Ultimate Precision Buy/Sell Signals - Advanced Edition V1.01Ultimate Precision Buy/Sell Signals - Advanced Edition V1.01
Overview
This powerful TradingView indicator is designed for maximum precision in trading by utilizing a combination of EMA, RSI, ATR, ADX, Fibonacci extensions, and divergence detection to identify optimal buy and sell opportunities. The script is designed to help traders maximize profits while minimizing risk with advanced risk calculations and stop-loss mechanisms.
How to Use the Indicator
1. Buy and Sell Signals
BUY Signal (Green Label Below Candles):
RSI crosses above 30 (indicating an oversold recovery).
Price is below the EMA (indicating undervaluation).
ADX confirms a strong trend (if enabled).
📌 Action: Open a long position and set a stop-loss based on the risk level.
SELL Signal (Red Label Above Candles):
RSI crosses below 70 (indicating an overbought condition).
Price is above the EMA (indicating overextension).
ADX confirms a strong trend (if enabled).
📌 Action: Open a short position and set a stop-loss based on the risk level.
2. Risk and Profit Calculations
Risk Level is determined using ATR × Risk Multiplier.
Potential Profit is calculated as Risk Level × Risk-Reward Ratio.
These values are displayed in a floating label to help manage your trades efficiently.
📌 Tip: A higher Risk Multiplier means wider stop-losses, useful for volatile markets.
3. Trend Confirmation (ADX Filter)
If trend filtering is enabled, buy/sell signals will only appear in strong trends.
ADX must be above 25 to confirm that the market is trending.
📌 Tip: Use this to avoid false signals in ranging markets.
4. Divergence Detection
Bullish Divergence (Blue Circle Below Candles): Indicates a potential reversal upwards.
Bearish Divergence (Orange Circle Above Candles): Indicates a potential downward reversal.
📌 Tip: Divergences provide early warnings before market reversals.
5. Heatmap Visualization
Green Background: Indicates a high-confidence buy zone.
Red Background: Indicates a high-confidence sell zone.
📌 Tip: Use heatmaps to spot high-conviction trades.
6. Fibonacci-Based Take Profit (Optional)
If enabled, the script will use Fibonacci extensions instead of fixed risk-reward levels.
📌 Tip: Fibonacci-based TP works well in trending markets.
7. Alerts (No Need to Stare at the Charts!)
Set alerts for BUY and SELL signals.
TradingView will notify you when a perfect trade setup appears.
📌 Tip: Enable alerts in TradingView settings.
Socials & Live Stream Promotion 🚀
If you love this indicator and want to see it in action, check out my live trading sessions and content here:
🎥 YouTube: www.YouTube.com
🎮 Kick: www.Kick.com
🎥 Twitch: twitch.tv
👀 Follow me for more trading insights, strategies, and live action!
Funny Trading Joke to Brighten Your Day 😆
💬 Trader: "I lost my whole account today!"💬 Friend: "What happened?!"💬 Trader: "Well... I kept seeing those green candles and thought they were money-growing trees. Turns out they were just market traps!"😂😂😂
🚀 Smash that FOLLOW button if this indicator helps your trading! Let’s dominate the markets together! 📈🔥
Statistics
Comprehensive Volume and Metrics with Pre-Market Volume Data
This script is designed for traders who want a detailed view of market activity, including regular market and pre-market volume, dollar volume, relative volume (RVOL), average daily range (ADR), average true range (ATR), relative strength index (RSI), and the QQQ’s percentage change.
The script includes customizable metrics displayed in tables on the chart for easy analysis, with the option to toggle the visibility of each metric.
Key Features:
Volume and Dollar Volume:
Displays the volume of shares traded during the current day (or pre-market, if enabled).
Includes a calculation of dollar volume, representing the total dollar amount of trades (Volume × Close Price).
Relative Volume (RVOL):
Displays RVOL Day, which is the relative volume of the current day compared to the 2-day moving average.
Shows RVOL 90D, indicating relative volume over the past 90 days.
Both RVOL metrics are calculated as percentages and display the percentage change compared to the standard (100%).
Pre-Market Data:
Includes pre-market volume (PVOL) and pre-market dollar volume (P$ VOL) which are displayed only if pre-market data is enabled.
Tracks volume and dollar volume during pre-market hours (4:00 AM to 9:30 AM Eastern Time) for more in-depth analysis.
Optionally, shows pre-market RSI based on volume-weighted close prices.
Average Daily Range (ADR):
Displays the percentage change between the highest and lowest prices over the defined ADR period (default is 20 days).
Average True Range (ATR):
Shows the ATR, a popular volatility indicator, for a given period (default is 14 bars).
RSI (Relative Strength Index):
Displays RSI for the given period (default is 14).
RSI is calculated using pre-market data when available.
QQQ:
Shows the percentage change of the QQQ ETF from the previous day’s close.
The QQQ percentage change is color-coded: green for positive, red for negative, and gray for no change.
Customizable Inputs:
Visibility Options: Toggle the visibility of each metric, such as volume, dollar volume, RVOL, ADR, ATR, RSI, and QQQ.
Pre-Market Data: Enable or disable the display of pre-market data for volume and dollar volume.
Table Positioning: Adjust the position of tables displaying the metrics either at the bottom-left or bottom-right of the chart.
Text Color and Table Background: Choose between white or black text for the tables and customize the background color.
Tables:
The script utilizes tables to display multiple metrics in an organized and easy-to-read format.
The values are updated dynamically, reflecting real-time data as the market moves.
Pre-Market Data:
The script calculates pre-market volume and dollar volume, along with other key metrics like RSI and RVOL, to help assess market sentiment before the market officially opens.
The pre-market data is accumulated from 4:00 AM to 9:30 AM ET, allowing for pre-market analysis and comparison to regular market hours.
User-Friendly and Flexible:
This script is designed to be highly customizable, giving you the ability to toggle which metrics to display and where they appear on the chart. You can easily focus on the data that matters most to your trading strategy.
Best Buffett Ratio w/ Std-Dev Offset + Conditional PlotSummary:
This script provides a visually clear way to track the so-called “Buffett Ratio,”
a popular market valuation gauge which compares the total US stock market cap
to the country’s GDP. In addition, it plots a “hardcoded” long-term trend line,
along with fixed standard-deviation bands (in log space), and uses background colors
to signal potentially overvalued or undervalued zones.
What Is the Buffett Ratio?
Often credited to Warren Buffett, the Buffett Ratio (or Buffett Indicator) measures:
(Total US Stock Market Capitalization) / (US GDP)
• A higher ratio typically means equities are more expensive relative to the size of the economy.
• A lower ratio suggests equities may be more attractively valued compared to GDP.
Historically, the ratio has tended to drift upward over many decades,
as the US economy and stock markets grow, but it still oscillates around some trend over time.
How to Use
1) Add to Chart:
- In TradingView, simply apply the indicator (it internally fetches CRSPTM1 & GDP data).
2) Tweak Inputs:
- Log Offset for 1σ: Adjust how wide the ±1σ/±2σ bands appear around the trend.
- Anchor Points: Edit startYear , endYear , startRatio , endRatio
if you want a different slope or different “fair value” anchors.
3) Interpretation:
- If the indicator is above +2σ (red line) , it’s historically “very expensive,”
often leading to lower future returns over the long term.
- If it’s below –2σ (green line) , it’s historically “deep undervaluation,”
often pointing to better future returns over time.
- The intermediate zones show degrees of mild over- or undervaluation.
How This Script Works
1) Buffett Ratio Calculation:
- The script requests data from TradingView’s built-in CRSPTM1 index (total US market cap).
- It also requests US GDP data via request.economic("US", "GDP") .
- If GDP data is missing, the ratio becomes na on that bar.
2) Hardcoded Trend Line:
- Rather than a rolling average, the script uses two “anchors” (e.g. 1950 → 0.30 ratio, 2024 → 1.25 ratio)
and solves for a single log-growth rate to produce a steady upward slope.
3) Fixed Standard Deviations in Log Space:
- The script takes the log of the trend line, then applies a fixed offset for ±1σ and ±2σ,
creating proportional bands that do not “expand/contract” from a rolling window.
4) Conditional Plotting:
- The script only begins plotting once the Buffett Ratio actually has data (around 2011).
5) Color-Coded Zones:
- Above +2σ: red background (historically very expensive)
- Between +1σ and +2σ: yellow background (moderately expensive)
- Between –1σ and +1σ: no background color (around normal)
- Between –2σ and –1σ: aqua background (moderately undervalued)
- Below –2σ: green background (historically deep undervaluation)
Final Notes
• Data Limitations: US GDP data and CRSPTM1 only go back so far, so this starts around 2011.
• Long-Term vs. Short-Term: Best viewed on monthly/quarterly charts and interpreted over years.
• Tuning: If you believe structural changes have shifted the ratio’s fair slope,
adjust the code’s anchors or log offsets.
Enjoy, and use responsibly!
DCA Simulation for CryptoCommunity v1.1Overview
This script provides a detailed simulation of a Dollar-Cost Averaging (DCA) strategy tailored for crypto traders. It allows users to visualize how their DCA strategy would perform historically under specific parameters. The script is designed to help traders understand the mechanics of DCA and how it influences average price movement, budget utilization, and trade outcomes.
Key Features:
Combines Interval and Safety Order DCA:
Interval DCA: Regular purchases based on predefined time intervals.
Safety Order DCA: Additional buys triggered by percentage price drops.
Interactive Visualization:
Displays buy levels, average price, and profit-taking points on the chart.
Allows traders to assess how their strategy adapts to price movements.
Comprehensive Dashboard:
Tracks money spent, contracts acquired, and budget utilization.
Shows maximum amounts used if profit-taking is active.
Dynamic Safety Orders:
Resets safety orders when a new higher high is established.
Customizable Parameters:
Adjustable buy frequency, safety order settings, and profit-taking levels.
Suitable for traders with varying budgets and risk tolerances.
Default Strategy Settings:
Account Size: Default account size is set to $10,000 to represent a realistic budget for the average trader.
Commission & Slippage: Includes realistic trading fees and slippage assumptions to ensure accurate backtesting results.
Risk Management: Defaults to risking no more than 5% of the account balance per trade.
Sample Size: Optimized to generate a minimum of 100 trades for meaningful statistical analysis. Users can adjust parameters to fit longer timeframes or different datasets.
Usage Instructions:
Configure Your Strategy: Set the base order, safety order size, and buy frequency based on your preferred DCA approach.
Analyze Historical Performance: Use the chart and dashboard to understand how the strategy performs under different market conditions.
Optimize Parameters: Adjust settings to align with your risk tolerance and trading objectives.
Important Notes:
This script is for educational and simulation purposes. It is not intended to provide financial advice or guarantee profitability.
If the strategy's default settings do not meet your needs, feel free to adjust them while keeping risk management in mind.
TradingView limits the number of open trades to 999, so reduce the buy frequency if necessary to fit longer timeframes.
Simplified HMM for Regime DetectionSimplified HMM made possible in PineScript
This is a very basic version of Hidden Markov Model used to detect market regimes. To not confuse with the real model.
There are 3 possible states:
Bull: Positive momentum and strong uptrend.
Bear: Negative momentum and strong downtrend.
Ranging: Weak momentum or indecisive market.
The table shows the current probability of convergence, aka. what's the probability that the market will stay in the same regime and the transition probability.
How to use?
This indicator doesn't generate entry signals and is best used to asses the current trend and trend strength. For more questions, don't hesitate to ask me.
Relative Strength Screener V3relative strength vs spy screener list. based on a set of stocks, higher number is more strength vs spy.
Countdown Candle RRS// Countdown Candle RRS Indicator
//
// This indicator displays a countdown timer for the current candle on the chart.
// It shows the remaining time until the current candle closes, providing traders
// with a visual reference for time-based decision making.
//
// Features:
// - Customizable countdown display (size, position, and color)
// - Adapts to different timeframes (daily, hourly, and minute-based)
// - Displays time in appropriate format based on the chart timeframe
// - Daily or higher: XdHH:MM:SS (e.g., 2d05:30:15)
// - Hourly: HH:MM:SS
// - Minute or lower: MM:SS
// - Updates in real-time on the last candle
//
// Usage:
// - Add this indicator to your chart to see the countdown timer
// - Use the input options to customize the appearance and position of the timer
// - The timer will update on each tick, showing the time remaining until the current candle closes
//
// Note: This indicator is particularly useful for traders who need precise timing
// for entry or exit decisions, especially in fast-moving markets or when using
// specific time-based strategies.
//
// Author: reza rashidi
// Version: 1.0
Price Move DetectorThe Price Move Detector is a powerful technical analysis tool that automatically detects and highlights significant price movements over a user-defined time frame. This indicator allows traders to quickly identify instances where an asset has experienced a large price change, making it easier to spot potential trading opportunities.
Key Features
Customizable Parameters: Adjust the percentage change and time period (bars or sessions) to define what qualifies as a "significant" price move.
Automatic Highlighting: The indicator overlays a background highlight on the chart whenever the price moves by the specified percentage within the chosen time period.
Flexible Time Frame: Use this indicator across various timeframes and adjust the settings to suit your trading strategy, such as detecting 100% price moves over 20 sessions.
Ideal for Historical Analysis: Perfect for backtesting and screening for past price surges, helping traders spot explosive price action and market trends.
Use Cases
Spot Potential Breakouts: Use the detector to identify stocks or assets that have made significant moves, potentially signaling the start of a breakout or new trend.
Quickly Identify Major Market Moves: Scan historical data to pinpoint times when an asset experienced substantial price changes, providing insight into past performance and future potential.
How to Use
Customize the Settings
Percentage Threshold: Set the minimum percentage increase (e.g., 50%, 100%) that qualifies as a significant move. You can experiment with different percentages to suit your analysis.
Time Period (Bars): Define the lookback period (in bars/sessions) over which the price move should be measured. For example, set it to 20 bars for a one-month time frame on a daily chart.
Analyze the Highlights
Whenever the price increases by the defined percentage over the set period, the indicator will highlight that section of the chart with a background color.
The highlighted sections will make it easy to identify historical periods of large price movements, which can be useful for spotting trends, potential breakouts, or other market behaviors.
Adjust the Parameters for Your Strategy
You can fine-tune the settings to detect smaller or larger price moves depending on your trading goals.
The indicator is flexible enough for use on different timeframes and assets, providing valuable insights across various markets.
EMA 14/25/55The indicator calculates three Exponential Moving Averages (EMAs) with periods of 14, 25, and 55. These EMAs are commonly used for analyzing short-term, medium-term, and long-term price trends. The plots are color-coded (red for 14, orange for 25, and aqua for 55) for easy differentiation, helping traders identify trend direction and potential crossovers for trading signals.
Señales EUR/USD Este script está diseñado para generar señales de compra (⬆️) y venta (⬇️) en el par EUR/USD
, combinando múltiples indicadores técnicos para asegurar alta precisión.
Korrelyatsiya Indikatori (US30, Nasdaq, S&P 500)by ig Toni_5123 for only corrilation us30 nasdaq and sps500
EMA 14/25/55The indicator calculates three Exponential Moving Averages (EMAs) with periods of 14, 25, and 55. These EMAs are commonly used for analyzing short-term, medium-term, and long-term price trends. The plots are color-coded (red for 14, orange for 25, and aqua for 55) for easy differentiation, helping traders identify trend direction and potential crossovers for trading signals.
Codi's Perp-Spot Basis# Perp-Spot Basis Indicator
This indicator calculates the percentage basis between perpetual futures and spot prices for crypto assets. It is inspired by the original concept from **Krugermacro**, with the added improvement of **automatic detection of the asset pairs** based on the current chart symbol. This enhancement makes it faster and easier to apply across different assets without manual configuration.
## How It Works
The indicator compares the perpetual futures price (e.g., `BTCUSDT.P`) to the spot price (e.g., `BTCUSDT`) on Binance. The difference is expressed as a percentage: (Perp - Spot) / Spot * 100
The results are displayed in a color-coded graph:
- **Blue (Positive Basis):** Perpetual futures are trading at a premium, indicating **bullish sentiment** among derivatives traders.
- **Red (Negative Basis):** Perpetual futures are trading at a discount, indicating **bearish sentiment** among derivatives traders.
This percentage basis is a core component in understanding funding rates and derivatives market dynamics. It serves as a faster proxy for funding rates, which typically lag behind real-time price movements.
---
## How to Use It
### General Concept
- **Red (Negative Basis):** Ideal to execute **longs** when derivatives traders are overly bearish.
- **Blue (Positive Basis):** Ideal to execute **shorts** when derivatives traders are overly bullish.
### Pullback Sniping
1. During an **uptrend**:
- If the basis turns **red** temporarily, it can signal an opportunity to **buy the dip**.
2. During a **downtrend**:
- If the basis turns **blue** temporarily, it can signal an opportunity to **sell the rip**.
3. Wait for the basis to **pop back** (higher in uptrend, lower in downtrend) to time entries more effectively—this often coincides with **stop runs** or **liquidations**.
### Intraday Execution
- **When price is falling**:
- If the basis is **red**, the move is derivatives-led (**normal**).
- If the basis is **blue**, spot traders are leading, and perps are offside—wait for **price dumps** before longing.
- **When price is rising**:
- If the basis is **blue**, the move is derivatives-led (**normal**).
- If the basis is **red**, spot traders are leading, and perps are offside—wait for **price pops** before shorting.
### Larger Time Frames
- **Consistently Blue Basis:** Indicates a **bull market** as derivatives traders are bullish over the long term.
- **Consistently Red Basis:** Indicates a **bear market** as derivatives traders are bearish over the long term.
---
## Improvements Over the Original
This version of the Perp-Spot Basis indicator **automatically detects the Binance perpetual futures and spot pairs** based on the current chart symbol. For example:
- If you are viewing `ETHUSDT`, it automatically references `ETHUSDT.P` for the perpetual futures pair and `ETHUSDT` for the spot pair in BINANCE.
Green/Red Candle Ratio - Ratiomizer V1.01🔥 Green/Red Candle Ratio Indicator 🔥
📊 Track Market Momentum Instantly!
🚀 Welcome to the Green/Red Candle Ratio Indicator! 🚀
This script dynamically calculates the ratio of green (bullish) vs. red (bearish) candles in your visible chart area, giving you a quick overview of market sentiment.
🛠 About This Script
⚡ Created with the help of ChatGPT – This is an early draft and open-source for everyone to use and improve!
⚠️ Short timeframe users (1min, 5min, etc.) – Be aware that too many candles can sometimes cause issues with calculations.
🔗 If you improve on this idea or use it in your own project, I’d love a little credit! 💙
📺 Follow My Trading Journey!
🎥 Live Trading, Insights & Market Analysis:
📌 YouTube → www.YouTube.com
📌 Kick → www.Kick.com
🙌 Support the content and join an awesome trading community!
💡 How to Use This Indicator
1️⃣ Apply it to your chart
2️⃣ See the real-time green/red candle ratio
3️⃣ Zoom in/out to dynamically adjust the calculations
4️⃣ Use it as an extra confirmation tool for trend momentum
💬 Feedback & improvements are welcome! If you run into issues or have ideas to make this script better, let me know in the community!
🔥 Happy Trading & Stay Profitable! 🔥 🏆
//@version=5
indicator("Green/Red Candle Ratio", overlay=true)
// Define the number of past bars to check (must be within TradingView limits)
maxBarsBack = math.min(500, bar_index) // Ensures we do not exceed available bars
// Initialize counters
greenCount = 0
redCount = 0
neutralCount = 0
totalBars = 0
// Loop through the last `maxBarsBack` bars
for i = 0 to maxBarsBack - 1
idx = bar_index - i
if idx >= 0
if close > open
greenCount := greenCount + 1
else if close < open
redCount := redCount + 1
else
neutralCount := neutralCount + 1
// Ensure only green + red candles contribute to the percentage calculation
validBars = greenCount + redCount
// Calculate ratios correctly so they always add up to 100%
greenRatio = validBars > 0 ? (greenCount / validBars) * 100 : na
redRatio = validBars > 0 ? (redCount / validBars) * 100 : na
// Create label text
labelText = "Green: " + str.tostring(greenRatio, "#.##") + "% Red: " + str.tostring(redRatio, "#.##")
labelColor = greenRatio > redRatio ? color.green : color.red
// Delete previous label before creating a new one
var label ratioLabel = na
if not na(ratioLabel)
label.delete(ratioLabel)
// Display only one label that updates dynamically
ratioLabel := label.new(bar_index, high, labelText, color=color.white, textcolor=labelColor, size=size.normal, style=label.style_label_down)
Basis SpreadBasis Spread monitor, calculating the spread of spot and perp futures. Currently works on Binance.
Mxwll Hedge Suite [Mxwll]Hello Traders!
The Mxwll Hedge Suite determines the best asset to hedge against the asset on your chart!
By determining correlation between the asset on your chart and a group of internally listed assets, the Mxwll Hedge Suite determines which asset from the list exhibits the highest negative correlation, and then determines exactly how many coins/shares/contracts of the asset must be bought to achieve a perfect 1:1 hedge!
The image above exemplifies the process!
The purple box on the chart shows the eligible price action used to determine correlation between the asset on my chart (BTCUSDT.P) and the list of cryptocurrencies that can be used as a hedge!
From this price action, the coin determined to have to greatest negative correlation to BTCUSDT.P is FTMUSD.
The image above further outlines the hedge table located in the bottom-right corner of your chart!
The hedge table shows exactly how many coins you’d need to purchase for the hedge asset at various leverages to achieve a perfect 1:1 hedge!
Hedge Suite works on any asset on any timeframe!
And that’s all! A short and sweet script that is hopefully helpful to traders looking to hedge their positions with a negatively correlated asset!
Thank you, Traders!
Mean Reversion Pro Strategy [tradeviZion]Mean Reversion Pro Strategy : User Guide
A mean reversion trading strategy for daily timeframe trading.
Introduction
Mean Reversion Pro Strategy is a technical trading system that operates on the daily timeframe. The strategy uses a dual Simple Moving Average (SMA) system combined with price range analysis to identify potential trading opportunities. It can be used on major indices and other markets with sufficient liquidity.
The strategy includes:
Trading System
Fast SMA for entry/exit points (5, 10, 15, 20 periods)
Slow SMA for trend reference (100, 200 periods)
Price range analysis (20% threshold)
Position management rules
Visual Elements
Gradient color indicators
Three themes (Dark/Light/Custom)
ATR-based visuals
Signal zones
Status Table
Current position information
Basic performance metrics
Strategy parameters
Optional messages
📊 Strategy Settings
Main Settings
Trading Mode
Options: Long Only, Short Only, Both
Default: Long Only
Position Size: 10% of equity
Starting Capital: $20,000
Moving Averages
Fast SMA: 5, 10, 15, or 20 periods
Slow SMA: 100 or 200 periods
Default: Fast=5, Slow=100
🎯 Entry and Exit Rules
Long Entry Conditions
All conditions must be met:
Price below Fast SMA
Price below 20% of current bar's range
Price above Slow SMA
No existing position
Short Entry Conditions
All conditions must be met:
Price above Fast SMA
Price above 80% of current bar's range
Price below Slow SMA
No existing position
Exit Rules
Long Positions
Exit when price crosses above Fast SMA
No fixed take-profit levels
No stop-loss (mean reversion approach)
Short Positions
Exit when price crosses below Fast SMA
No fixed take-profit levels
No stop-loss (mean reversion approach)
💼 Risk Management
Position Sizing
Default: 10% of equity per trade
Initial capital: $20,000
Commission: 0.01%
Slippage: 2 points
Maximum one position at a time
Risk Control
Use daily timeframe only
Avoid trading during major news events
Consider market conditions
Monitor overall exposure
📊 Performance Dashboard
The strategy includes a comprehensive status table displaying:
Strategy Parameters
Current SMA settings
Trading direction
Fast/Slow SMA ratio
Current Status
Active position (Flat/Long/Short)
Current price with color coding
Position status indicators
Performance Metrics
Net Profit (USD and %)
Win Rate with color grading
Profit Factor with thresholds
Maximum Drawdown percentage
Average Trade value
📱 Alert Settings
Entry Alerts
Long Entry (Buy Signal)
Short Entry (Sell Signal)
Exit Alerts
Long Exit (Take Profit)
Short Exit (Take Profit)
Alert Message Format
Strategy name
Signal type and direction
Current price
Fast SMA value
Slow SMA value
💡 Usage Tips
Consider starting with Long Only mode
Begin with default settings
Keep track of your trades
Review results regularly
Adjust settings as needed
Follow your trading plan
⚠️ Disclaimer
This strategy is for educational and informational purposes only. It is not financial advice. Always:
Conduct your own research
Test thoroughly before live trading
Use proper risk management
Consider your trading goals
Monitor market conditions
Never risk more than you can afford to lose
📋 Release Notes
14 January 2025
Added New Fast & Slow SMA Options:
Fibonacci-based periods: 8, 13, 21, 144, 233, 377
Additional period: 50
Complete Fast SMA options now: 5, 8, 10, 13, 15, 20, 21, 34, 50
Complete Slow SMA options now: 100, 144, 200, 233, 377
Bug Fixes:
Fixed Maximum Drawdown calculation in the performance table
Now using strategy.max_drawdown_percent for accurate DD reporting
Previous version showed incorrect DD values
Performance metrics now accurately reflect trading results
Performance Note:
Strategy tested with Fast/Slow SMA 13/377
Test conducted with 10% equity risk allocation
Daily Timeframe
For Beginners - How to Modify SMA Levels:
Find this line in the code:
fastLength = input.int(title="Fast SMA Length", defval=5, options= )
To add a new Fast SMA period: Add the number to the options list, e.g.,
To remove a Fast SMA period: Remove the number from the options list
For Slow SMA, find:
slowLength = input.int(title="Slow SMA Length", defval=100, options= )
Modify the options list the same way
⚠️ Note: Keep the periods that make sense for your trading timeframe
💡 Tip: Test any new combinations thoroughly before live trading
"Trade with Discipline, Manage Risk, Stay Consistent" - tradeviZion
IU Trailing Stop Loss MethodsThe 'IU Trailing Stop Loss Methods' it's a risk management tool which allows users to apply 12 trailing stop-loss (SL) methods for risk management of their trades and gives live alerts when the trailing Stop loss has hit. Below is a detailed explanation of each input and the working of the Script.
Main Inputs:
- bar_time: Specifies the date from which the trade begins and entry price will be the open of the first candle.
- entry_type: Choose between 'Long' or 'Short' positions.
- trailing_method: Select the trailing stop-loss method. Options include ATR, Parabolic SAR, Supertrend, Point/Pip based, Percentage, EMA, Highest/Lowest, Standard Deviation, and multiple target-based methods.
- exit_after_close: If checked, exits the trade only after the candle closes.
Optional Inputs:
ATR Settings:
- atr_Length: Length for the ATR calculation.
- atr_factor: ATR multiplier for SL calculation.
Parabolic SAR Settings:
- start, increment, maximum: Parameters for the Parabolic SAR indicator.
Supertrend Settings:
- supertrend_Length, supertrend_factor: Length and factor for the Supertrend indicator.
Point/Pip Based:
- point_base: Set trailing SL in points/pips.
Percentage Based:
- percentage_base: Set SL as a percentage of entry price.
EMA Settings:
- ema_Length: Length for EMA calculation.
Standard Deviation Settings:
- std_Length, std_factor: Length and factor for standard deviation calculation.
Highest/Lowest Settings:
- highest_lowest_Length: Length for the highest/lowest SL calculation.
Target-Based Inputs:
- ATR, Point, Percentage, and Standard Deviation based target SL settings with customizable lengths and multipliers.
Entry Logic:
- Trades initiate based on the entry_type selected and the specified bar_time.
- If Long is selected, a long trade is initiated when the conditions match, and vice versa for Short.
Trailing Stop-Loss (SL) Methods Explained:
The strategy dynamically adjusts stop-loss based on the chosen method. Each method has its calculation logic:
- ATR: Stop-loss calculated using ATR multiplied by a user-defined factor.
- Parabolic SAR: Uses the Parabolic SAR indicator for trailing stop-loss.
- Supertrend: Utilizes the Supertrend indicator as the stop-loss line.
- Point/Pip Based: Fixed point-based stop-loss.
- Percentage Based: SL set as a percentage of entry price.
- EMA: SL based on the Exponential Moving Average.
- Highest/Lowest: Uses the highest high or lowest low over a specified period.
- Standard Deviation: SL calculated using standard deviation.
Exit Conditions:
- If exit_after_close is enabled, the position will only close after the candle confirms the stop-loss hit.
- If exit_after_close is disabled, the strategy will close the trade immediately when the SL is breached.
Visualization:
The script plots the chosen trailing stop-loss method on the chart for easy visualization.
Target-Based Trailing SL Logic:
- When a position is opened, the strategy calculates the initial stop-loss and progressively adjusts it as the price moves favorably.
- Each SL adjustment is stored in an array for accurate tracking and visualization.
Alerts and Labels:
- When the Entry or trailing stop loss is hit this scripts draws a label and give alert to the user that trailing stop has been hit for the trade.
Note - on the historical data The Script will show nothing if the entry and the exit has happened on the same candle, because we don't know what was hit first SL or TP (basically how the candle was formed on the lower timeframe).
Summary:
This script offers flexible trailing stop-loss options for traders who want dynamic risk management in their strategies. By offering multiple methods like ATR, SAR, Supertrend, and EMA, it caters to various trading styles and risk preferences.
Advanced Options Trading Indicator: Buy & Sell Signal Generator This powerful custom indicator combines the Relative Strength Index (RSI) and Moving Average (MA) to help traders identify optimal entry and exit points in the options market. The indicator generates real-time buy and sell signals based on RSI crossovers and price positioning relative to the moving average, providing actionable insights for traders seeking to make informed decisions. Additionally, it calculates potential call and put option strike prices with a buffer for added flexibility and precision, ensuring a well-rounded approach to options trading.
Machine Learning Price Target Prediction Signals [AlgoAlpha]Introducing the Machine Learning Price Target Predictions, a cutting-edge trading tool that leverages kernel regression to provide accurate price targets and enhance your trading strategy. This indicator combines trend-based signals with advanced machine learning techniques, offering predictive insights into potential price movements. Perfect for traders looking to make data-driven decisions with confidence.
What is Kernel Regression and How It Works
Kernel regression is a non-parametric machine learning technique that estimates the relationship between variables by weighting data points based on their similarity to a given input. The similarity is determined using a kernel function, such as the Gaussian (RBF) kernel, which assigns higher weights to closer data points and progressively lower weights to farther ones. This allows the model to make smooth and adaptive predictions, balancing recent data and historical trends.
Key Features
🎯 Predictive Price Targets : Uses kernel regression to estimate the magnitude of price movements.
📈 Dynamic Trend Analysis : Multiple trend detection methods, including EMA crossovers, Hull Moving Average, and SuperTrend.
🔧 Customizable Settings : Adjust bandwidth for kernel regression and tweak trend indicator parameters to suit your strategy.
📊 Visual Trade Levels : Displays take-profit and stop-loss levels directly on the chart with customizable colors.
📋 Performance Metrics : Real-time win rate, recommended risk-reward ratio, and training data size displayed in an on-chart table.
🔔 Alerts : Get notified for new trends, take-profit hits, and stop-loss triggers.
How to Use
🛠 Add the Indicator : Add it to your favorites and apply it to your chart. Configure the trend detection method (SuperTrend, HMA, or EMA crossover) and other parameters based on your preferences.
📊 Analyze Predictions : Observe the predicted move size, recommended risk-reward ratio, and trend direction. Use the displayed levels for trade planning.
🔔 Set Alerts : Enable alerts for trend signals, take-profit hits, or stop-loss triggers to stay informed without constant monitoring.
How It Works
The indicator calculates features such as price volatility, relative strength, and trend signals, which are stored during training periods. When a trend change is detected, the kernel regression model predicts the likely price move based on these features. Predictions are smoothed using the specified bandwidth to avoid overfitting while ensuring timely responses to feature changes. Visualized take-profit and stop-loss levels help traders optimize risk management. Real-time metrics like win rate and recommended risk-reward ratios provide actionable insights for decision-making.
weakly dividesThis indicator takes the last weekly candle and divides it into the number the user wants. This is a great strategy.
Buy Low over 18 SMA Strategythis is a customizeable strategy to buy on daily chart where you can select after which days you want to buy
with a 1or2 day trailing stop on prior low
the Nasdaq seams to be most profitable when buying above the wednesdays and fridays high
this avoided entries in the bearish move on july 2024
breakeven stops in aug 2023
only small losses in jan/april/sept 2022
all in all a pretty good strategy when exiting below the low of prior two days
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 ----------------
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(If you are comfortable with statistics feel free to skip ahead to the next section)
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-------- 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 ------------------------------
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---- 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 :)
