Gold Forex 20 Pips TP/SL Strategy By Vjay for 1hrGold Forex 20 Pips TP/SL Strategy By Vjay for 1hr
in this strategy you need to set time frame on 1 hr and target, sl will be 20 pips this strategy 55% accurate on 1 hr
Indicators and strategies
Trend-Based Signals (NASDAQ) - LA CLAVE ESTÁ EN NO RENDIRSEOrlando Pereira
// Highlight Time Zones
in_zone1 = (hour == 8 and minute >= 30 and minute <= 35) // 8:30 am to 8:36 am EST
in_zone2 = (hour == 8 and minute > 35) or (hour == 9) or (hour == 10 and minute == 0) // 8:36 am to 10:00 am EST
bgcolor(in_zone1 ? color_zone1 : na, title="Zone 1 Background")
bgcolor(in_zone2 ? color_zone2 : na, title="Zone 2 Background")
// Display motivational message
if bar_index == na
label.new(bar_index, high, "LA CLAVE ESTÁ EN NO RENDIRSE",
style=label.style_label_center,
color=color.orange,
textcolor=color.black,
size=size.large)
Jrols bullshitGamestop has had its fair share of grifters throughout the years, but none as blatant as JamesRoland
x.com
He's selling a "trigger indicator" taht is just fucking EMAs.
I thought I'd provide the community with a free version because fuck him that's why.
I'm sick and tired of bad actors taking advantage of people.
Its not fancy with bullshit fades like his, but i don't fucking care. Run it and get the same "signals" as his bullshit paid indicator provides.
How to Use the "Jrols Bullshit" Indicator in Pine Script
This script is a custom indicator for TradingView that plots three different Exponential Moving Averages (EMAs) on the chart, each calculated from different timeframes: Daily, Weekly, and Monthly. These EMAs help identify long-term trends on different time scales.
Key Features:
Daily 40 EMA (calculated using a daily timeframe)
Weekly 120 EMA (calculated using a weekly timeframe)
Monthly 575 EMA (approximated using 575 periods on the daily chart)
Each of these EMAs is plotted using a step line style, making it easy to see the level at which each EMA lies over time.
RANJAN ALMA Xit is a risk management indicator where you invest 30% on yellow dot, then 30% on black dot,and rest 40% on green dot. sl 4% initially after final entry trail on 100 alma
FACTOR MONITORThe Factor Monitor is a comprehensive designed to track relative strength and standard deviation movements across multiple market segments and investment factors. The indicator calculates and displays normalized percentage moves and their statistical significance (measured in standard deviations) across daily, 5-day, and 20-day periods, providing a multi-timeframe view of market dynamics.
Key Features:
Real-time tracking of relative performance between various ETF pairs (e.g., QQQ vs SPY, IWM vs SPY)
Standard deviation scoring system that identifies statistically significant moves
Color-coded visualization (green/red) for quick interpretation of relative strength
Multiple timeframe analysis (1-day, 5-day, and 20-day moves)
Monitoring of key market segments:
Style factors (Value, Growth, Momentum)
Market cap segments (Large, Mid, Small)
Sector relative strength
Risk factors (High Beta vs Low Volatility)
Credit conditions (High Yield vs Investment Grade)
The tool is particularly valuable for:
Identifying significant factor rotations in the market
Assessing market breadth through relative strength comparisons
Spotting potential trend changes through statistical deviation analysis
Monitoring sector leadership and market regime shifts
Quantifying the magnitude of market moves relative to historical norms
India VIXThe VIX chart represents the Volatility Index, commonly referred to as the "Fear Gauge" of the stock market. It measures the market's expectations of future volatility over the next 30 days, based on the implied volatility of NSE index options. The VIX is often used as an indicator of investor sentiment, reflecting the level of fear or uncertainty in the market.
Here’s a breakdown of what you might observe on a typical VIX chart:
VIX Value: The y-axis typically represents the VIX index value, with higher values indicating higher levels of expected market volatility (more fear or uncertainty), and lower values signaling calm or stable market conditions.
VIX Spikes: Large spikes in the VIX often correspond to market downturns or periods of heightened uncertainty, such as during financial crises or major geopolitical events. A high VIX is often associated with a drop in the stock market.
VIX Drops: A decline in the VIX indicates a reduction in expected market volatility, usually linked with periods of market calm or rising stock prices.
Trend Analysis: Technical traders might use moving averages or other indicators on the VIX chart to assess the potential for future market movements.
Inverse Relationship with the Stock Market: Typically, there is an inverse correlation between the VIX and the stock market. When stocks fall sharply, volatility increases, and the VIX tends to rise. Conversely, when the stock market rallies or remains stable, the VIX tends to fall.
A typical interpretation would be that when the VIX is low, the market is relatively stable, and when the VIX is high, the market is perceived to be uncertain or volatile.
Accumulated Funding RateAccumulated Funding Rate
for future contract -ve/+ve funding fees that indicate long and short opening so that price differance between Spot and Future is balance buy exchange funding between long and short holder
-ve rate means Short is high so short holder has to pay fees to Long to correction in Price and vise versa
so over the periode of time accumulated rate its indicates the Bubble which can be explode any time to Liquidation of inbalance Long/Short Ratio some time its take longer period but its indicated bubbles direction
maximum -ve rates indicate Short opened from long period of time so when its liquidate/exit
then price will be correct to its original price that was struck due Short holder over the time and then now market will liquidate/exit those unstable Short like 50X/25X leverage and correct the price
[blackcat] L2 Wave Base CampOVERVIEW
The L2 Wave Base Camp indicator is a technical analysis tool designed to identify trends and potential trading signals by visualizing price and volume data through moving averages and relative strength calculations. It operates in its own panel on the trading chart, providing traders with a clear and color-coded representation of market conditions.
FEATURES
Customizable Base Camp Level: Users can set a horizontal line at a specific level to mark significant price points.
Color-Coded Histograms: Different colors indicate various market conditions, such as price position relative to moving averages.
Labeled Signals: The indicator labels potential "Valley" and "Top" points, suggesting buying and selling opportunities.
Volume Analysis: Incorporates volume data to identify potential trend reversals based on volume trends.
HOW TO USE
Set the Base Camp Level: Adjust the input parameter to define a significant price level.
Interpret Histogram Colors: Use the color-coded histograms to understand the current market condition.
Look for Labeled Signals: Pay attention to "Valley" and "Top" labels for potential trading opportunities.
Analyze Volume Trends: Monitor volume data for signs of trend reversals.
LIMITATIONS
Not a Standalone Tool: Should be used in conjunction with other indicators and analysis methods.
Backtesting Required: Essential to understand historical performance before live trading.
NOTES
The indicator uses moving averages (SMA) and relative strength calculations to smooth data and identify trends.
Crossover events between different moving averages generate buy and sell signals.
THANKS
Special thanks to the original author for developing this insightful trading tool.
Imbalance Imbalance Detection:
Bullish Imbalance: Occurs when the high of the bar two periods ago is lower than the low of the current bar, indicating a possible reversal to the upside.
Bearish Imbalance: Occurs when the high of the current bar is lower than the low of the bar two periods ago, indicating a potential reversal to the downside.
Visual Highlighting:
The script changes the color of the previous bar (one bar before the imbalance) based on whether a bullish or bearish imbalance is detected.
Users can customize the color for both bullish and bearish imbalances to suit their preferences.
User Inputs:
Show Imbalance: Option to toggle the visibility of the imbalance signals on the chart.
Color Customization: Users can choose custom colors for bullish and bearish imbalances, allowing for easy distinction between the two types.
Alert Conditions:
The script includes pre-configured alert conditions for both bullish and bearish imbalances, notifying traders when an imbalance is detected, which can be useful for triggering trades or further analysis.
Quarter Shift IdentifierQuarter Shift Identifier
This indicator helps traders and analysts identify significant price movements between quarters. It calculates the percentage change from the close of the previous quarter to the current price and signals when this change exceeds a 4% threshold.
Key Features:
• Automatically detects quarter transitions
• Calculates quarter-to-quarter price changes
• Signals significant shifts when the change exceeds 4%
• Displays blue up arrows for bullish shifts and red down arrows for bearish shifts
How it works:
1. The script tracks the closing price of each quarter
2. When a new quarter begins, it calculates the percentage change from the previous quarter's close
3. If the change exceeds 4%, an arrow is plotted on the chart
This tool can be useful for:
• Identifying potential trend changes at quarter boundaries
• Analyzing seasonal patterns in price movements
• Supplementing other technical analysis tools for a comprehensive market view
Recommended Timeframes are Weekly and Daily.
Disclaimer:
This indicator is for informational and educational purposes only. It is not financial advice and should not be the sole basis for any investment decisions. Always conduct your own research and consider your personal financial situation before trading or investing. Past performance does not guarantee future results.
Bitcoin 1H-15M Breakout StrategyKey Features
1H and 15M Timeframes:
The script uses the 1-hour timeframe for the range and 15-minute timeframe for breakout conditions.
request.security is used to fetch the higher timeframe data.
Risk Management:
Variables entry_price, sl_price, and tp_price are declared explicitly as float with na initialization to handle dynamic assignment.
Stop-loss and take-profit levels are calculated based on the specified Risk-Reward Ratio (RRR) and buffer (in pips).
Trade Logic:
Long trade triggered when the 15-minute candle closes above the 1-hour high.
Short trade triggered when the 15-minute candle closes below the 1-hour low.
Visualization:
The range_high and range_low (previous 1-hour high and low) are plotted on the chart using dashed lines.
Debugging:
Enabling the show_debug input displays labels showing stop-loss and take-profit values for easier troubleshooting.
Combined Multi-Timeframe EMA OscillatorThis script aims to visualize the strength of bullish or bearish trends by utilizing a mix of 200 EMA across multiple timeframes. I've observed that when the multi-timeframe 200 EMA ribbon is aligned and expanding, the uptrend usually lasts longer and is safer to enter at a pullback for trend continuation. Similarly, when the bands are expanding in reverse order, the downtrend holds longer, making it easier to sell the pullbacks.
In this script, I apply a purely empirical and experimental method: a) Ranking the position of each of the above EMAs and turning it into an oscillator. b) Taking each 200 EMA on separate timeframes, turning it into a stochastic-like oscillator, and then averaging them to compute an overall stochastic.
To filter a bullish signal, I use the bullish crossover between these two aggregated oscillators (default: yellow and blue on the chart) which also plots a green shadow area on the screen and I look for buy opportunities/ ignore sell opportunities while this signal is bullish. Similarly, a bearish crossover gives us a bearish signal which also plots a red shadow area on the screen and I only look for sell opportunities/ ignore any buy opportunities while this signal is bearish.
Note that directly buying the signal as it prints can lead to suboptimal entries. The idea behind the above is that these crossovers point on average to a stronger trend; however, a trade should be initiated on the pullbacks with confirmation from momentum and volume indicators and in confluence with key areas of support and resistance and risk management should be used in order to protect your position.
Disclaimer: This script does not constitute certified financial advice, the current work is purely experimental, use at your own discretion.
Neural Network Proxy Strategy Alt by NHBprodHey, this is a trading strategy I’ve been working on. It uses a combination of three technical indicators: Bollinger Bands (to measure price volatility), Average True Range (ATR, to gauge price movement range), and Chaikin Money Flow (CMF, to check the flow of money in and out of an asset). The script normalizes each of these indicators which is essentially a simplified version of machine learning to create a single combined score, which is kind of like a neural network proxy. If this score goes above 0.5, it signals a potential buy, and if it goes below -0.5, it signals a potential sell. It’s pretty cool because you can tweak the weights of each indicator to suit different market conditions. It even plots the combined score on the chart to help visualize the signals!
This strategy is built for Bitcoin specifically, and it's applied on the 3 hour chart. Check out the results yourself. If you traded this strategy using Long only, then it yielded a staggering ~3% per trade, and there are hundreds of trades in this dataset!
Commission and slippage are included by the way!
If you want to trade this strategy in real time, I also have a pairing indicator script, and you can easily right click on the chart to create a 'buy' alert or a 'sell' alert that can be sent directly to your phone, or email. You can also set it up so that it sends a message to your trading broker so that it automatically purchases and sells based on this strategy. If you'd like help setting that up, let me know!
Pivot Points High Low - JVersion**Indicator Name**: Pivot Points High Low (Without Price Labels)
**Overview**
The Pivot Points High Low indicator is designed to identify and mark local highs and lows (or “pivot” points) on a price chart. Unlike other pivot-based indicators that label each pivot with its exact price, this version displays only small circular markers—removing clutter and focusing attention on the pivot locations themselves.
**Key Features**
1. **Pivot Detection**
- The script uses TradingView’s built-in `ta.pivothigh()` and `ta.pivotlow()` functions to determine when the market has formed a pivot high or pivot low.
- You can define how many bars to the left and right are required to confirm a pivot, helping you tailor the indicator to different market conditions and timeframes.
2. **Clean Markers**
- Each confirmed pivot high or low is represented by a circle placed precisely on the candle where the pivot is detected.
- No numeric labels are shown, keeping your chart visually uncluttered while still highlighting important turning points in price.
3. **Customization**
- **Left/Right Pivot Length**: Choose how many bars to the left and right must be lower (for highs) or higher (for lows) to validate a pivot. Larger values mean fewer but more significant pivots; smaller values mean more frequent pivots.
- **Marker Colors**: Independently customize the colors of the high-marker circles and low-marker circles to easily distinguish between local tops and bottoms.
4. **Usage and Interpretation**
- **Identifying Reversals**: As soon as a circle appears at a local high or low, it may indicate a short-term trend reversal or the beginning of a new swing in price.
- **Combine with Other Tools**: Pivot points are more informative when used alongside broader trend analysis, support/resistance identification, or other momentum indicators.
- **Adjusting Sensitivity**: By increasing or decreasing the left/right pivot lengths, you can make the indicator more or less sensitive to small market fluctuations.
5. **Practical Tips**
- **Swing Trading**: Shorter lengths can be used by swing traders looking for quick reversals in lower timeframes.
- **Longer-Term Trends**: Larger lengths are better for position traders or those who prefer to see only major turning points in the market.
- **Clean Chart Layout**: Because text labels are removed, you can visually focus on the circles—especially helpful if you use multiple indicators and prefer a less cluttered chart.
---
By pinpointing local highs and lows without price labels, the **Pivot Points High Low** indicator keeps charts neat yet informative, allowing traders to quickly recognize potential turning points in the market and make more informed decisions.
TTZConcept GOLD XAUUSD Lot CalculatorThe Gold Lot Size Calculator for XAU/USD on TradingView is a powerful and user-friendly tool designed by TTZ Concept to help traders calculate the optimal lot size for their Gold trades based on their account size, risk tolerance, and the price movement of Gold (XAU/USD). Whether you're a beginner or an experienced trader, this tool simplifies position sizing, ensuring that your trades align with your risk management strategy.
Key Features:
Accurate Lot Size Calculation: Calculates the optimal lot size for XAU/USD trades based on your specified account balance and the percentage of risk per trade.
Flexible Risk Management**: Input your desired risk percentage (e.g., 1%, 2%) to ensure that you are not risking more than you're comfortable with on any single trade.
Customizable Inputs: Enter your account balance, risk percentage, stop loss (in pips), and leverage to get an accurate lot size recommendation.
Real-Time Data The tool uses real-time Gold price data to calculate the position size, ensuring that your risk management is always up to date with market conditions.
-Simple Interface: With easy-to-use sliders and input fields, you can quickly adjust your parameters and get the required lot size in seconds.
No Complicated Calculations Automatically factors in the pip value and contract specifications for XAU/USD, eliminating the need for manual calculations.
How It Works:
1. Input your trading account balance: The tool calculates based on your total equity.
2. Set your risk percentage: Choose how much of your account you want to risk on a single trade.
3. Define your stop loss in pips: Specify the distance of your stop loss from the entry point.
4. Get your recommended lot size: Based on your inputs, the tool will calculate the ideal lot size for your trade.
Why Use This Tool?
Precise Risk Management: Take control of your trading risk by ensuring that each trade is positioned according to your risk tolerance.
Save Time: No need for manual calculations — let the calculator handle the complex math and focus on your strategy.
Adapt to Changing Market Conditions: As the price of Gold (XAU/USD) fluctuates, your lot size adapts to ensure consistent risk management across different market conditions.
Perfect for:
- Gold traders (XAU/USD)
- Beginners seeking to understand position sizing and risk management
- Experienced traders looking to streamline their trading process
- Anyone who trades Gold futures, CFDs, or spot Gold in their trading account
Williams %R IntensityOverview
"Williams %R Intensity" is a unique indicator that combines the classic Williams %R with a dynamic intensity-based visualization. This indicator helps traders identify overbought and oversold conditions with enhanced clarity while also predicting potential future crossovers using smoothed slope calculations. It is tailored for traders seeking a more nuanced approach to trend detection and momentum analysis.
Features and How It Works
Core Calculation:
Williams %R : Measures the current closing price relative to the highest high and lowest low over a user-defined length (default: 14).
Exponential Moving Average (EMA) : Smoothens the %R values for better trend tracking (default length: 14).
Overbought/Oversold Zones :
Upper and lower threshold levels are set at -20 (overbought) and -80 (oversold), making it easier to identify extreme conditions.
Intensity Visualization:
The intensity is calculated based on the absolute distance between Williams %R and its EMA.
The closer the value is to extreme levels, the more pronounced the visual intensity, capping at 90% transparency.
Overbought conditions are highlighted in red; oversold conditions in teal.
Crossover Signals:
Bullish Cross: When Williams %R crosses above its EMA in the oversold zone.
Bearish Cross: When Williams %R crosses below its EMA in the overbought zone.
The background color changes (lime for bullish, red for bearish) to highlight these critical moments when enabled via the "Show Cross & Predicted Cross Signal" option.
Future Cross Prediction:
Uses the smoothed slope of %R to estimate future values over a customizable number of steps.
Predicts potential bullish or bearish crosses based on the interaction between the predicted Williams %R and EMA.
Light green and light red background colors indicate predicted bullish and bearish crosses, respectively.
How to Use
Trend Detection: Use the Williams %R and its EMA to identify ongoing trends and confirm their strength.
Overbought/Oversold Analysis: Pay attention to crosses in extreme zones (-20 and -80) for potential reversals.
Intensity-Based Filtering: The intensity visualization helps to focus on the most significant conditions, reducing noise.
Cross Prediction: Enable "Show Cross & Predicted Cross Signal" to anticipate future turning points and plan trades proactively.
Example Applications
Scalping: Monitor rapid crossovers in lower timeframes for quick entries and exits.
Swing Trading: Use the overbought/oversold zones and cross predictions to identify longer-term reversal opportunities.
Risk Management: The intensity visualization can be used to filter out weak signals, ensuring higher-quality trade setups.
Chart Information
For clarity and compliance with publishing standards:
The chart should display the full symbol, timeframe, and the script name ("Williams %R Intensity").
Ensure the indicator is visible and properly configured for the chart.
Accurate Bollinger Bands mcbw_ [True Volatility Distribution]The Bollinger Bands have become a very important technical tool for discretionary and algorithmic traders alike over the last decades. It was designed to give traders an edge on the markets by setting probabilistic values to different levels of volatility. However, some of the assumptions that go into its calculations make it unusable for traders who want to get a correct understanding of the volatility that the bands are trying to be used for. Let's go through what the Bollinger Bands are said to show, how their calculations work, the problems in the calculations, and how the current indicator I am presenting today fixes these.
--> If you just want to know how the settings work then skip straight to the end or click on the little (i) symbol next to the values in the indicator settings window when its on your chart <--
--------------------------- What Are Bollinger Bands ---------------------------
The Bollinger Bands were formed in the 1980's, a time when many retail traders interacted with their symbols via physically printed charts and computer memory for personal computer memory was measured in Kb (about a factor of 1 million smaller than today). Bollinger Bands are designed to help a trader or algorithm see the likelihood of price expanding outside of its typical range, the further the lines are from the current price implies the less often they will get hit. With a hands on understanding many strategies use these levels for designated levels of breakout trades or to assist in defining price ranges.
--------------------------- How Bollinger Bands Work ---------------------------
The calculations that go into Bollinger Bands are rather simple. There is a moving average that centers the indicator and an equidistant top band and bottom band are drawn at a fixed width away. The moving average is just a typical moving average (or common variant) that tracks the price action, while the distance to the top and bottom bands is a direct function of recent price volatility. The way that the distance to the bands is calculated is inspired by formulas from statistics. The standard deviation is taken from the candles that go into the moving average and then this is multiplied by a user defined value to set the bands position, I will call this value 'the multiple'. When discussing Bollinger Bands, that trading community at large normally discusses 'the multiple' as a multiplier of the standard deviation as it applies to a normal distribution (gaußian probability). On a normal distribution the number of standard deviations away (which trades directly use as 'the multiple') you are directly corresponds to how likely/unlikely something is to happen:
1 standard deviation equals 68.3%, meaning that the price should stay inside the 1 standard deviation 68.3% of the time and be outside of it 31.7% of the time;
2 standard deviation equals 95.5%, meaning that the price should stay inside the 2 standard deviation 95.5% of the time and be outside of it 4.5% of the time;
3 standard deviation equals 99.7%, meaning that the price should stay inside the 3 standard deviation 99.7% of the time and be outside of it 0.3% of the time.
Therefore when traders set 'the multiple' to 2, they interpret this as meaning that price will not reach there 95.5% of the time.
---------------- The Problem With The Math of Bollinger Bands ----------------
In and of themselves the Bollinger Bands are a great tool, but they have become misconstrued with some incorrect sense of statistical meaning, when they should really just be taken at face value without any further interpretation or implication.
In order to explain this it is going to get a bit technical so I will give a little math background and try to simplify things. First let's review some statistics topics (distributions, percentiles, standard deviations) and then with that understanding explore the incorrect logic of how Bollinger Bands have been interpreted/employed.
---------------- Quick Stats Review ----------------
.
(If you are comfortable with statistics feel free to skip ahead to the next section)
.
-------- I: Probability distributions --------
When you have a lot of data it is helpful to see how many times different results appear in your dataset. To visualize this people use "histograms", which just shows how many times each element appears in the dataset by stacking each of the same elements on top of each other to form a graph. You may be familiar with the bell curve (also called the "normal distribution", which we will be calling it by). The normal distribution histogram looks like a big hump around zero and then drops off super quickly the further you get from it. This shape (the bell curve) is very nice because it has a lot of very nifty mathematical properties and seems to show up in nature all the time. Since it pops up in so many places, society has developed many different shortcuts related to it that speed up all kinds of calculations, including the shortcut that 1 standard deviation = 68.3%, 2 standard deviations = 95.5%, and 3 standard deviations = 99.7% (these only apply to the normal distribution). Despite how handy the normal distribution is and all the shortcuts we have for it are, and how much it shows up in the natural world, there is nothing that forces your specific dataset to look like it. In fact, your data can actually have any possible shape. As we will explore later, economic and financial datasets *rarely* follow the normal distribution.
-------- II: Percentiles --------
After you have made the histogram of your dataset you have built the "probability distribution" of your own dataset that is specific to all the data you have collected. There is a whole complicated framework for how to accurately calculate percentiles but we will dramatically simplify it for our use. The 'percentile' in our case is just the number of data points we are away from the "middle" of the data set (normally just 0). Lets say I took the difference of the daily close of a symbol for the last two weeks, green candles would be positive and red would be negative. In this example my dataset of day by day closing price difference is:
week 1:
week 2:
sorting all of these value into a single dataset I have:
I can separate the positive and negative returns and explore their distributions separately:
negative return distribution =
positive return distribution =
Taking the 25th% percentile of these would just be taking the value that is 25% towards the end of the end of these returns. Or akin the 100%th percentile would just be taking the vale that is 100% at the end of those:
negative return distribution (50%) = -5
positive return distribution (50%) = +4
negative return distribution (100%) = -10
positive return distribution (100%) = +20
Or instead of separating the positive and negative returns we can also look at all of the differences in the daily close as just pure price movement and not account for the direction, in this case we would pool all of the data together by ignoring the negative signs of the negative reruns
combined return distribution =
In this case the 50%th and 100%th percentile of the combined return distribution would be:
combined return distribution (50%) = 4
combined return distribution (100%) = 10
Sometimes taking the positive and negative distributions separately is better than pooling them into a combined distribution for some purposes. Other times the combined distribution is better.
Most financial data has very different distributions for negative returns and positive returns. This is encapsulated in sayings like "Price takes the stairs up and the elevator down".
-------- III: Standard Deviation --------
The formula for the standard deviation (refereed to here by its shorthand 'STDEV') can be intimidating, but going through each of its elements will illuminate what it does. The formula for STDEV is equal to:
square root ( (sum ) / N )
Going back the the dataset that you might have, the variables in the formula above are:
'mean' is the average of your entire dataset
'x' is just representative of a single point in your dataset (one point at a time)
'N' is the total number of things in your dataset.
Going back to the STDEV formula above we can see how each part of it works. Starting with the '(x - mean)' part. What this does is it takes every single point of the dataset and measure how far away it is from the mean of the entire dataset. Taking this value to the power of two: '(x - mean) ^ 2', means that points that are very far away from the dataset mean get 'penalized' twice as much. Points that are very close to the dataset mean are not impacted as much. In practice, this would mean that if your dataset had a bunch of values that were in a wide range but always stayed in that range, this value ('(x - mean) ^ 2') would end up being small. On the other hand, if your dataset was full of the exact same number, but had a couple outliers very far away, this would have a much larger value since the square par of '(x - mean) ^ 2' make them grow massive. Now including the sum part of 'sum ', this just adds up all the of the squared distanced from the dataset mean. Then this is divided by the number of values in the dataset ('N'), and then the square root of that value is taken.
There is nothing inherently special or definitive about the STDEV formula, it is just a tool with extremely widespread use and adoption. As we saw here, all the STDEV formula is really doing is measuring the intensity of the outliers.
--------------------------- Flaws of Bollinger Bands ---------------------------
The largest problem with Bollinger Bands is the assumption that price has a normal distribution. This is assumption is massively incorrect for many reasons that I will try to encapsulate into two points:
Price return do not follow a normal distribution, every single symbol on every single timeframe has is own unique distribution that is specific to only itself. Therefore all the tools, shortcuts, and ideas that we use for normal distributions do not apply to price returns, and since they do not apply here they should not be used. A more general approach is needed that allows each specific symbol on every specific timeframe to be treated uniquely.
The distributions of price returns on the positive and negative side are almost never the same. A more general approach is needed that allows positive and negative returns to be calculated separately.
In addition to the issues of the normal distribution assumption, the standard deviation formula (as shown above in the quick stats review) is essentially just a tame measurement of outliers (a more aggressive form of outlier measurement might be taking the differences to the power of 3 rather than 2). Despite this being a bit of a philosophical question, does the measurement of outlier intensity as defined by the STDEV formula really measure what we want to know as traders when we're experiencing volatility? Or would adjustments to that formula better reflect what we *experience* as volatility when we are actively trading? This is an open ended question that I will leave here, but I wanted to pose this question because it is a key part of what how the Bollinger Bands work that we all assume as a given.
Circling back on the normal distribution assumption, the standard deviation formula used in the calculation of the bands only encompasses the deviation of the candles that go into the moving average and have no knowledge of the historical price action. Therefore the level of the bands may not really reflect how the price action behaves over a longer period of time.
------------ Delivering Factually Accurate Data That Traders Need------------
In light of the problems identified above, this indicator fixes all of these issue and delivers statistically correct information that discretionary and algorithmic traders can use, with truly accurate probabilities. It takes the price action of the last 2,000 candles and builds a huge dataset of distributions that you can directly select your percentiles from. It also allows you to have the positive and negative distributions calculated separately, or if you would like, you can pool all of them together in a combined distribution. In addition to this, there is a wide selection of moving averages directly available in the indicator to choose from.
Hedge funds, quant shops, algo prop firms, and advanced mechanical groups all employ the true return distributions in their work. Now you have access to the same type of data with this indicator, wherein it's doing all the lifting for you.
------------------------------ Indicator Settings ------------------------------
.
---- Moving average ----
Select the type of moving average you would like and its length
---- Bands ----
The percentiles that you enter here will be pulled directly from the return distribution of the last 2,000 candles. With the typical Bollinger Bands, traders would select 2 standard deviations and incorrectly think that the levels it highlights are the 95.5% levels. Now, if you want the true 95.5% level, you can just enter 95.5 into the percentile value here. Each of the three available bands takes the true percentile you enter here.
---- Separate Positive & Negative Distributions----
If this box is checked the positive and negative distributions are treated indecently, completely separate from each other. You will see that the width of the top and bottom bands will be different for each of the percentiles you enter.
If this box is unchecked then all the negative and positive distributions are pooled together. You will notice that the width of the top and bottom bands will be the exact same.
---- Distribution Size ----
This is the number of candles that the price return is calculated over. EG: to collect the price return over the last 33 candles, the difference of price from now to 33 candles ago is calculated for the last 2,000 candles, to build a return distribution of 2000 points of price differences over 33 candles.
NEGATIVE NUMBERS(<0) == exact number of candles to include;
EG: setting this value to -20 will always collect volatility distributions of 20 candles
POSITIVE NUMBERS(>0) == number of candles to include as a multiple of the Moving Average Length value set above;
EG: if the Moving Average Length value is set to 22, setting this value to 2 will use the last 22*2 = 44 candles for the collection of volatility distributions
MORE candles being include will generally make the bands WIDER and their size will change SLOWER over time.
I wish you focus, dedication, and earnest success on your journey.
Happy trading :)
Onky's DikFat Supreme Supply and Demand Onky's DikFat Supreme Supply and Demand is an essential tool for traders looking to harness the power of Supply and Demand Trading , a strategy based on the fundamental market principle that prices increase when demand exceeds supply and decrease when supply surpasses demand. This indicator helps you pinpoint key Supply and Demand Zones on the chart, acting as high-probability areas for potential market reversals.
Introduction to Supply and Demand Trading
Supply and demand trading is one of the most powerful approaches used by traders across all financial markets, from stocks to forex to commodities. It works on the idea that prices will naturally rise when there is more demand than supply, and fall when there is more supply than demand. Understanding where these zones lie on the chart is critical for making profitable trades. By identifying key support and resistance levels driven by these forces, traders can anticipate price movements with high accuracy.
Benefits of Using Supply & Demand Trading:
Simple Trading Approach : Focus on market structure rather than complex indicators.
High-Probability Trading Setups : Recognize zones where price is likely to reverse.
Minimal Indicators Required : The strategy works on pure price action.
Works Across All Markets : Supply and demand principles apply to stocks, forex, and commodities.
High Accuracy : When implemented correctly, it offers a high degree of precision.
Whether you are just starting or looking to refine your strategy, understanding how to identify supply and demand zones can greatly improve your trading decisions. Here’s how you can begin:
Step 1: Identify Supply and Demand Zones
Before entering trades, it's essential to first identify the Supply and Demand Zones on your chart. These zones act as key support and resistance levels where price is likely to reverse.
Supply Zone : This represents an area where selling pressure exceeds buying pressure, causing the price to drop.
Demand Zone : This marks an area where buying pressure exceeds selling pressure, driving the price upwards.
These zones are crucial for spotting potential turning points in the market. Using Onky's DikFat Supreme Supply and Demand indicator, supply and demand zones are automatically detected, helping you to identify these key levels with ease. The indicator highlights these zones with specific color coding, allowing you to quickly see where price might reverse based on historical price action.
Step 2: Confirm Your Entry and Exit
Once you've identified the supply and demand zones, confirmation is key before entering any trades.
Entry Confirmation :
Look for additional technical indicators and patterns that signal a strong trade setup:
Candlestick Patterns : Bullish engulfing, Piercing Line, and other reversal patterns.
Chart Patterns : Double bottom, Head and Shoulders, and other formations that suggest a market shift.
Momentum Indicators : Use tools like MACD and RSI to confirm the strength of the trend.
Exit Confirmation :
Plan your exits with discipline to maximize your profits and minimize losses:
Stop Loss : Always place stop losses just outside of the supply or demand zone.
Exit Strategies :
Close part of the position at 2x risk and move stop loss to breakeven.
Trail stops below the previous support or resistance levels.
Close the full position using reversal candlestick patterns.
Step 3: Use Effective Risk Management
Incorporating effective Risk Management practices is essential for long-term success in supply and demand trading. Even with a high-probability edge, managing your risk ensures that you protect your capital and make more informed decisions.
Risk Management Best Practices :
Risk 1%-3% Per Trade : For a $10,000 account, risk only $100-$300 per trade.
Position Sizing : Stick to position sizes appropriate for your account size to manage risk effectively.
Set Stop Loss Orders : Always manage your risk with clearly defined stop losses.
Control Emotions : Avoid overtrading, revenge trading, and excessive confidence. Stick to your plan.
By combining supply and demand zones with solid risk management, you can confidently trade the markets and grow your account over time.
Start Applying Supply and Demand
Now that you understand the basics, you can begin applying Supply and Demand trading using the Onky's DikFat Supreme Supply and Demand indicator to detect key zones and high-probability setups. Here’s how to start:
Identify Fresh Supply and Demand Levels : Use the indicator to automatically find the most relevant zones.
Confirm Setups with Additional Signals : Use candlestick patterns, momentum indicators, and chart patterns for entry confirmation.
Manage Risk on Every Trade : Always use proper risk management to ensure you’re protecting your capital.
As you become more proficient in identifying and trading these zones, you will enhance your trading strategy and improve your consistency. Implementing these practices early on will help you grow as a trader and achieve long-term success.
Additional Resources
Price Action and Supply and Demand : A deeper dive into how price action complements supply and demand analysis.
Supply and Demand Trading - The Ultimate Guide : A comprehensive guide to mastering supply and demand trading techniques.
Advanced Supply and Demand Zones : Learn to identify more complex supply and demand zones for greater trading precision.
With the right education, dedication, and a focus on proper risk management, you can successfully trade based on supply and demand principles, no matter your experience level.
3 Candle AlertThis is a test for integration using a webhook. I am publishing it so I can share it. Ultimately, this is what we want to do:
1. Trade Entry Rules:
Wait until at least the 3rd bar of the day (15 minutes after market open) before entering the first trade.
Order of Priority for Entry:
Look for two consecutive volume bars of the same color (the second bar must have higher volume than the first).
Look for a “price push” beyond the high or low of the day (as determined in the first 15 minutes).
2. Trading Direction:
If the volume bars are RED, I take a Long Position.
If the volume bars are GREEN, I take a Short Position.
Quasimodo PatternWhat is a Quasimodo Pattern?
A Quasimodo Pattern is a chart pattern traders look for to predict possible price reversals in the market:
- Bullish Quasimodo: Signals a possible price increase (buying opportunity).
- Bearish Quasimodo: Signals a potential price decrease (selling opportunity).
How the Script Works
1. Bullish Quasimodo:
- Checks if the price pattern shows signs of a potential upward movement:
- The current low price is higher than a previous price point (suggesting fair value gap).
- The previous candle closed higher than it opened (bullish candle).
- The candle before that closed lower than it opened (bearish candle).
2. Bearish Quasimodo:
- Looks for signs of a downward movement:
- The current high price is lower than a previous price point (suggesting fair value gap).
- The previous candle closed lower than it opened (bearish candle).
- The candle before that closed higher than it opened (bullish candle).
Visual Indicators
- Yellow Candles: Indicate a bullish Quasimodo pattern.
- Pink Candles: Indicate a bearish Quasimodo pattern.
Alerts
If a Quasimodo pattern is detected, the script sends an alert:
- The alert says: "A Quasimodo Pattern has appeared!"
Purpose
Traders can use this tool to quickly spot potential trend changes without manually analyzing every chart, saving time and improving decision-making for trades.
It Screams When Crypto BottomsGet ready to ride the crypto rollercoaster with your new favourite tool for catching Bitcoin at its juiciest, most oversold moments.
This isn’t just another boring indicator — it screams when it’s time to load your bags and get ready for the ride back up!
Expect it to scream just once or twice per cycle at the very bottom, so you know exactly when the party starts!
Why You'll Love It:
Crypto-Exclusive Magic: It does not really matter what chart you are on; this indicator only bothers about the original and realised market cap of BTC. We all know the rest will follow.
Big Picture Focus: Designed for daily. No noisy intraday drama — just pure, clear signals.
Screaming Alerts: When the signal hits, it’s like a neon sign screaming, “Crypto Bottomed!"
Think of this indicator as your backstage pass to the crypto world’s most dramatic moments. It’s not subtle — it’s bold, loud, and ready to help you time the market like a pro.
P.S.: Use it only on a daily chart. Don’t even try it on shorter timeframes — it won’t scream, and you’ll miss the show! 🙀
GANN Level (Salil Sir)GANN Level Indicator Description
This Pine Script calculates and plots Gann Levels based on a user-defined price input. It creates horizontal lines at key support and resistance levels derived from the input price, applying Gann's theory of market structure. The levels are dynamically calculated and squared for enhanced precision.
Key Features:
Manual Price Input:
The user inputs a round off of square root of base price (Manual_Input), which serves as the foundation for calculations.
Support and Resistance Levels:
Six resistance levels (R1 to R6) and six support levels (S1 to S6) are calculated by incrementing or decrementing the base price in steps of 0.25.
Squared Levels:
Each level is squared (level^2) to align with Gann's mathematical principles.
Visualization:
All levels, including the base price squared (GANN), are plotted as horizontal dotted lines:
Black Line: Base price squared (Gann Level).
Green Lines: Resistance levels.
Red Lines: Support levels.
Purpose:
The indicator helps traders identify potential support and resistance zones based on Gann's methodology, providing a mathematical framework for decision-making.
Usage:
Adjust the Manual Price in the settings to the desired value.
Observe the plotted levels for key support and resistance zones on the chart.
Use these levels to make informed trading decisions or to validate other indicators.
