- ask me anything about the code
-don't be shy to report any bugs or offer improvements of any kind.
- originally created for @ict_whiz and made public at his request
Commitments of Traders Report [Advanced]This indicator displays the Commitment of Traders (COT) report data in a clear, table format similar to an Excel spreadsheet, with additional functionalities to analyze open interest and position changes. The COT report, published weekly by the Commodity Futures Trading Commission (CFTC), provides valuable insights into market sentiment by revealing the positioning of various trader categories.
Display:
Release Date: When the data was released.
Open Interest: Shows the total number of open contracts for the underlying instrument held by selected trader category.
Net Contracts: Shows the difference between long and short positions for selected trader category.
Long/Short OI: Displays the long and short positions held by selected trader category.
Change in Long/Short OI: Displays the change in long and short positions since the previous reporting period. This can highlight buying or selling pressure.
Long & Short Percentage: Displays the percentage of total long and short positions held by each category.
Trader Categories (Configurable)
Commercials: Hedgers who use futures contracts to manage risk associated with their underlying business (e.g., producers, consumers).
Non-Commercials (Large Speculators): Speculative traders with large positions who aim to profit from price movements (e.g., hedge funds, investment banks).
Non-Reportable (Small Speculators/Retail Traders): Smaller traders with positions below the CFTC reporting thresholds.
CFTC Code: If the indicator fails to retrieve data, you can manually enter the CFTC code for the specific instrument. The code for instrument can be found on CFTC's website.
Using the Indicator Effectively
Market Sentiment Gauge: Analyze the positioning of each trader category to gauge overall market sentiment.
High net longs by commercials might indicate a bullish outlook, while high net shorts could suggest bearish sentiment.
Changes in open interest and long/short positions can provide additional insights into buying and selling pressure.
Trend Confirmation: Don't rely solely on COT data for trade signals. Use it alongside price action and other technical indicators for confirmation.
Identify Potential Turning Points: Extreme readings in COT data, combined with significant changes in open interest or positioning, might precede trend reversals, but exercise caution and combine with other analysis tools.
Disclaimer
Remember, the COT report is just one piece of the puzzle. It should not be used for making isolated trading decisions. Consider incorporating it into a comprehensive trading strategy that factors in other technical and fundamental analysis.
Credit
A big shoutout to Nick from Transparent FX ! His expertise and thoughtful analysis have been a major inspiration in developing this COT Report indicator. To know more about this indicator and how to use it, be sure to check out his work.
Genuine Liquidation Delta [Mxwll] - No EstimatesTHANK YOU TradingView for allowing us to upload custom data!!!
As a result, Mxwll Capital is providing an indicator that shows REAL liquidation delta for over 100 cryptocurrencies sourced directly from a popular crypto exchange!
Features
Crypto exchange sourced liquidation delta
Crypto exchange sourced long liquidation daily count
Crypto exchange sourced short liquidation daily count
All provided data extends back 2 years!!
Various aesthetic components to illustrate data
Liquidation delta data (sourced from a popular exchange) is provided for:
1000shib
aave
ada
algo
alice
arb
audio
alpha
ankr
ape
apt
atom
avax
axs
bal
band
bat
bch
bel
blz
blur
bnb
bnx
btc
chr
chz
comp
coti
crv
ctk
dash
defi
doge
dot
dydx
edu
egld
enj
ens
eos
etc
eth
fil
flm
ftm
fxs
gala
gmx
grt
hbar
hnt
icx
id
inj
iost
iota
joe
kava
knc
ksm
ldo
lina
link
lit
lrc
ltc
mana
mask
matic
mkr
near
neo
ocean
omg
one
ont
op
people
qtum
reef
ren
rndr
rose
rlc
rsr
rune
rvn
sand
sfp
skl
snx
sol
stmx
storj
sui
sushi
sxp
theta
tomo
trb
trx
unfi
uni
vet
waves
xem
xlm
xmr
xrp
xtz
yfi
zec
zen
zil
zrx
How-To
The image above shows the indicator with default settings.
The image above shows the start point of our data!
Over 2-years of data, allowing for plentiful analysis!
The image above explains the primary plot.
Filled blue columns reflect liquidation delta exceeding the long side. When the liquidation delta plot is aqua and exceeds 0 to the upside, longs were liquidated more than shorts for the
day.
Filled red columns reflect liquidation delta exceeding the short side. When the liquidation delta plot is red and exceeds 0 to the downside, shorts were liquidated more than longs for the day.
The image above explains the solid line (polyline) plot and its intentions!
Filled, solid, blue line reflects the total number of long liquidation events for the period.
Filled, solid, red line reflects the total number of short liquidation events for the period.
Keep in mind that the total number of liquidation events is normalized to plot alongside the total liquidation delta for the day. So, there aren't "millions" of liquidation events taking place, the total liquidation count for the long and short side is simply normalized to fit atop total liquidation delta.
The image above explains the liquidation count meter the indicator provides!
The left (blue columns) reflect the intensity of long liquidation events for the day. The right (red columns) reflect the intensity of short liquidation events for the day.
The "Max" numbers at the top show the maximum number of long liquidation events, or short liquidation events, for their respective columns.
Therefore, if the number of long liquidation events were "1.241k", as stated for this cryptocurrency in the table, the blue meter would be full. Similar logic applies to the red meter.
Once more, THANK YOU @TradingView and @PineCoders for allowing us to upload custom data! This project wouldn't be possible without it!
Median Supertrend [BackQuant]Median Supertrend Concept by BackQuant ©
This was created since the normal supertrend is noisy, in the attempts to remove that and still get a good signal we decided to use a special median calculation as the source to a modified supertrend. This allows us to reduce noise, and make the supertrend adaptive to volatility. The full description and reasoning, including definitions and backtests are as follows:
1. Definition of Median
The median is a statistical measure that identifies the middle value in a given set of numbers when those numbers are arranged in either ascending or descending order. If the dataset has an even number of observations, the median is calculated as the average of the two middle numbers. This measure is particularly useful in understanding the central tendency of data, especially in cases where the dataset may contain outliers that could skew the mean. For example, in a dataset representing the earnings of families, the median provides a more accurate reflection of the typical income than the mean if the dataset includes extreme values.
2. Understanding Supertrend and Its Use Case
Supertrend is a popular trend-following indicator used in technical analysis. It is computed using the Average True Range (ATR) to capture volatility, combined with a moving average. The indicator provides clear signals to traders about bullish or bearish trends, indicating potential entry and exit points. Traders often use Supertrend in various market conditions to enhance their trading strategies, leveraging its simplicity and effectiveness in identifying ongoing trends and reversals.
3. Rationale Behind Combining Median with Supertrend
The integration of the median into the Supertrend indicator seeks to mitigate the impact of outliers and sudden market spikes that can affect trend analysis. By using the median value of price data for trend determination, the Median Supertrend aims to offer a more stable and reliable indicator that reflects the underlying market conditions more accurately than traditional methods. This modification is intended to improve the timing of trend detection and the precision of entry and exit signals.
4. Key Differences and Benefits
Enhanced Stability: The use of median values reduces sensitivity to extreme price movements, offering a smoother trend line that can lead to more reliable trading signals.
Adaptive Sensitivity: Users can adjust the indicator's sensitivity to align with different trading styles and market conditions through customizable parameters like the ATR multiplier and lookback period.
Explicit Trading Signals: The indicator simplifies the trading process by providing clear, actionable long and short signals based on trend reversals, aiding in decision-making.
Customizability: Options to use Heikin Ashi candles, paint candles based on the trend, and toggle signal visibility allow traders to personalize the indicator to their preference.
5. User Inputs
The Median Supertrend indicator includes several user inputs to tailor its operation:
Use HA Candles as Source?: Option to base calculations on Heikin Ashi candles for smoother price data.
Paint Candles According to Trend?: Visual aid that colors candles based on the current trend direction, enhancing chart readability.
ATR Period and Multiplier: Parameters to adjust the sensitivity of the trend detection, allowing users to fine-tune the indicator.
Adaptive Lookback Period: Defines the period for the median calculation, offering flexibility in trend assessment.
Show Long and Short Signals: Enables traders to visualize entry signals directly on the chart.
6. Application in Trading
Traders can incorporate the Median Supertrend into their strategies as a standalone indicator for trend following or as a filter in a multi-indicator system. It is particularly useful in markets known for having outliers or sudden price jumps, as the median-based calculation provides a grounded trend analysis. This indicator can be applied across various timeframes and asset classes, making it a versatile tool for day traders, swing traders, and long-term investors alike.
7. Summary and Empirical Soundness
The integration of median values into the Supertrend indicator represents an innovative approach to trend analysis, addressing some of the volatility and outlier-related challenges inherent in traditional methods. This combination is empirically sound as it leans on the statistical robustness of the median to offer a more stable and reliable trend determination mechanism.
8. Relavant Backtests on Major Assets (1D Timeframe)
We include these backtests as a general proxy for how they work.
Please do your own calibrating to suit it to your own needs and backtest.
Past results don't = future results but they can help you understand how it functions.
INDEX:BTCUSD
INDEX:ETHUSD
BINANCE:SOLUSD
Semaphore PlotThe Semaphore Plot V2, crafted by OmegaTools for the TradingView platform, is a sophisticated technical analysis tool designed to offer traders nuanced insights into market dynamics. This closed-source script embodies a novel approach by synthesizing multiple technical analysis methodologies into a coherent analytical framework. This detailed description aims to demystify the operational essence of the Semaphore Plot V2 and elucidate its application in trading scenarios without overstepping into claims of infallibility or price prediction accuracy.
Analytical Foundations and Integration:
At its core, the Semaphore Plot V2 is founded on the integration of several analytical dimensions, each contributing to a comprehensive market overview:
1. Dynamic Trend Analysis: Unlike conventional trend indicators that might rely solely on moving averages, the Semaphore Plot V2 examines the market's direction through a more complex lens. It assesses momentum, utilizing derivatives of price movements to understand the velocity and acceleration of trends. This analysis is deepened by examining the rate of change (ROC), providing a multi-tiered view of how swiftly market conditions are evolving.
2. Volatility Insights: Recognizing volatility as a pivotal component of market behavior, the script incorporates volatility metrics to analyze market conditions. By evaluating historical price ranges and applying statistical models, it aims to gauge the potential for future price fluctuations, thus offering insights into market stability or turbulence without predicting specific movements.
3. Linear Regression and Predictive Analysis: The script utilizes linear regression to analyze price data points over a specified period, offering a statistical basis to understand the trajectory of market trends. This regression analysis is complemented by market momentum indicators, forming a predictive model that suggests potential areas where market activity might concentrate. It's important to note that these "predictions" are not certainties but rather statistically derived zones of interest based on historical data.
4. Market Sentiment and Risk Evaluation: Incorporating an evaluation of market sentiment, the script analyzes trends in trading volume and price action to deduce the prevailing market mood. Risk assessment tools, such as the analysis of statistical deviations and Value at Risk (VaR), are also applied to offer a perspective on the risk associated with current market conditions.
Operational Mechanism:
- By processing the integrated analysis, the script generates semaphore signals which are plotted on the trading chart. These signals are not direct buy or sell signals but are designed to highlight areas where, based on the script’s complex analysis, market activity might see significant developments.
- Additionally, the Semaphore Plot V2 features an information table that provides a retrospective analysis of the signals' alignment with market movements, offering traders a tool to assess the script's historical context.
Application and Utility:
- Traders can leverage the Semaphore Plot V2 by applying it to their TradingView charts and adjusting input settings such as lookback periods and sensitivity according to their preferences.
- The semaphore signals serve as markers for areas of potential interest. Traders are encouraged to interpret these signals within the context of their overall market analysis, incorporating other fundamental and technical analysis tools as necessary.
- The informational table serves as a resource for evaluating the historical context of the signals, providing an additional layer of insight for informed decision-making.
The Essence of Originality:
The Semaphore Plot V2 distinguishes itself through the innovative melding of traditional technical analysis components into a unique analytical concoction. This originality lies not in the creation of new technical indicators but in the novel integration and application of existing methodologies to offer a holistic view of market conditions.
Responsible Usage Disclaimer:
The financial markets are characterized by uncertainty, and the Semaphore Plot V2 is intended to serve as an analytical tool within a trader's arsenal, not a standalone solution for trading decisions. It is critical for users to understand that the script does not guarantee trading success nor does it claim to predict exact price movements. Traders should employ the Semaphore Plot V2 alongside comprehensive market analysis and sound risk management practices, acknowledging that past performance is not indicative of future results and that trading involves the risk of loss.
Trading TP SL Risk Commission Calculator🎉 Introducing Your Trading TP SL Risk Commission Calculator! 🎉
Hey there, savvy trader! 🚀 Are you looking to enhance your trading game? Meet the Trading TP SL Risk Commission Calculator! This handy tool is here to guide you through the complexities of trading, providing insights into your potential risks and rewards. Let's walk through how you can leverage it for smarter trading decisions!
Setting Up 🛠
Let's get your calculator ready for action:
Lines and Labels Visibility: Flip this switch on to see your Entry, Take Profit (TP), Stop Loss (SL), and Liquidation points displayed on your chart. It's a great way to get a visual summary of your strategy.
Input Your Trade Details: Enter your Entry Price, Take Profit Price, and Stop Loss Price. These figures are crucial for mapping out your trade.
Order Info: Specify your Order Size in USD, the amount of Leverage you're using, and your platform's Commission Rate. This customizes the calculator to fit your unique trading setup.
Customizing Your View 🎨
Table Placement & Size: Pick the location and size for your results table to appear on your screen. Tailor it to your liking, whether you prefer it out of the way or front and center.
Deciphering Your Results 📊
With your inputs in place, the calculator springs into action. Here's what you'll find:
Risk Assessment (with Emojis!): Quickly gauge your risk level with our intuitive emoji system, ranging from "⛔️⛔️⛔️" (very high risk) to "✅✅✅" (very low risk).
Profit and Loss Insights: Understand your potential take-profit gains and stop-loss implications, both as percentages and in USD. We also factor in fees to give you a clear picture.
Liquidation Alert: For those using leverage, the liquidation price calculation is crucial to avoid unpleasant surprises.
Expert Tips 💡
Stay Flexible: Market conditions evolve, so should your strategy. Revisit and adjust your inputs regularly to stay aligned with your trading goals.
Risk Emoji Check: Keep an eye on your risk level emojis. A sea of "⛔️" might signal it's time to reassess your approach.
Use Visual Guides: The on-chart lines and labels offer a quick visual reference to how your current trade measures up against your TP, SL, and liquidation thresholds.
Dive In and Trade Smart! 🚦
This calculator isn't just about making calculations; it's about empowering you to make informed trading decisions. With this tool in your arsenal, you're equipped to navigate the trading waters with confidence and clarity.
Risk Management Chart█ OVERVIEW
Risk Management Chart allows you to calculate and visualize equity and risk depend on your risk-reward statistics which you can set at the settings.
This script generates random trades and variants of each trade based on your settings of win/loss percent and shows it on the chart as different polyline and also shows thick line which is average of all trades.
It allows you to visualize and possible to analyze probability of your risk management. Be using different settings you can adjust and change your risk management for better profit in future.
It uses compound interest for each trade.
Each variant of trade is shown as a polyline with color from gradient depended on it last profit.
Also I made blurred lines for better visualization with function :
poly(_arr, _col, _t, _tr) =>
for t = 1 to _t
polyline.new(_arr, false, false, xloc.bar_index, color.new(_col, 0 + t * _tr), line_width = t)
█ HOW TO USE
Just add it to the cart and expand the window.
█ SETTINGS
Start Equity $ - Amount of money to start with (your equity for trades)
Win Probability % - Percent of your win / loss trades
Risk/Reward Ratio - How many profit you will get for each risk(depends on risk per trade %)
Number of Trades - How many trades will be generated for each variant of random trading
Number of variants(lines) - How many variants will be generated for each trade
Risk per Trade % -risk % of current equity for each trade
If you have any ask it at comments.
Hope it will be useful.
Difference from Highest Price (Last N Candles)The output of this TradingView indicator is a label that appears below the latest candle on the chart. This label provides information about:
The highest high of the last N candles.
The highest close of the last N candles.
The current trading price.
The percentage difference between the highest high and the current trading price.
The percentage difference between the highest close and the current trading price.
The percentage change in price from the previous candle.
The N-day average percentage change.
This information is useful for traders to understand the relationship between the current price and recent price action, as well as to identify potential overbought or oversold conditions based on the comparison with recent highs and closes.
Here's a breakdown of what the code does:
It takes an input parameter for the number of days (or candles) to consider (input_days).
It calculates the highest high and highest close of the last N candles (highest_last_n_high and highest_last_n_close).
It calculates the difference between the close of the current candle and the close of the previous candle (diff), along with the percentage change.
It maintains an array of percentage changes of the last N days (percentage_changes), updating it with the latest percentage change.
It calculates the sum of percentage changes and the N-day average percentage change.
It calculates the difference between the highest high/highest close of the last N candles and the current trading price, along with their percentage differences.
Finally, it plots this information as a label below the candle for the latest bar.
Blockunity Miners Synthesis (BMS)Track the status of Bitcoin and Ethereum Miners' Netflows and their asset reserves.
The Idea
The goal is to provide a simple tool for visualizing the changes in miners' flows and reserves.
How to Use
Analysing the behaviour of miners enables you to detect long-term opportunities, in particular with the state of reserves, but also in the shorter term with the visualization of Netflows.
Elements
Miners Reserves
Miners Reserves represent the balances of addresses belonging to mining pools (in BTC or ETH).
This data can also be displayed in USD via the indicator parameters:
Miners Netflow
The Netflow is calculated by subtracting the outflows from the inflows originating from addresses associated with mining pools. When this result is negative, it indicates that more funds are exiting the miners' accounts than the funds they are receiving. Consequently, negative miner netflows suggests selling activity.
This data can also be displayed in USD via the indicator parameters. You can also choose the timeframe. For example, selecting "Yearly" will give a Netflow daily average taking into account the last 365 days:
Settings
In the settings, you can first choose which asset to view, between Bitcoin and Ethereum. Here are the reserves of Ethereum miners:
As with Bitcoin, Netflow can also be displayed in the timeframe of your choice. Here you can see the average daily netflow of Ethereum miners in USD over the last 30 days:
Here are all the parameters:
Asset Selector: Choose between Bitcoin or Ethereum miner data.
Get values in USD: Displays values in USD instead of assets.
Switch between Netflow and Reserve : If checked, displays Miners' Reserves data. If unchecked, displays Miners' Netflow data.
Display timeframe: Allows you to select the timeframe for displaying the Netflow plot.
Period Lookback (in days): Select the period to be taken into account when calculating the variation percentage of Miners' Reserves.
Lastly, you can modify all table and labels parameters.
Moving Average PropertiesThis indicator calculates and visualizes the Relative Smoothness (RS) and Relative Lag (RL) or call it accuracy of a selected moving average (MA) in comparison to the SMA of length 2 (the lowest possible length for a moving average and also the one closest to the price).
Median RS (Relative Smoothness):
Interpretation: The median RS represents the median value of the Relative Smoothness calculated for the selected moving average across a specified look-back period (max bar lookback is set at 3000).
Significance: A more negative (larger) median RS suggests that the chosen moving average has exhibited smoother price behavior compared to a simple moving average over the analyzed period. A less negative value indicates a relatively choppier price movement.
Median RL (Relative Lag):
Interpretation: The median RL represents the median value of the Relative Lag calculated for the selected moving average compared to a simple moving average of length 2.
Significance: A higher median RL indicates that the chosen moving average tends to lag more compared to a simple moving average. Conversely, lower values suggest less lag in the selected moving average.
Ratio of Median RS to Median RL:
Interpretation: This ratio is calculated by dividing the median RS by the median RL.
Significance: Traders might use this ratio to assess the balance between smoothness and lag in the chosen moving average. This a measure of for every % of lag what is the smoothness achieved. This can be used a benchmark to decide what length to choose for a MA to get an equivalent value between two stocks. For example a TESLA stock on a 15 minute time frame with a length of 12 has a value (ratio of RS/RL) of -150 , where as APPLE stock of length 35 on a 15 minute chart also has a value (ratio of RS/RL) of -150.
I imply that a MA of length 12 working on TESLA stock is equivalent to MA of length 35 on a APPLE stock. (THIS IS A EXAMPLE).
My assumption is that finding the right moving average length for a stock isn't a one-size-fits-all situation. It's not just about using a fixed length; it's about adapting to the unique characteristics of each stock. I believe that what works for one stock might not work for another because they have different levels of smoothness or lag in their price movements. So, instead of applying the same length to all stocks, I suggest adjusting the length of the moving average to match the values that we know work best for achieving the desired smoothness or lag or its ratio (RS/RL). This way, we're customizing the indicator for each stock, tailoring it to their individual behaviors rather than sticking to a one-size-fits-all approach.
Users can choose from various types of moving averages (EMA, SMA, WMA, VWMA, HMA) and customize the length of the moving average. RS measures the smoothness of the MA, while RL measures its lag compared to a simple moving average. The script plots the median RS and RL values, the selected MA, and the ratio of median RS to median RL on the price chart. Traders can use this information to assess the performance of different moving averages and potentially inform their trading decisions.
ATR Grid Levels [By MUQWISHI]▋ INTRODUCTION :
The “ATR Levels” produces a sequence of horizontal line levels above and below the Center Line (reference level). They are sized based on the instrument's volatility, representing the average historical price movement on a selected higher timeframe using the average true range (ATR) indicator.
_______________________
▋ OVERVIEW:
_______________________
▋ IMPLEMENTATION:
The indicator starts by drawing a Center Line that is selected by the user from a variety of common levels. Then, it draws a sequence of horizontal lines above and below the Center Line, which are sized based on the most confirmed average true range (ATR) at the selected higher timeframe.
In the top right corner of the chart, there is a table displaying both the selected ATR (in the right cell) and the ATR of the current bar (in the left cell). This feature enables users to compare these two values. It's important to note that the ATR of the current bar may not be confirmed yet, as the market is still active.
_______________________
▋ INDICATOR SETTINGS:
# Section (1): ATR Settings
(1) ATR Period & Smoothing.
(2) Timeframe where ATR value imported from.
(3) To show/hide the table comparison between the current ATR and the ATR for the selected period. Also, ability to color the current ATR cell if it’s greater.
# Section (2): Levels Settings
(1) Selecting a Center Line level among a variety of common levels, which is taken as reference level where a sequence of horizontal lines plot above and below it.
(2) Size of grid in ATR unit.
(3) Number of horizontal lines to plot in a single side.
(4) Grid Side. Ability to plot above or below the Center Line.
(5) Lines colors, and mode.
(6) Line style.
(7) Label style.
(8) Ability to remove old lines, from previous HTF.
_____________________
▋ COMMENT:
The ATR Levels should not be taken as a major concept to build a trading decision.
Please let me know if you have any questions.
Thank you.
LuxAlgo® - Screener (OSC)The LuxAlgo® - Screener (OSC) is a complete tool allowing users to check returned information from the Oscillator Matrix™ toolkit's features for various user selected tickers and timeframes.
Users can customize the returned information by the screener, as well as filtering out displayed tickers based on custom user set rules.
🔶 FEATURES
Users can place the location of the screener everywhere they want, multiple locations are supported, you can even have it on your chart by drag and dropping the screener to your chart, allowing you to analyze them alongside your favorite indicators.
Keeping track of various tickers is crucial to have a deeper understanding of the overall market activity.
Our screener let you quickly access your preferred information in a convenient way thanks to the described features below:
Screening of the main Oscillator Matrix™ features on up to 10 user selected tickers and timeframes.
Ticker filtering based on custom user set rules.
Ticker sorting based on ascending/descending user selected data returned by the screener.
The LuxAlgo® - Screener (OSC) returns the following information:
Current price
Current volume
Current price percent change (% CHG)
Current price change (CHG)
Current rating
Most recent HyperWave signal
Current Money Flow value
Current Overflow value
Current HyperWave value
Most recent reversal signal
Most recent divergence
Current Confluence status
🔹 Rating
Users can quickly check the overall sentiment based on the screeners returned information by looking at the Rating column. Tickers can be rated as follows:
▲ Strong Bullish (more than 80% of the returned information is bullish)
△ Bullish (60% to 80% of the returned information is bullish)
― Neutral (40% to 60% of the returned information is bullish)
▽ Bearish (20% to 60% of the returned information is bullish)
▼ Strong Bearish (less than 20% of the returned information is bullish)
This can be a quick way to asses the confluence between all the returned information on the screener for a specific ticker.
🔹 Filtering
Thanks to the integrated filtering capabilities of the LuxAlgo® - Screener (OSC) you will be able to keep track of the information from tickers that return specific information you want to see.
For example do you want to only see the information from tickers with bullish money flow?
Nothing easier, all you need is to select the "Above" option in the Money Flow dropdown menu and set the value 50 in the input to the right.
However, you don't have to stop at 1 filtering condition, create more complex ones that fits your trading style for the tickers you truly want to look at!
🔹 Sorting
As traders we want to quickly spot the tickers with most volume, most volatility, with the strongest uptrend or downtrend.
The LuxAlgo® - Screener (OSC) lets you do that by sorting supported information in an ascending or descending order, letting you access the most relevant information faster.
LuxAlgo® - Screener (PAC)The LuxAlgo® - Screener (PAC) is a complete tool allowing users to check returned information from the Price Action Concepts™ toolkit's features for various user selected tickers and timeframes.
Users can customize the returned information by the screener, as well as filtering out displayed tickers based on custom user set rules.
🔶 FEATURES
Users can place the location of the screener everywhere they want, multiple locations are supported, you can even have it on your chart by drag and dropping the screener to your chart, allowing you to analyze them alongside your favorite indicators.
Keeping track of various tickers is crucial to have a deeper understanding of the overall market activity.
Our screener let you quickly access your preferred information in a convenient way thanks to the described features below:
Screening of the main Price Action Concepts™ features on up to 10 user selected tickers and timeframes.
Ticker filtering based on custom user set rules.
Ticker sorting based on ascending/descending user selected data returned by the screener.
The LuxAlgo® - Screener (PAC) returns the following information:
Current price
Current volume
Current price percent change (% CHG)
Current price change (CHG)
Current rating
Most recent market structure
Most recent Order Block type and relative position to price
Order Block buy volume
Order Block sell volume
Order Block total volume
Most recent user set imbalance type status. Options include screening for FVG, Inverse FVG, Double FVG, Volume Imbalance and Opening Gap
Price position relative to Premium/Discount zones
Most recent liquidity grab
Most recent equal high/low
🔹 Rating
Users can quickly check the overall sentiment based on the screeners returned information by looking at the Rating column. Tickers can be rated as follows:
▲ Strong Bullish (more than 80% of the returned information is bullish)
△ Bullish (60% to 80% of the returned information is bullish)
― Neutral (40% to 60% of the returned information is bullish)
▽ Bearish (20% to 60% of the returned information is bullish)
▼ Strong Bearish (less than 20% of the returned information is bullish)
This can be a quick way to asses the confluence between all the returned information on the screener for a specific ticker.
🔹 Filtering
Thanks to the integrated filtering capabilities of the LuxAlgo® - Screener (PAC) you will be able to keep track of the information from tickers that return specific information you want to see.
For example do you want to only see the information from up trending tickers? Nothing easier, all you need is to select the up trending related options (▲ Strong Bullish or △ Bullish) in the rating dropdown menu.
However, you don't have to stop at 1 filtering condition, create more complex ones that fits your trading style for the tickers you truly want to look at!
🔹 Sorting
As traders we want to quickly spot the tickers with most volume, most volatility, with the strongest uptrend or downtrend.
The LuxAlgo® - Screener (PAC) lets you do that by sorting supported information in an ascending or descending order, letting you access the most relevant information faster.
LuxAlgo® - Screener (S&O)The LuxAlgo® - Screener (S&O) is a complete tool allowing users to check returned information from Signals & Overlays™ features for various user selected tickers and timeframes.
Users can customize the returned information by the screener, as well as filtering out displayed tickers based on custom user set rules.
🔶 FEATURES
Users can place the location of the screener everywhere they want, multiple locations are supported, you can even have it on your chart by drag and dropping the screener to your chart, allowing you to analyze them alongside your favorite indicators.
Keeping track of various tickers is crucial to have a deeper understanding of the overall market activity.
Our screener let you quickly access your preferred information in a convenient way thanks to the described features below:
Screening of the main Signals & Overlays™ features on up to 10 user selected tickers and timeframes.
Ticker filtering based on custom user set rules.
Ticker sorting based on ascending/descending user selected data returned by the screener.
The LuxAlgo® - Screener (S&O) returns the following information:
Current price
Current volume
Current price percent change (% CHG)
Current price change (CHG)
Current rating
Most recent signal
Number of Exits since most recent signals
Current Smart Trail status
Current Reversal Zones status
Current Trend Catcher status
Current Trend Tracer status
Current Neo Cloud status
Current Trend Strength value
Current Lux Volatility value
Current Squeeze Index value
Current Volume Sentiment value
🔹 Rating
Users can quickly check the overall sentiment based on the screeners returned information by looking at the Rating column. Tickers can be rated as follows:
▲ Strong Bullish (more than 80% of the returned information is bullish)
△ Bullish (60% to 80% of the returned information is bullish)
― Neutral (40% to 60% of the returned information is bullish)
▽ Bearish (20% to 60% of the returned information is bullish)
▼ Strong Bearish (less than 20% of the returned information is bullish)
This can be a quick way to asses the confluence between all the returned information on the screener for a specific ticker.
🔹 Filtering
Thanks to the integrated filtering capabilities of the LuxAlgo® - Screener (S&O) you will be able to keep track of the information from tickers that return specific information you want to see.
For example do you want to only see the information from up trending tickers? Nothing easier, all you need is to select the up trending related options (▲ Strong Bullish or △ Bullish) in the rating dropdown menu.
However you don't have to stop at 1 filtering condition, create more complex ones that fits your trading style for the tickers you truly want to look at!
🔹 Sorting
As traders we want to quickly spot the tickers with most volume, most volatility, with the strongest uptrend or downtrend.
The LuxAlgo® - Screener (S&O) lets you do that by sorting supported information in an ascending or descending order, letting you access the most relevant information faster.
Machine Learning Cross-Validation Split & Batch HighlighterThis indicator is designed for traders and analysts who employ Machine Learning (ML) techniques for cross-validation in financial markets.
The script visually segments a selected range of historical price data into splits and batches, helping in the assessment of model performance over different market conditions.
User
Theory
In ML, cross-validation is a technique to assess the generalizability of a model, typically by partitioning the data into a set of "folds" or "splits." Each split acts as a validation set, while the others form the training set. This script takes a unique approach by considering the sequential nature of financial time series data, where random shuffling of data (as in traditional cross-validation) can disrupt the temporal order, leading to misleading results.
Chronological Integrity of Splits
Even if the order of the splits is shuffled for cross-validation purposes, the data within each split remains in its original chronological sequence. This feature is crucial for time series analysis, as it respects the inherent order-dependency of financial markets. Thus, each split can be considered a microcosm of market behavior, maintaining the integrity of trends, cycles, and patterns that could be disrupted by random sampling.
The script allows users to define the number of splits and the size of each batch within a split. By doing so, it maintains the chronological sequence of the data, ensuring that the validation set is representative of a future time period that the model would predict.
www.tradingview.com
Parameters
Number of Splits: Defines how many segments the selected data range will be divided into. Each split serves as a standalone testing ground for the ML model. (Up to 24)
Batch Size: Determines the number of bars (candles) in each batch within a split. Smaller batches can help pinpoint overfitting at a finer granularity.
Start Index: The bar index from where the historical data range begins. It sets the starting point for data analysis.
End Index: The bar index where the historical data range ends. It marks the cutoff for data to be included in the model assessment.
Usage
To use this script effectively:
1 - Input the Start Index and End Index to define the historical data range you wish to analyze.
2 - Adjust the Number of Splits to create multiple validation sets for cross-validation.
3 - Set the Batch Size to control the granularity of each validation set within the splits.
4 - The script will highlight the background of each batch within the splits using alternating shades, allowing for a clear visual distinction of the data segmentation.
By maintaining the temporal sequence and allowing for adjustable granularity, the "ML Split and Batch Highlighter" aids in creating a robust validation framework for time series forecasting models in finance.
Bandwidth Volatility - Silverman Rule of thumb EstimatorOverview
This indicator calculates volatility using the Rule of Thumb bandwidth estimator and incorporating the standard deviations of returns to get historical volatility. There are two options: one for the original rule of thumb bandwidth estimator, and another for the modified rule of thumb estimator. This indicator comes with the bandwidth , which is shown with the color gradient columns, which are colored by a percentile of the bandwidth, and the moving average of the bandwidth, which is the dark shaded area.
The rule of thumb bandwidth estimator is a simple and quick method for estimating the bandwidth parameter in kernel density estimation (KSE) or kernel regression. It provides a rough approximation of the bandwidth without requiring extensive computation resources or fine-tuning. One common rule of thumb estimator is Silverman rule, which is given by
h = 1.06*σ*n^(-1/5)
where
h is the bandwidth
σ is the standard deviation of the data
n is the number of data points
This rule of thumb is based on assuming a Gaussian kernel and aims to strike a balance between over-smoothing and under-smoothing the data. It is simple to implement and usually provides reasonable bandwidth estimates for a wide range of datasets. However , it is important to note that this rule of thumb may not always have optimal results, especially for non-Gaussian or multimodal distributions. In such cases, a modified bandwidth selection, such as cross-validation or even applying a log transformation (if the data is right-skewed), may be preferable.
How it works:
This indicator computes the bandwidth volatility using returns, which are used in the standard deviation calculation. It then estimates the bandwidth based on either the Silverman rule of thumb or a modified version considering the interquartile range. The percentile ranks of the bandwidth estimate are then used to visualize the volatility levels, identify high and low volatility periods, and show them with colors.
Modified Rule of thumb Bandwidth:
The modified rule of thumb bandwidth formula combines elements of standard deviations and interquartile ranges, scaled by a multiplier of 0.9 and inversely with a number of periods. This modification aims to provide a more robust and adaptable bandwidth estimation method, particularly suitable for financial time series data with potentially skewed or heavy-tailed data.
Formula for Modified Rule of Thumb Bandwidth:
h = 0.9 * min(σ, (IQR/1.34))*n^(-1/5)
This modification introduces the use of the IQR divided by 1.34 as an alternative to the standard deviation. It aims to improve the estimation, mainly when the underlying distribution deviates from a perfect Gaussian distribution.
Analysis
Rule of thumb Bandwidth: Provides a broader perspective on volatility trends, smoothing out short-term fluctuations and focusing more on the overall shape of the density function.
Historical Volatility: Offers a more granular view of volatility, capturing day-to-day or intra-period fluctuations in asset prices and returns.
Modelling Requirements
Rule of thumb Bandwidth: Provides a broader perspective on volatility trends, smoothing out short-term fluctuations and focusing more on the overall shape of the density function.
Historical Volatility: Offers a more granular view of volatility, capturing day-to-day or intra-period fluctuations in asset prices and returns.
Pros of Bandwidth as a volatility measure
Robust to Data Distribution: Bandwidth volatility, especially when estimated using robust methods like Silverman's rule of thumb or its modifications, can be less sensitive to outliers and non-normal distributions compared to some other measures of volatility
Flexibility: It can be applied to a wide range of data types and can adapt to different underlying data distributions, making it versatile for various analytical tasks.
How can traders use this indicator?
In finance, volatility is thought to be a mean-reverting process. So when volatility is at an extreme low, it is expected that a volatility expansion happens, which comes with bigger movements in price, and when volatility is at an extreme high, it is expected for volatility to eventually decrease, leading to smaller price moves, and many traders view this as an area to take profit in.
In the context of this indicator, low volatility is thought of as having the green color, which indicates a low percentile value, and also being below the moving average. High volatility is thought of as having the yellow color and possibly being above the moving average, showing that you can eventually expect volatility to decrease.
Likelihood of Winning - Probability Density FunctionIn developing the "Likelihood of Winning - Probability Density Function (PDF)" indicator, my aim was to offer traders a statistical tool to quantify the probability of reaching target prices. This indicator, grounded in risk assessment principles, enables users to analyze potential outcomes based on the normal distribution, providing insights into market dynamics.
The tool's flexibility allows for customization of the data series, lookback periods, and target settings for both long and short scenarios. It features a color-coded visualization to easily distinguish between probabilities of hitting specified targets, enhancing decision-making in trading strategies.
I'm excited to share this indicator with the trading community, hoping it will enhance data-driven decision-making and offer a deeper understanding of market risks and opportunities. My goal is to continuously improve this tool based on user feedback and market evolution, contributing to more informed trading practices.
This indicator leverages the "NormalDistributionFunctions" library, enabling easy integration into other indicators or strategies. Users can readily embed advanced statistical analysis into their trading tools, fostering innovation within the Pine Script community.
