Naive Bayes Candlestick Pattern Classifier v1.1 BETAAn intermezzo on why i made this script publication..
A : Candlestick Pattern took hours to backtest, why not using Machine Learning techniques?
B : Machine Learning, no that's gonna be really heavy bro!
A : Not really, because we use Naive Bayes.
B : The simplest, yet powerful machine learning algorithm to separate (a.k.a classify) multivariate data.
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Hello, everyone!
After deep research in extracting meaningful information from the market, I ended up building this powerful machine learning indicator based on the evolution of Bayesian Statistics. This indicator not only leverages the simplicity of Naive Bayes but also extends its application to candlestick pattern analysis, making it an invaluable tool for traders who are looking to enhance their technical analysis without spending countless hours manually backtesting each pattern on each market!.
What most interesting part is actually after learning all of likely useless methods like fibonacci, supply and demand, volume profile, etc. We always ended up back to basic like support and resistance and candlestick patterns, but with a slight twist on strategy algorithm design and statistical approach. Thus, the only reason why i made this, because i exactly know that you guys will ended up in this position as time goes by.
The essence of this indicator lies in its ability to automate the recognition and statistical evaluation of various candlestick patterns. Traditionally, traders have relied on visual inspection and manual backtesting to determine the effectiveness of patterns like Bullish Engulfing, Bearish Engulfing, Harami variations, Hammer formations, and even more complex multi-candle patterns such as Three White Soldiers, Three Black Crows, Dark Cloud Cover, and Piercing Pattern. However, these conventional methods are both time-consuming and prone to subjective bias.
To address these challenges, I employed Naive Bayes—a probabilistic classifier that, despite its simplicity, offers robust performance in various domains. Naive Bayes assumes that each feature is independent of the others given the class label, which, although a strong assumption, works remarkably well in practice, especially when the dataset is large like market data and the feature space is high-dimensional. In our case, each candlestick pattern acts as a feature that can be statistically evaluated based on its historical performance. The indicator calculates a probability that a given pattern will lead to a price reversal, by comparing the pattern’s close price to the highest or lowest price achieved in a lookahead window.
One of the standout features of this script is its flexibility. Each candlestick pattern is not only coded into the system but also comes with individual toggles to enable or disable them based on your trading strategy. This means you can choose to focus on single-candle patterns like Bullish Engulfing or more complex multi-candle formations such as Three White Soldiers, without modifying the core code. The built-in customization options allow you to adjust colors and labels for each pattern, giving you the freedom to tailor the visual output to your preference. This level of customization ensures that the indicator integrates seamlessly into your existing TradingView setup.
Moreover, the indicator isn’t just about pattern recognition—it also incorporates outcome-based learning. Every time a pattern is detected, it looks ahead a predefined number of bars to evaluate if the expected reversal actually materialized. This outcome is then stored in arrays, and over time, the script dynamically calculates the probability of success for each pattern. These probabilities are presented in a real-time updating table on your chart, which shows not only the percentage probability but also the count of historical occurrences. With this information at your fingertips, you can quickly gauge the reliability of each pattern in your chosen market and timeframe.
Another significant advantage of this approach is its speed and efficiency. While more complex machine learning models like neural networks might require heavy computational resources and longer training times, the Naive Bayes classifier in this script is lightweight, instantaneous and can be updated on the fly with each new bar. This real-time capability is essential for modern traders who need to make quick decisions in fast-paced markets.
Furthermore, by automating the process of backtesting, the indicator frees up your time to focus on other aspects of trading strategy development. Instead of manually analyzing hundreds or even thousands of candles, you can rely on the statistical power of Naive Bayes to provide you with insights on which patterns are most likely to result in profitable moves. This not only enhances your efficiency but also helps to eliminate the cognitive biases that often plague manual analysis.
In summary, this indicator represents a fusion of traditional candlestick analysis with modern machine learning techniques. It harnesses the simplicity and effectiveness of Naive Bayes to deliver a dynamic, real-time evaluation of various candlestick patterns. Whether you are a seasoned trader looking to refine your technical analysis or a beginner eager to understand market dynamics, this tool offers a powerful, customizable, and efficient solution. Welcome to a new era where advanced statistical methods meet practical trading insights—happy trading and may your patterns always be in your favor!
Note : On this current released beta version, you must manually adjust reversal percentage move based on each market. Further updates may include automated best range detection and probability.
Cerca negli script per "backtest"
RBF Kijun Trend System [InvestorUnknown]The RBF Kijun Trend System utilizes advanced mathematical techniques, including the Radial Basis Function (RBF) kernel and Kijun-Sen calculations, to provide traders with a smoother trend-following experience and reduce the impact of noise in price data. This indicator also incorporates ATR to dynamically adjust smoothing and further minimize false signals.
Radial Basis Function (RBF) Kernel Smoothing
The RBF kernel is a mathematical method used to smooth the price series. By calculating weights based on the distance between data points, the RBF kernel ensures smoother transitions and a more refined representation of the price trend.
The RBF Kernel Weighted Moving Average is computed using the formula:
f_rbf_kernel(x, xi, sigma) =>
math.exp(-(math.pow(x - xi, 2)) / (2 * math.pow(sigma, 2)))
The smoothed price is then calculated as a weighted sum of past prices, using the RBF kernel weights:
f_rbf_weighted_average(src, kernel_len, sigma) =>
float total_weight = 0.0
float weighted_sum = 0.0
// Compute weights and sum for the weighted average
for i = 0 to kernel_len - 1
weight = f_rbf_kernel(kernel_len - 1, i, sigma)
total_weight := total_weight + weight
weighted_sum := weighted_sum + (src * weight)
// Check to avoid division by zero
total_weight != 0 ? weighted_sum / total_weight : na
Kijun-Sen Calculation
The Kijun-Sen, a component of Ichimoku analysis, is used here to further establish trends. The Kijun-Sen is computed as the average of the highest high and the lowest low over a specified period (default: 14 periods).
This Kijun-Sen calculation is based on the RBF-smoothed price to ensure smoother and more accurate trend detection.
f_kijun_sen(len, source) =>
math.avg(ta.lowest(source, len), ta.highest(source, len))
ATR-Adjusted RBF and Kijun-Sen
To mitigate false signals caused by price volatility, the indicator features ATR-adjusted versions of both the RBF smoothed price and Kijun-Sen.
The ATR multiplier is used to create upper and lower bounds around these lines, providing dynamic thresholds that account for market volatility.
Neutral State and Trend Continuation
This indicator can interpret a neutral state, where the signal is neither bullish nor bearish. By default, the indicator is set to interpret a neutral state as a continuation of the previous trend, though this can be adjusted to treat it as a truly neutral state.
Users can configure this setting using the signal_str input:
simple string signal_str = input.string("Continuation of Previous Trend", "Treat 0 State As", options = , group = G1)
Visual difference between "Neutral" (Bottom) and "Continuation of Previous Trend" (Top). Click on the picture to see it in full size.
Customizable Inputs and Settings:
Source Selection: Choose the input source for calculations (open, high, low, close, etc.).
Kernel Length and Sigma: Adjust the RBF kernel parameters to change the smoothing effect.
Kijun Length: Customize the lookback period for Kijun-Sen.
ATR Length and Multiplier: Modify these settings to adapt to market volatility.
Backtesting and Performance Metrics
The indicator includes a Backtest Mode, allowing users to evaluate the performance of the strategy using historical data. In Backtest Mode, a performance metrics table is generated, comparing the strategy's results to a simple buy-and-hold approach. Key metrics include mean returns, standard deviation, Sharpe ratio, and more.
Equity Calculation: The indicator calculates equity performance based on signals, comparing it against the buy-and-hold strategy.
Performance Metrics Table: Detailed performance analysis, including probabilities of positive, neutral, and negative returns.
Alerts
To keep traders informed, the indicator supports alerts for significant trend shifts:
// - - - - - ALERTS - - - - - //{
alert_source = sig
bool long_alert = ta.crossover (intrabar ? alert_source : alert_source , 0)
bool short_alert = ta.crossunder(intrabar ? alert_source : alert_source , 0)
alertcondition(long_alert, "LONG (RBF Kijun Trend System)", "RBF Kijun Trend System flipped ⬆LONG⬆")
alertcondition(short_alert, "SHORT (RBF Kijun Trend System)", "RBF Kijun Trend System flipped ⬇Short⬇")
//}
Important Notes
Calibration Needed: The default settings provided are not optimized and are intended for demonstration purposes only. Traders should adjust parameters to fit their trading style and market conditions.
Neutral State Interpretation: Users should carefully choose whether to treat the neutral state as a continuation or a separate signal.
Backtest Results: Historical performance is not indicative of future results. Market conditions change, and past trends may not recur.
Supertrend StrategyThe Supertrend Strategy was created based on the Supertrend and Relative Strength Index (RSI) indicators, widely respected tools in technical analysis. This strategy combines these two indicators to capture market trends with precision and reliability, looking for optimizing exit levels at oversold or overbought price levels.
The Supertrend indicator identifies trend direction based on price and volatility by using the Average True Range (ATR). The ATR measures market volatility by calculating the average range between an asset’s high and low prices over a set period. It provides insight into price fluctuations, with higher ATR values indicating increased volatility and lower values suggesting stability. The Supertrend Indicator plots a line above or below the price, signaling potential buy or sell opportunities: when the price closes above the Supertrend line, an uptrend is indicated, while a close below the line suggests a downtrend. This line shifts as price movements and volatility levels change, acting as both a trailing stop loss and trend confirmation.
To enhance the Supertrend strategy, the Relative Strength Index (RSI) has been added as an exit criterion. As a momentum oscillator, the RSI indicates overbought (usually above 70) or oversold (usually below 30) conditions. This integration allows trades to close when the asset is overbought or oversold, capturing gains before a possible reversal, even if the percentage take profit level has not been reached. This mechanism aims to prevent losses due to market reversals before the Supertrend signal changes.
### Key Features
1. **Entry criteria**:
- The strategy uses the Supertrend indicator calculated by adding or subtracting a multiple of the ATR from the closing price, depending on the trend direction.
- When the price crosses above the Supertrend line, the strategy signals a long (buy) entry. Conversely, when the price crosses below, it signals a short (sell) entry.
- The strategy performs a reversal if there is an open position and a change in the direction of the supertrend occurs
2. **Exit criteria**:
- Take profit of 30% (default) on the average position price.
- Oversold (≤ 5) or overbought (≥ 95) RSI
- Reversal when there is a change in direction of the Supertrend
3. **No Repainting**:
- This strategy is not subject to repainting, as long as the timeframe configured on your chart is the same as the supertrend timeframe .
4. **Position Sizing by Equity and risk management**:
- This strategy has a default configuration to operate with 35% of the equity. At the time of opening the position, the supertrend line is typically positioned at about 12 to 16% of the entry price. This way, the strategy is putting at risk about 16% of 35% of equity, that is, around 5.6% of equity for each trade. The percentage of equity can be adjusted by the user according to their risk management.
5. **Backtest results**:
- This strategy was subjected to deep backtesting and operations in replay mode, including transaction fees of 0.12%, and slippage of 5 ticks.
- The past results in deep backtest and replay mode were compatible and profitable (Variable results depending on the take profit used, supertrend and RSI parameters). However, it should be noted that few operations were evaluated, since the currency in question has been created for a short time and the frequency of operations is relatively small.
- Past results are no guarantee of future results. The strategy's backtest results may even be due to overfitting with past data.
Default Settings
Chart timeframe: 2h
Supertrend Factor: 3.42
ATR period: 14
Supertrend timeframe: 2 h
RSI timeframe: 15 min
RSI Lenght: 5 min
RSI Upper limit: 95
RSI Lower Limit: 5
Take Profit: 30%
BYBIT:1000000MOGUSDT.P
BreakoutTrendFollowingINFO:
The "BreakoutTrendFollowing" indicator is a comprehensive trading system designed for trend-following in various market environments. It combines multiple technical indicators, including Moving Averages (MA), MACD, and RSI,
along with volume analysis and breakout detection from consolidation, to identify potential entry points in trending markets. This strategy is particularly effective for assets that exhibit strong trends and significant price movements.
Note that using the consolidation filter reduces the amount of entries the strategy detects significantly, and needs to be used if we want to have an increased confidence in the trend via breakout.
However, the strategy can be easily transformed to various only trend-following strategies, by applying different filters and configurations.
The indicator can be used to connect to the Signal input of the TTS (TempalteTradingStrategy) by jason5480 in order to backtest it, thus effectively turning it into a strategy (instructions below in TTS CONNECTIVITY section)
DETAILS:
The strategy's core is built upon several key components:
Moving Average (MA): Used to determine the general trend direction. The strategy checks if the price is above the selected MA type and length.
MACD Filter: Analyzes the relationship between two moving averages to confirm the trend's momentum.
Consolidation Detection: Identifies periods of price consolidation and triggers trades on breakouts from these ranges.
Volume Analysis: Assesses trading volume to confirm the strength and validity of the breakout.
RSI: Used to avoid overbought conditions, ensuring trades are entered in favorable market situations.
Wick filters: make sure there is not a long wick that indicates selling pressure from above
The strategy generates buy signals when several conditions are met concurrently (each one of them can be individually enabled/disabled)"
The price is above the selected MA.
A breakout occurs from a configurable consolidation range.
The MACD line is above the signal line, indicating bullish momentum.
The RSI is below the overbought threshold.
There's an increase in trading volume, confirming the breakout's strength.
Currently the strategy fires SL signals, as the approach is to check for loss of momentum - i.e. crossunder of the MACD line and signal line, but that is to everyone to determine the exit conditions.
The buy and SL signals are set on the chart using green or orange triangles on the below/above the price action.
SETTINGS:
Users can customize various parameters, including MA type and period, MACD settings, consolidation length, and volume increase percentage. The strategy is equipped with alert conditions for both entry (buy signals) and exit (set stop loss) points, facilitating both manual and automated trading.
Each one of the technical indicators, as well as the consilidation range and breakout/wick settings can be configured and enabled/disabled individually.
Please thoroughly review the available settings of the script, but here is an outline of the most important ones:
Use bar wicks (instead of open/close) - the ref_high/low will be taken based on the bar wicks, rather than the open/close when determining the breakout and MA
Enter position only on green candles - additional filters to make sure that we enter only on strong momentum
MA Filter: (enable, source, type, length) - general settings for MA filter to be checked against the stock price (close or upper wick)
MACD Filter: (enable, source, Osc MA type, Signal MA type, Fast MA length, Slow MA length, Low MACD Hist) - detailed settings for fine MACD tuning
Consolidation:
Consolidation Type: we have two different ways of detecting the consolidation, note the types below.
CONSOLIDATION_BASIC - consolidation areas by looking for the pivot point of a trend and counts the number of bars that have not broken the consolidation high/low levels.
CONSOLIDATIO_RANGE_PERCENT - identifies consolidation by comparing the range between the highest and lowest price points over a specified period.
So in summary the CONSOLIDATIO_RANGE_PERCENT uses a percentage-based range to define consolidation, while CONSOLIDATION_BASIC uses a count of bars within a high-low range to establish consolidation.
Thus the former is more focused on the tightness of the price range, whereas the latter emphasizes the duration of the consolidation phase.
The CONSOLIDATIO_RANGE_PERCENT might be more sensitive to recent price movements and suitable for shorter-term analysis, while CONSOLIDATION_BASIC could be better for identifying longer-term consolidation patterns.
Min consolidation length - applicable for CONSOLIDATION_BASIC case, the min number of bars for the price to be in the range to consider consolidation
Consolidation Loopback period - applicable for CONSOLIDATION_BASIC case, the loopback number of bars to look for consolidation
Consolidation Range percent - applicable for CONSOLIDATIO_RANGE_PERCENT, the percent between the high and low in the range to consider consolidation
Plot consolidation - enables plotting of the consolidation (only for debug purposes)
Breakout: (enable, low, high) - the definition of the breakout from the previous consolidation range, the price should be between to determine the breakout as successfull
Upper wick: (enable, percent) - defines the percent of the upper wick compared to the whole candle to allow breakout (if the wick is too big part of the candle we can consider entering the position riskier)
RSI: (enable, length, overbought) - general settings for RSI TA
Volume (enbale, percentage increase, average volume filter en, loopback bars) - percentage of increase of the volume to consider for a breakout. There are two modes - percentage increase compared to the previous bar, or percentage against the average volume for the last loopback bars.
Note that there are many different configuration that you can play with, and I believe this is the strength of the strategy, as it can provide a single solution for different cases and scenarios.
My advice is to try and play with the different options for different markets based on the approach you want to implement and try turning features on/off and tuning them further.
TTS SETTINGS (NEEDED IF USED TO BACKTEST WITH TTS):
The TempalteTradingStrategy is a strategy script developed in Pine by jason5480, which I recommend for quick turn-around of testing different ideas on a proven and tested framework
I cannot give enough credit to the developer for the efforts put in building of the infrastructure, so I advice everyone that wants to use it first to get familiar with the concept and by checking
by checking jason5480's profile www.tradingview.com
The TTS itself is extremely functional and have a lot of properties, so its functionality is beyond the scope of the current script -
Again, I strongly recommend to be thoroughly explored by everyone that plans on using it.
In the nutshell it is a script that can be feed with buy/sell signals from an external indicator script and based on many configuration options it can determine how to execute the trades.
The TTS has many settings that can be applied, so below I will cover only the ones that differ from the default ones, at least according to my testing - do your own research, you may find something even better :)
The current/latest version that I've been using as of writing and testing this script is TTSv48
Settings which differ from the default ones:
Deal Conditions Mode - External (take enter/exit conditions from an external script)
🔌Signal 🛈➡ - BreakoutTrendFollowing: 🔌Signal to TTS (this is the output from the indicator script, according to the TTS convention)
Order Type - STOP (perform stop order)
Distance Method - HHLL (HigherHighLowerLow - in order to set the SL according to the strategy definition from above)
The next are just personal preferences, you can feel free to experiment according to your trading style
Take Profit Targets - 0 (either 100% in or out, no incremental stepping in or out of positions)
Dist Mul|Len Long/Short- 10 (make sure that we don't close on profitable trades by any reason)
Quantity Method - EQUITY (personal backtesting preference is to consider each backtest as a separate portfolio, so determine the position size by 100% of the allocated equity size)
Equity % - 100 (note above)
Risk Reward Optimiser [ChartPrime]█ CONCEPTS
In modern day strategy optimization there are few options when it comes to optimizing a risk reward ratio. Users frequently need to experiment and go through countless permutations in order to tweak, adjust and find optimal in their data.
Therefore we have created the Risk Reward Optimizer.
The Risk Reward Optimizer is a technical tool designed to provide traders with comprehensive insights into their trading strategies.
It offers a range of features and functionalities aimed at enhancing traders' decision-making process.
With a focus on comprehensive data, it is there to help traders quickly and efficiently locate Risk Reward optimums for inbuilt of custom strategies.
█ Internal and external Signals:
The script can optimize risk to reward ratio for any type of signals
You can utilize the following :
🔸Internal signals ➞ We have included a number of common indicators into the optimizer such as:
▫️ Aroon
▫️ AO (Awesome Oscillator)
▫️ RSI (Relative Strength Index)
▫️ MACD (Moving Average Convergence Divergence)
▫️ SuperTrend
▫️ Stochastic RSI
▫️ Stochastic
▫️ Moving averages
All these indicators have 3 conditions to generate signals :
Crossover
High Than
Less Than
🔸External signal
▫️ by incorporating your own indicators into the analysis. This flexibility enables you to tailor your strategy to your preferences.
◽️ How to link your signal with the optimizer:
In order to be able to analysis your signal we need to read it and to do so we would need to PLOT your signal with a defined value
plot( YOUR LONG Condition ? 100 : 0 , display = display.data_window)
█ Customizable Risk to Reward Ratios:
This tool allows you to test seven different customizable risk to reward ratios , helping you determine the most suitable risk-reward balance for your trading strategy. This data-driven approach takes the guesswork out of setting stop-loss and take-profit levels.
█ Comprehensive Data Analysis:
The tool provides a table displaying key metrics, including:
Total trades
Wins
Losses
Profit factor
Win rate
Profit and loss (PNL)
This data is essential for refining your trading strategy.
🔸 It includes a tooltip for each risk to reward ratio which gives data for the:
Most Profitable Trade USD value
Most Profitable Trade % value
Most Profitable Trade Bar Index
Most Profitable Trade Time (When it occurred)
Position and size is adjustable
█ Visual insights with histograms:
Visualize your trading performance with histograms displaying each risk to reward ratio trade space, showing total trades, wins, losses, and the ratio of profitable trades.
This visual representation helps you understand the strengths and weaknesses of your strategy.
It offers tooltips for each RR ratio with the average win and loss percentages for further analysis.
█ Dynamic Highlighting:
A drop-down menu allows you to highlight the maximum values of critical metrics such as:
Profit factor
Win rate
PNL
for quick identification of successful setups.
█ Stop Loss Flexibility:
You can adjust stop-loss levels using three different calculation methods:
ATR
Pivot
VWAP
This allows you to align risk-reward ratios with your preferred risk tolerance.
█ Chart Integration:
Visualize your trades directly on your price chart, with each trade displayed in a distinct color for easy tracking.
When your take-profit (TP) level is reached , the tool labels the corresponding risk-reward ratio for that specific TP, simplifying trade management.
█ Detailed Tooltips:
Tooltips provide deeper insights into your trading performance. They include information about the most profitable trade, such as the time it occurred, the bar index, and the percentage gain. Histogram tooltips also offer average win and loss percentages for further analysis.
█ Settings:
█ Code:
In summary, the Risk Reward Optimizer is a data-driven tool that offers traders the ability to optimize their risk-reward ratios, refine their strategies, and gain a deeper understanding of their trading performance. Whether you're a day trader, swing trader, or investor, this tool can help you make informed decisions and improve your trading outcomes.
Tailored-Custom Hamonic Patterns█ OVERVIEW
We have included by default 3 known Patterns. The Bat, the Butterfly and the Gartley. But have you ever wondered how effective other,
not yet known models could be? Don't ask yourself the question anymore, it's time to find out for yourself! You have the option to customize
your own Patterns with the Backtesting tool and set Retracement Ratios and Targets for your own Patterns. In addition to this, in order to determine
the Trend at a glance and make Pattern detection more efficient, we have linked the calculation of Patterns to Bands of several types to choose
from (Bollinger, Keltner, Donchian) that you can select from a drop-down menu in the settings and play with the Multiplier
and the Adaptive Length of the Patterns to see how it affects the success rate in the Backtesting table.
█ HOW DOES IT WORK?
- Harmonic Patterns
-Pattern Names, Colors, Style etc… Everything is customizable.
-Dynamic Adaptative Length with Min/Max Length.
- XAB/ABC Ratio
-Min/Max XAB/ABC Configurable Ratio for each Pattern to create your own Patterns.
(This is really the particular option of this Indicator, because it allows you to be able to Backtest in real time
after having played at configuring your own Ratios)
- Bands
-Contrary to the original logic of the HeWhoMustNotBeNamed script, here when the price breaks out of the upper Bands
(example, Bollinger band, Keltner Channel or Donchian Channel) , with a predetermined Minimum and Maximum Length and Multiplier, we can consider
the Trend to be Bearish (and not Bullish) and similarly when the price breaks down in the lower band, we can consider the Trend
to be Bullish (not Bearish) . We have also added the middle line of the Channels (which can be useful for 'Scalper' type Traders.
-The Length of the Bands Filter is directly related to the Dynamic Length of the Patterns.
-You can use a drop-down menu to select from the following Bands Filters :
SMA, EMA, HMA, RMA, WMA, VWMA, HIGH/LOW, LINREG, MEDIAN.
-Sticky and Adaptive Bands options has been included.
- Projections
-BD/CD Projection Ratio configurable for each Pattern.
(Projections are visible as Dotted Lines which we can choose to Extend or not)
- Targets
-Target, PRZ and Stop Levels are set to optimal values based on individual Patterns. (The PRZ Level corresponds to point D
of the detected Pattern so its value should always be 0) but you can change the Targets value (defined in %) as you wish.
Again here, you have the option to fully configure the Style and Extend the Lines or not.
- Backtesting Table
-As said previously, with the possibility of testing the Success Rate of each of the 3 Customizable Patterns,
this option is part of the logic of this Indicator.
- Alerts
-We originally believe that this Indicator does not even need Alerts. But we still decided to include at least one Alert
that you can set for when a new Pattern is detected.
█ NOTES
Thanks to HeWhoMustNotBeNamed for his permission to reuse some part of his zigzag scripts.
Remember to only make a decision once you are sure of your analysis. Good trading sessions to everyone and don't forget,
risk management remains the most important!
Machine Learning: Lorentzian Classification█ OVERVIEW
A Lorentzian Distance Classifier (LDC) is a Machine Learning classification algorithm capable of categorizing historical data from a multi-dimensional feature space. This indicator demonstrates how Lorentzian Classification can also be used to predict the direction of future price movements when used as the distance metric for a novel implementation of an Approximate Nearest Neighbors (ANN) algorithm.
█ BACKGROUND
In physics, Lorentzian space is perhaps best known for its role in describing the curvature of space-time in Einstein's theory of General Relativity (2). Interestingly, however, this abstract concept from theoretical physics also has tangible real-world applications in trading.
Recently, it was hypothesized that Lorentzian space was also well-suited for analyzing time-series data (4), (5). This hypothesis has been supported by several empirical studies that demonstrate that Lorentzian distance is more robust to outliers and noise than the more commonly used Euclidean distance (1), (3), (6). Furthermore, Lorentzian distance was also shown to outperform dozens of other highly regarded distance metrics, including Manhattan distance, Bhattacharyya similarity, and Cosine similarity (1), (3). Outside of Dynamic Time Warping based approaches, which are unfortunately too computationally intensive for PineScript at this time, the Lorentzian Distance metric consistently scores the highest mean accuracy over a wide variety of time series data sets (1).
Euclidean distance is commonly used as the default distance metric for NN-based search algorithms, but it may not always be the best choice when dealing with financial market data. This is because financial market data can be significantly impacted by proximity to major world events such as FOMC Meetings and Black Swan events. This event-based distortion of market data can be framed as similar to the gravitational warping caused by a massive object on the space-time continuum. For financial markets, the analogous continuum that experiences warping can be referred to as "price-time".
Below is a side-by-side comparison of how neighborhoods of similar historical points appear in three-dimensional Euclidean Space and Lorentzian Space:
This figure demonstrates how Lorentzian space can better accommodate the warping of price-time since the Lorentzian distance function compresses the Euclidean neighborhood in such a way that the new neighborhood distribution in Lorentzian space tends to cluster around each of the major feature axes in addition to the origin itself. This means that, even though some nearest neighbors will be the same regardless of the distance metric used, Lorentzian space will also allow for the consideration of historical points that would otherwise never be considered with a Euclidean distance metric.
Intuitively, the advantage inherent in the Lorentzian distance metric makes sense. For example, it is logical that the price action that occurs in the hours after Chairman Powell finishes delivering a speech would resemble at least some of the previous times when he finished delivering a speech. This may be true regardless of other factors, such as whether or not the market was overbought or oversold at the time or if the macro conditions were more bullish or bearish overall. These historical reference points are extremely valuable for predictive models, yet the Euclidean distance metric would miss these neighbors entirely, often in favor of irrelevant data points from the day before the event. By using Lorentzian distance as a metric, the ML model is instead able to consider the warping of price-time caused by the event and, ultimately, transcend the temporal bias imposed on it by the time series.
For more information on the implementation details of the Approximate Nearest Neighbors (ANN) algorithm used in this indicator, please refer to the detailed comments in the source code.
█ HOW TO USE
Below is an explanatory breakdown of the different parts of this indicator as it appears in the interface:
Below is an explanation of the different settings for this indicator:
General Settings:
Source - This has a default value of "hlc3" and is used to control the input data source.
Neighbors Count - This has a default value of 8, a minimum value of 1, a maximum value of 100, and a step of 1. It is used to control the number of neighbors to consider.
Max Bars Back - This has a default value of 2000.
Feature Count - This has a default value of 5, a minimum value of 2, and a maximum value of 5. It controls the number of features to use for ML predictions.
Color Compression - This has a default value of 1, a minimum value of 1, and a maximum value of 10. It is used to control the compression factor for adjusting the intensity of the color scale.
Show Exits - This has a default value of false. It controls whether to show the exit threshold on the chart.
Use Dynamic Exits - This has a default value of false. It is used to control whether to attempt to let profits ride by dynamically adjusting the exit threshold based on kernel regression.
Feature Engineering Settings:
Note: The Feature Engineering section is for fine-tuning the features used for ML predictions. The default values are optimized for the 4H to 12H timeframes for most charts, but they should also work reasonably well for other timeframes. By default, the model can support features that accept two parameters (Parameter A and Parameter B, respectively). Even though there are only 4 features provided by default, the same feature with different settings counts as two separate features. If the feature only accepts one parameter, then the second parameter will default to EMA-based smoothing with a default value of 1. These features represent the most effective combination I have encountered in my testing, but additional features may be added as additional options in the future.
Feature 1 - This has a default value of "RSI" and options are: "RSI", "WT", "CCI", "ADX".
Feature 2 - This has a default value of "WT" and options are: "RSI", "WT", "CCI", "ADX".
Feature 3 - This has a default value of "CCI" and options are: "RSI", "WT", "CCI", "ADX".
Feature 4 - This has a default value of "ADX" and options are: "RSI", "WT", "CCI", "ADX".
Feature 5 - This has a default value of "RSI" and options are: "RSI", "WT", "CCI", "ADX".
Filters Settings:
Use Volatility Filter - This has a default value of true. It is used to control whether to use the volatility filter.
Use Regime Filter - This has a default value of true. It is used to control whether to use the trend detection filter.
Use ADX Filter - This has a default value of false. It is used to control whether to use the ADX filter.
Regime Threshold - This has a default value of -0.1, a minimum value of -10, a maximum value of 10, and a step of 0.1. It is used to control the Regime Detection filter for detecting Trending/Ranging markets.
ADX Threshold - This has a default value of 20, a minimum value of 0, a maximum value of 100, and a step of 1. It is used to control the threshold for detecting Trending/Ranging markets.
Kernel Regression Settings:
Trade with Kernel - This has a default value of true. It is used to control whether to trade with the kernel.
Show Kernel Estimate - This has a default value of true. It is used to control whether to show the kernel estimate.
Lookback Window - This has a default value of 8 and a minimum value of 3. It is used to control the number of bars used for the estimation. Recommended range: 3-50
Relative Weighting - This has a default value of 8 and a step size of 0.25. It is used to control the relative weighting of time frames. Recommended range: 0.25-25
Start Regression at Bar - This has a default value of 25. It is used to control the bar index on which to start regression. Recommended range: 0-25
Display Settings:
Show Bar Colors - This has a default value of true. It is used to control whether to show the bar colors.
Show Bar Prediction Values - This has a default value of true. It controls whether to show the ML model's evaluation of each bar as an integer.
Use ATR Offset - This has a default value of false. It controls whether to use the ATR offset instead of the bar prediction offset.
Bar Prediction Offset - This has a default value of 0 and a minimum value of 0. It is used to control the offset of the bar predictions as a percentage from the bar high or close.
Backtesting Settings:
Show Backtest Results - This has a default value of true. It is used to control whether to display the win rate of the given configuration.
█ WORKS CITED
(1) R. Giusti and G. E. A. P. A. Batista, "An Empirical Comparison of Dissimilarity Measures for Time Series Classification," 2013 Brazilian Conference on Intelligent Systems, Oct. 2013, DOI: 10.1109/bracis.2013.22.
(2) Y. Kerimbekov, H. Ş. Bilge, and H. H. Uğurlu, "The use of Lorentzian distance metric in classification problems," Pattern Recognition Letters, vol. 84, 170–176, Dec. 2016, DOI: 10.1016/j.patrec.2016.09.006.
(3) A. Bagnall, A. Bostrom, J. Large, and J. Lines, "The Great Time Series Classification Bake Off: An Experimental Evaluation of Recently Proposed Algorithms." ResearchGate, Feb. 04, 2016.
(4) H. Ş. Bilge, Yerzhan Kerimbekov, and Hasan Hüseyin Uğurlu, "A new classification method by using Lorentzian distance metric," ResearchGate, Sep. 02, 2015.
(5) Y. Kerimbekov and H. Şakir Bilge, "Lorentzian Distance Classifier for Multiple Features," Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods, 2017, DOI: 10.5220/0006197004930501.
(6) V. Surya Prasath et al., "Effects of Distance Measure Choice on KNN Classifier Performance - A Review." .
█ ACKNOWLEDGEMENTS
@veryfid - For many invaluable insights, discussions, and advice that helped to shape this project.
@capissimo - For open sourcing his interesting ideas regarding various KNN implementations in PineScript, several of which helped inspire my original undertaking of this project.
@RikkiTavi - For many invaluable physics-related conversations and for his helping me develop a mechanism for visualizing various distance algorithms in 3D using JavaScript
@jlaurel - For invaluable literature recommendations that helped me to understand the underlying subject matter of this project.
@annutara - For help in beta-testing this indicator and for sharing many helpful ideas and insights early on in its development.
@jasontaylor7 - For helping to beta-test this indicator and for many helpful conversations that helped to shape my backtesting workflow
@meddymarkusvanhala - For helping to beta-test this indicator
@dlbnext - For incredibly detailed backtesting testing of this indicator and for sharing numerous ideas on how the user experience could be improved.
Simple SuperTrend Strategy for BTCUSD 4HHello guys!, If you are a swing trader and you are looking for a simple trend strategy, you should check this one. Based in the supertrend indicator, this strategy will help you to catch big movements in BTCUSD 4H and avoid losses as much as possible in consolidated situations of the market
This strategy was designed for BTCUSD in 4H timeframe
Backtesting context: 2020-01-02 to 2023-01-05 (The strategy has also worked in previous years)
Trade conditions:
Rules are actually simple, the most important thing is the risk and position management of this strategy
For long:
Once Supertrend changes from a downtrend to a uptrend, you enter into a long position. The stop loss will be defined by the atr stop loss
The first profit will be of 0.75 risk/reward ratio where half position will be closed. When this happens, you move the stop loss to break even.
Now, just will be there two situations:
Once Supertrend changes from a uptrend to a downtrend, you close the other half of the initial long position.
If price goes againts the position, the position will be closed due to breakeven.
For short:
Once Supertrend changes from a uptrend to a downtrend, you enter into a short position. The stop loss will be defined by the atr stop loss
The first profit will be of 0.75 risk/reward ratio where half position will be closed. When this happens, you move the stop loss to break even.
Like in the long position, just will be there two situations:
Once Supertrend changes from a downtrend to a uptrend, you close the other half of the initial short position.
If price goes againts the position, the position will be closed due to breakeven.
Risk management
For calculate the amount of the position you will use just a small percent of your initial capital for the strategy and you will use the atr stop loss for this.
Example: You have 1000 usd and you just want to risk 2,5% of your account, there is a long signal at price of 20,000 usd. The stop loss price from atr stop loss is 19,000. You calculate the distance in percent between 20,000 and 19,000. In this case, that distance would be of 5,0%. Then, you calculate your position by this way: (initial or current capital * risk per trade of your account) / (stop loss distance).
Using these values on the formula: (1000*2,5%)/(5,0%) = 500usd. It means, you have to use 500 usd for risking 2.5% of your account.
We will use this risk management for apply compound interest.
Script functions
Inside of settings, you will find some utilities for display atr stop loss, supertrend or positions.
You will find the settings for risk management at the end of the script if you want to change something. But rebember, do not change values from indicators, the idea is to not over optimize the strategy.
If you want to change the initial capital for backtest the strategy, go to properties, and also enter the commisions of your exchange and slippage for more realistic results.
Signals meanings:
L for long position. CL for close long position.
S for short position. CS for close short position.
Tp for take profit (it also appears when the position is closed due to stop loss, this due to the script uses two kind of positions)
Exit due to break even or due to stop loss
Some things to consider
USE UNDER YOUR OWN RISK. PAST RESULTS DO NOT REPRESENT THE FUTURE.
DEPENDING OF % ACCOUNT RISK PER TRADE, YOU COULD REQUIRE LEVERAGE FOR OPEN SOME POSITIONS, SO PLEASE, BE CAREFULL AND USE CORRECTLY THE RISK MANAGEMENT
The amount of trades closed in the backtest are not exactly the real ones. If you want to know the real ones, go to settings and change % of trade for first take profit to 100 for getting the real ones. In the backtest, the real amount of opened trades was of 194.
Indicators used:
Supertrend
Atr stop loss by garethyeo
This is the fist strategy that I publish in tradingview, I will be glad with you for any suggestion, support or advice for future scripts. Do not doubt in make any question you have and if you liked this content, leave a boost. I plan to bring more strategies and useful content for you!
Grid Strategy Back Tester (Long/Short/Neutral)Preface
I'd like to send a thank you to @xxattaxx-DisDev.
The 'Line' Code, which was the most difficult to plan the Grid Indicator, was solved through the 'Grid Bot Simulator' script of @xxattaxx-DisDev.
A brief description of the indicators
These indicators are designed for backtesting of grid trading that can be opened on various exchanges.
Grid trading is a method of selling at particular intervals as prices rise and fall for gird interval price range.
This indicator is actually designed to see what the Long / Short / Neutral grid has achieved and how much it has achieved over a given period of time.
How to use
1. Lower Limit and Upper Limit are required when putting indicators on the chart.
After that, choose the 'Time' when to open the grid.
Also, select Long / Short / Neutral direction if necessary.
2. Statistics Table
Matched Grid shows how many grid pairs were engaged during the backtesting period.
The Daily Average Matching Profit is calculated based on the number of these closed grids.
Total Matching Profit is calculated as Matching Grid * Per Matching Profit.
Position Profit/Loss shows the benefits and losses from your current position.
Total Profit/Loss is sum of Total Matching Profit and Position Profit/Loss.
The Expanded APY shows the benefits of running the strategy on these terms for a year.
Max Loss of Upper is the maximum loss assumed to be directly at the top of the grid range.
BEP days (Upper) show how many days of maintenance relative to Average Matching Profit can result in greater profit than maximum loss if the grid continues to move within range.
(In the case of Long Strategy, it appears to be 'Min Profit', which shows minimal benefit if it reaches the top.)
Max Loss of Lower and BEP days (Lower) shows the opposite.
(In the case of Short Strategy, it is also referred to as 'Min Profit', which shows minimal benefit if it reaches the bottom.)
3. Grid Info
Total Grid Number, Upper Limit, and Lower Limit show the values you set in INPUT.
Grid Open Price shows the price for the period you decide to open.
Starting Position shows the number of positions that were initially held in the case of a Long / Short Strategy.
(0 for Neutral Strategy)
Per Grid qty shows how many positions are allocated to one grid
Grid Interval shows the spacing of each grid.
Per Matched Profit shows how much profit is generated when a single grid is matched.
Caution
Backtesting results for these indicators may vary depending on the time frame.
Therefore, I recommend that you use it only to compare Profit/Loss over time.
*In addition, there is a problem that all lines in the grid are not implemented, but it is independent of the backtest results.
--------------------------------------
서문
지표를 기획함에 있어서 가장 어려웠던 line 코드를 @xxattaxx-DisDev의 'Grid Bot Simulator' 스크립트를 통해 해결할 수 있었습니다.
이에 감사의 말씀을 드립니다.
해당 지표에 대한 간단한 설명
해당 지표는 다양한 거래소에서 오픈할 수 있는 그리드 매매에 대한 백테스팅을 위해 만들어졌습니다.
그리드매매는, 특정 가격 구간에 대해 가격이 오르고 내림에 따라 일정 간격에 맞춰 매매를 하는 방식입니다.
이 지표는 실질적으로 롱/숏/중립 그리드가 어떠한 성과를, 특정 기간동안 얼마나 냈는지를 확인하고자 만들어졌습니다.
사용방법
1. 인풋
지표를 차트위에 넣을 때, Lower Limit과 Upper Limit이 필요합니다.
그 후 그리드를 언제부터 오픈할 것인지를 선택하세요.
또, 필요하다면 Long / Short / Neutral의 방향을 선택하세요.
2. 그리드 통계
Matched Grid는, 백테스팅 기간동안 체결된 그리드 쌍이 몇개인지를 보여줍니다.
이 체결된 그리드의 갯수를 바탕으로 Daily Average Matched Profit이 계산됩니다.
Total Matched Profit은, Matched Grid * Per Matched Profit으로 계산됩니다.
Position Profit/Loss는, 현재 갖고 있는 포지션으로 인한 이익과 손실을 보여줍니다.
Total Matched Profit과 Position Profit/Loss를 합친 금액이 Total Profit/Loss가 됩니다.
Expcted APY는, 이러한 조건으로 전략을 1년동안 운영했을 때의 이익을 보여줍니다.
Max Loss of Upper는, 그리드 범위의 최상단에 바로 도달했을 경우를 가정한 최대 손실입니다.
BEP days(Upper)는, 그리드가 범위 내에서 계속 움직일 경우, Average Matched Profit을 기준으로 며칠동안 유지되어야 최대손실보다 더 큰 이익이 발생할 수 있는지를 보여줍니다.
(Long Strategy의 경우, ‘Min Profit’이라고 나타나는데, 최상단에 도달했을 경우 최소한의 이익을 보여줍니다)
Max Loss of Lower는 그 반대의 경우를 보여줍니다.
(Short Strategy의 경우, 역시 ‘Min Profit’이라고 나타나는데, 최하단에 도착했을 경우 최소한의 이익을 보여줍니다)
3. 그리드 정보
그리드 갯수, Upper Limt, Lower Limt은 자신이 설정한 값을 보여줍니다.
Grid Open Price는, 자신이 오픈하기로 정했던 기간의 가격을 보여줍니다.
Starting Position은, 롱/숏 그리드의 경우에 처음에 들고 시작했던 포지션의 갯수를 보여줍니다.
Neutral Strategy의 경우 0입니다.
Per Grid qty는, 하나의 그리드에 얼마만큼의 포지션이 배분되었는지를 보여주며
Grid Interval은 각 그리드의 간격을 보여줍니다.
또, Per Matched Profit은 하나의 그리드가 체결될 때 얼마만큼의 이익이 발생하는 지를 보여줍니다.
이러한 지표에 대한 역테스트 결과는 시간 프레임에 따라 달라질 수 있습니다.
따라서 시간 경과에 따른 손익을 비교할 때만 사용하는 것이 좋습니다.
*추가로, 그리드의 라인이 모두 구현되지 않는 문제가 있지만, 백테스팅 결과와는 무관합니다.
Linear EDCA v1.2Strategy Description:
Linear EDCA (Linear Enhanced Dollar Cost Averaging) is an enhanced version of the DCA fixed investment strategy. It has the following features:
1. Take the 1100-day SMA as a reference indicator, enter the buy range below the moving average, and enter the sell range above the moving average
2. The order to buy and sell is carried out at different "speed", which are set with two linear functions, and you can change the slope of the linear function to achieve different trading position control purposes
3. This fixed investment is a low-frequency strategy and only works on a daily level cycle
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Strategy backtest performance:
BTCUSD (September 2014~September 2022): Net profit margin 26378%, maximum floating loss 47.12% (2015-01-14)
ETHUSD (August 2018~September 2022): Net profit margin 1669%, maximum floating loss 49.63% (2018-12-14)
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How the strategy works:
Buying Conditions:
The closing price of the day is below the 1100 SMA, and the ratio of buying positions is determined by the deviation of the closing price from the moving average and the buySlope parameter
Selling Conditions:
The closing price of the day is above the 1100 SMA, and the ratio of the selling position is determined by the deviation of the closing price and the moving average and the sellSlope parameter
special case:
When the sellOffset parameter>0, it will maintain a small buy within a certain range above the 1100 SMA to avoid prematurely starting to sell
The maximum ratio of a single buy position does not exceed defInvestRatio * maxBuyRate
The maximum ratio of a single sell position does not exceed defInvestRatio * maxSellRate
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Version Information:
Current version v1.2 (the first officially released version)
v1.2 version setting parameter description:
defInvestRatio: The default fixed investment ratio, the strategy will calculate the position ratio of a single fixed investment based on this ratio and a linear function. The default 0.025 represents 2.5% of the position
buySlope: the slope of the linear function of the order to buy, used to control the position ratio of a single buy
sellSlope: the slope of the linear function of the order to sell, used to control the position ratio of a single sell
sellOffset: The offset of the order to sell. If it is greater than 0, it will keep a small buy within a certain range to avoid starting to sell too early
maxSellRate: Controls the maximum sell multiple. The maximum ratio of a single sell position does not exceed defInvestRatio * maxSellRate
maxBuyRate: Controls the maximum buy multiple. The maximum ratio of a single buy position does not exceed defInvestRatio * maxBuyRate
maPeriod: the length of the moving average, 1100-day MA is used by default
smoothing: moving average smoothing algorithm, SMA is used by default
useDateFilter: Whether to specify a date range when backtesting
settleOnEnd: If useDateFilter==true, whether to close the position after the end date
startDate: If useDateFilter==true, specify the backtest start date
endDate: If useDateFilter==true, specify the end date of the backtest
investDayofweek: Invest on the day of the week, the default is to close on Monday
intervalDays: The minimum number of days between each invest. Since it is calculated on a weekly basis, this number must be 7 or a multiple of 7
The v1.2 version data window indicator description (only important indicators are listed):
MA: 1100-day SMA
RoR%: floating profit and loss of the current position
maxLoss%: The maximum floating loss of the position. Note that this floating loss represents the floating loss of the position, and does not represent the floating loss of the overall account. For example, the current position is 1%, the floating loss is 50%, the overall account floating loss is 0.5%, but the position floating loss is 50%
maxGain%: The maximum floating profit of the position. Note that this floating profit represents the floating profit of the position, and does not represent the floating profit of the overall account.
positionPercent%: position percentage
positionAvgPrice: position average holding cost
--------------------------------
策略说明：
Linear EDCA（Linear Enhanced Dollar Cost Averaging）是一个DCA定投策略的增强版本，它具有如下特性：
1. 以1100日SMA均线作为参考指标，在均线以下进入定买区间，在均线以上进入定卖区间
2. 定买和定卖以不同的“速率”进行，它们用两条线性函数设定，并且你可以通过改变线性函数的斜率，以达到不同的买卖仓位控制的目的
3. 本定投作为低频策略，只在日级别周期工作
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策略回测表现：
BTCUSD（2014年09月~2022年09月）：净利润率26378%，最大浮亏47.12%（2015-01-14）
ETHUSD（2018年08~2022年09月）：净利润率1669%，最大浮亏49.63%（2018-12-14）
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策略工作原理：
买入条件：
当日收盘价在 1100 SMA 之下，由收盘价和均线的偏离度，以及buySlope参数决定买入仓位比例
卖出条件：
当日收盘价在 1100 SMA之上，由收盘价和均线的偏离度，以及sellSlope参数决定卖出仓位比例
特例：
当sellOffset参数>0，则在 1100 SMA以上一定范围内还会保持小幅买入，避免过早开始卖出
单次买入仓位比例最大不超过 defInvestRatio * maxBuyRate
单次卖出仓位比例最大不超过 defInvestRatio * maxSellRate
----------------
版本信息：
当前版本v1.2（第一个正式发布的版本）
v1.2版本设置参数说明：
defInvestRatio: 默认定投比例，策略会根据此比例和线性函数计算得出单次定投的仓位比例。默认0.025代表2.5%仓位
buySlope: 定买的线性函数斜率，用来控制单次买入的仓位倍率
sellSlope: 定卖的线性函数斜率，用来控制单次卖出的仓位倍率
sellOffset: 定卖的偏移度，如果大于0，则在一定范围内还会保持小幅买入，避免过早开始卖出
maxSellRate: 控制最大卖出倍率。单次卖出仓位比例最大不超过 defInvestRatio * maxSellRate
maxBuyRate: 控制最大买入倍率。单次买入仓位比例最大不超过 defInvestRatio * maxBuyRate
maPeriod: 均线长度，默认使用1100日MA
smoothing: 均线平滑算法，默认使用SMA
useDateFilter: 回测时是否要指定日期范围
settleOnEnd: 如果useDateFilter==true，在结束日之后是否平仓所持有的仓位平仓
startDate: 如果useDateFilter==true，指定回测开始日期
endDate: 如果useDateFilter==true，指定回测结束日期
investDayofweek: 每次在周几定投，默认在每周一收盘
intervalDays: 每次定投之间的最小间隔天数，由于是按周计算，所以此数字必须是7或7的倍数
v1.2版本数据窗口指标说明（只列出重要指标）：
MA：1100日SMA
RoR%: 当前仓位的浮动盈亏
maxLoss%: 仓位曾经的最大浮动亏损，注意此浮亏代表持仓仓位的浮亏情况，并不代表整体账户浮亏情况。例如当前仓位是1%，浮亏50%，整体账户浮亏是0.5%，但仓位浮亏是50%
maxGain%: 仓位曾经的最大浮动盈利，注意此浮盈代表持仓仓位的浮盈情况，并不代表整体账户浮盈情况。
positionPercent%: 仓位持仓占比
positionAvgPrice: 仓位平均持仓成本
DMI Swings (by Coinrule)The Directional Movement Index is a handy indicator that helps catch the direction in which the price of an asset is moving. It compares the prior highs and lows to draw three lines:
Positive directional line (+DI)
Negative directional line (-DI)
Average direction index (ADX)
DMI is simple to interpret. When +DI > - DI, it means the price is trending up. On the other hand, when -DI > +DI, the trend is weak or moving on the downside.
The ADX does not give an indication about the direction but about the strength of the trend.
Typically values of ADX above 25 mean that the trend is steeply moving up or down, based on the -DI and +D positioning. This script aims to capture swings in the DMI, and thus, in the trend of the asset, using a contrarian approach.
ENTRY
-DI is greater than +DI
ADX is greater than 45
EXIT
+DI is greater than -DI
ADX is greater than 45
Trading on high values of ADX, the strategy tries to spot extremely oversold and overbought conditions. Values of ADX above 45 may suggest that the trend has overextended and is may be about to reverse.
Our backtests suggest that this script performs well for very short-term scalping strategies on low time frames, such as the 1-minute.
The script considers a 0.1% trading fee to make results more realistic to those you can expect from live market conditions. So realistically, live results should be similar to backtested results.
You can plug this script directly into your crypto exchange using TradingView Signals on Coinrule.
Trade Safely!
Daily Close and 5/10 Robinhood TargetsThis script is super simple, just outputs a daily close line and also 5/10% targets higher and lower based on that price.
The reason I made this is somewhat simple which is what, ive noticed (havent statistically backtested) but many popular "robinhood stocks" when they run they tend to almost always tag 5 or 10% up or down.
The theory is something to do with the fact that robinhood alerts at those price levels, so when something like a BYND or RUN or TSLA or (pick a popular stock that runs) it tends to at least tap those levels. I rarely see it go up lets say, 4.33% and then turn around, typically it will at least wick if not pass 5% so using these might POSSIBLY be a level of alpha.
Use it for your own backtests though with something better.
PineScript v4 - Forex Pin-Bar Trading StrategyPineScript v4, forex trading robot based on the commonly used bullish / bearish pin-bar piercing the moving averages strategy.
I coded this robot to stress-test the PineScript v4 language to see how advanced it is, and whether I could port a forex trading strategy from MT4 to TradingView.
In my opinion, PineScript v4 is still not a professional coding language; for example you cannot use IF-statements to modify the contents of global variables; this makes complex robot behaviour difficult to implement. In addition, it is unclear if the programmer can use nested IF-ELSE, or nested FOR within IF.
The sequence of program execution is also unclear, and although complex order entry and exit appears to function properly, I am not completely comfortable with it.
Recommended Chart Settings:
Asset Class: Forex
Time Frame: H1
Long Entry Conditions:
a) Moving Average up trend, fast crosses above slow
b) Presence of a Bullish Pin Bar
c) Pin Bar pierces either Moving Average
d) Moving Averages must be sloping up, angle threshold (optional)
Short Entry Conditions:
a) Moving Average down trend, fast crosses below slow
b) Presence of a Bearish Pin Bar
c) Pin Bar pierces either Moving Average
d) Moving Averages must be sloping down, angle threshold (optional)
Exit Conditions:
a) Stoploss level is hit
b) Takeprofit level is hit
c) Moving Averages cross-back (optional)
Default Robot Settings:
Equity Risk (%): 3 //how much account balance to risk per trade
Stop Loss (x*ATR, Float): 2.1 //stoploss = x * ATR, you can change x
Risk : Reward (1 : x*SL, Float): 3.1 //takeprofit = x * stop_loss_distance, you can change x
Fast MA (Period): 20 //fast moving average period
Slow MA (Period): 50 //slow moving average period
ATR (Period): 14 //average true range period
Use MA Slope (Boolean): true //toggle the requirement of the moving average slope
Bull Slope Angle (Deg): 1 //angle above which, moving average is considered to be sloping up
Bear Slope Angle (Deg): -1 //angle below which, moving average is considered to be sloping down
Exit When MA Re-Cross (Boolean): true //toggle, close trade if moving average crosses back
Cancel Entry After X Bars (Period): 3 //cancel the order after x bars not triggered, you can change x
Backtest Results (2019 to 2020, H1, Default Settings):
EURJPY - 111% profit, 2.631 profit factor, 16.43% drawdown
EURUSD - 103% profit, 2.899 profit factor, 14.95% drawdown
EURAUD - 76.75% profit, 1.8 profit factor, 17.99% drawdown
NZDUSD - 64.62% profit, 1.727 profit factor, 19.14% drawdown
GBPUSD - 58.73% profit, 1.663 profit factor, 15.44% downdown
AUDJPY - 48.71% profit, 1.635 profit factor, 11.81% drawdown
USDCHF - 30.72% profit, 1.36 profit factor, 22.63% drawdown
AUDUSD - 8.54% profit, 1.092 profit factor, 19.86% drawdown
EURGBP - 0.03% profit, 1.0 profit factor, 29.66% drawdown
USDJPY - 1.96% loss, 0.972 profit factor, 28.37% drawdown
USDCAD - 6.36% loss, 0.891 profit factor, 21.14% drawdown
GBPJPY - 28.27% loss, 0.461 profit factor, 39.13% drawdown
To reduce the possibility of curve-fitting, this robot was backtested on 12 popular forex currencies, as shown above. The robot was profitable on 8 out of 12 currencies, breakeven on 1, and made a loss on 3.
The default robot settings could be over-fitting for the EUR, as we can see out-sized performance for the EUR pairs, with the exception of the EURGBP. We can see that GBPJPY made the largest loss, so these two pairs could be related.
Risk Warning:
This is a forex trading strategy that involves high risk of equity loss, and backtest performance will not equal future results. You agree to use this script at your own risk.
Bollinger Bands BAT/USDT 30minThis is ready to use Bollinger Band strategy that was backtested on the data from the previous year 2019.
The main purpose of this strategy is to determine trades with the highest probability of success, to keep a consistent portfolio growth throughout the year. This strategy cherry-picks the most reliable points of entry on a particular timeframe (30m) for the particular asset (BAT/USDT). The backtest shows a great result of 78.95% profitability with the maximum drawdown of -4.02%. This is one of my strategies out of the group of automated strategies that helps to grow my portfolio steadily.
You are welcome to change inputs and backtest the following strategy. Any comments or ideas would be appreciated.
If you are happy with existing results and would like to automate the strategy, which can be done through alerts, then you need to convert it to study and add alerts in the code.
Let me know if you are interested in that and I will create a study based on this strategy.
Institutional PointOverview Institutional Point is a sophisticated data-mining indicator designed to identify and track "institutional footprints" by isolating the single candle with the highest volume relative to a specific time anchor. Unlike traditional volume profiles that aggregate data into price bins, this script pinpoints the exact temporal origin of massive liquidity injections.
Core Methodology The script operates on a multi-timeframe analysis engine (MTF). It scans sub-chart data (2-minute or 15-minute intervals) to find the absolute maximum volume peak within a defined period. Once the "Institutional Point" is identified:
Source Identification: The origin candle is highlighted in white, signaling a high-conviction entry or exit by large-scale market participants.
Zone Projection: A borderless "Institutional Zone" is projected forward from the spike’s high/low range.
Dynamic Interaction: The zone remains active until the price revisits the area (mitigation) or until the time-based expiration is reached.
Anchor Modes & Precision
8-Hour Cycle: Optimized for high-frequency scalping. Anchors reset at 00:00, 08:00, and 16:00. Utilizes ultra-precise 2-minute volume detection.
Daily Session: Designed for intraday and swing traders. Anchors to the Daily Open. Utilizes 2-minute volume detection to isolate precise institutional orders.
Weekly Cycle: Built for identifying major structural pivots. Anchors to the Weekly Open. Utilizes 15-minute volume detection for macro-liquidity analysis.
Key Features
Naked Level Tracking: Zones automatically stop extending the moment they are "hit" by price action, providing a clean visual of unmitigated liquidity.
Anti-Noise Filter: Automatically excludes Saturday and Sunday data to maintain statistical integrity across global markets.
Minimalist Interface: High-contrast visual design focused on scannability and professional chart aesthetics.
Use Cases
Data Science & Backtesting: Ideal for measuring the "Z-Score" or "Percentile Distance" from institutional peaks.
Supply & Demand Trading: Automated identification of the "Origin of the Move."
Magnet Analysis: Tracking "Naked" volume spikes as high-probability magnets for future price mean reversion.
Strategy MTF ScannerDescription:
Stop guessing which timeframe is best for your strategy. This tool performs a "Top-Down Analysis" instantly by running a unified strategy simulation across 5 different timeframes simultaneously.
Why Use This?
A strategy that fails on the 1-Hour chart might print massive returns on the 4-Hour chart due to reduced noise. This scanner calculates the Equity Curve, Max Drawdown, and Win Rate for 15m, 1H, 4H, Daily, and Weekly charts (customizable) and presents the winner in a dashboard.
Features:
Simultaneous Backtesting: Runs 5 independent simulations inside request.security.
Equity & Drawdown Tracking: See not just how much you make, but how much risk is required on each timeframe.
Instant Comparison: Identify "Fractal Resonance" where multiple timeframes align in profitability.
Strategy Logic (Fully Customizable):
The default entry logic is a generic EMA 9/21 Crossover with a Trend Filter.
Note: This is an open-source framework. You can modify the calc_strategy_results function in the source code to substitute the crossover with your own custom entry conditions (RSI, Stochastic, Price Action, etc.).
Workflow:
Load this scanner to identify the dominant timeframe (e.g., 4H).
Switch your chart to the 4H timeframe.
Use the Strategy Grid Optimizer to fine-tune the specific EMA and ATR settings for that timeframe.
%-to-Tick Trailing Stop & VisualizerPercent-to-Tick Trailing Stop (strategy.exit Framework + Visualizer)
Overview
This script focuses on exit management and visualization, not entry performance. The included MA crossover entry is intentionally simple and replaceable.
Core idea (Percent → Tick conversion)
strategy.exit() trailing parameters are tick-based (trail_points, trail_offset, and loss).
This script lets you input distances in percent (%) and converts them into integer ticks using syminfo.mintick, making the same exit logic portable across most tick-based symbols/exchanges with different tick sizes.
//==What it provides==//
1. % → tick conversion for:
- Fixed stop loss (loss)
- Trailing activation distance (trail_points)
- Trailing offset distance (trail_offset)
2. On-chart visualization:
- Entry average price
- Trailing activation threshold
- Fixed stop-loss line
- Trailing stop line (with an exit-bar alignment attempt to reduce gaps)
//==How to use==//
1. Keep the included MA crossover entries, or replace them with your own entries.
2. Configure:
- Fixed Stop Loss % (loss_pct)
- Trailing Activation % (t_points_pct)
- Trailing Offset % (t_offset_pct)
3. Adjust commission/slippage defaults to match your market.
//==Important limitations (must read)==//
- calc_on_every_tick=true recalculates on realtime bars only; historical bars are evaluated differently. Backtests can differ from realtime behavior and may change after reload.
- Tick rounding: percent distances are rounded to integer ticks, so small differences can occur depending on tick size and price level.
- For more realistic intrabar backtesting, consider enabling Bar Magnifier in Strategy Properties (if available).
# Average Entry Price (Basis):
"Calculations are based on the position's average entry price (strategy.position_avg_price)."
# Pine Script v6:
"Written in the latest Pine Script v6."
요약
이 스크립트의 핵심은 “진입 전략”이 아니라 **strategy.exit()의 tick 기반 트레일링 파라미터를 % 입력으로 일반화(%→ticks 변환)**하여, 다양한 심볼/거래소의 서로 다른 tick size 환경에서도 동일한 exit 로직을 재사용할 수 있게 만든 “청산 프레임워크”입니다. 또한 calc_on_every_tick=true 환경에서 트리거/손절/트레일 라인을 실시간에 가깝게 시각화하는 데 중점을 두었습니다.
단, calc_on_every_tick은 실시간 바에서만 틱 단위 재계산이 적용되며, 히스토리 바/백테스트는 평가 방식이 달라 결과가 다를 수 있습니다.
HMA 9/50 Crossover + RSI 50 Filter1. The Core Indicators
HMA 9 (Fast): Acts as the primary trigger line. Its unique calculation minimizes lag compared to standard moving averages, allowing for faster entries.
HMA 50 (Slow): Defines the medium-term trend direction and acts as the "anchor" for crossover signals.
RSI 14: Serves as a "momentum gate." Instead of traditional overbought/oversold levels, we use the 50 midline to confirm that the directional strength supports the crossover.
2. Entry Conditions
Long Entry: Triggered when the HMA 9 crosses above the HMA 50 AND the RSI is greater than 50.
Short Entry: Triggered when the HMA 9 crosses below the HMA 50 AND the RSI is less than 50.
3. Execution & Reversal
This strategy is currently configured as an Always-in-the-Market system.
A "Long" position is automatically closed when a "Short" signal is triggered.
To prevent "pyramiding" (buying multiple positions in one direction), the script checks the current position_size before opening new entries.
How to Use
Timeframe: Optimized for 3-minute (3m) candles but can be tuned for 1m to 15m scalping.
Settings: Use the Inputs panel to adjust HMA lengths based on the volatility of your specific asset (e.g., shorter for stable stocks, longer for volatile crypto).
Visuals:
Aqua Line: HMA 9
Orange Line: HMA 50
Green Background: Bullish RSI Momentum (> 50)
Red Background: Bearish RSI Momentum (< 50)
Risk Disclosure
Whipsaws: This strategy is likely to underperform in sideways markets.
Backtesting: Past performance does not guarantee future results. Always test this strategy in the Strategy Tester with appropriate commission and slippage settings before live use.
TradingView Alert Adapter for AlgoWayTRALADAL is a universal TradingView alert adapter designed for traders who work with indicators and want to test and automate indicator-based signals in a structured way.
It allows users to convert indicator outputs into a TradingView strategy and forward the same logic through alerts for multi-platform execution via AlgoWay.
This script can be used as TradingView indicator automation, enabling traders to build a TradingView strategy from indicators and route TradingView alerts through an AlgoWay connector TradingView workflow for multi-platform execution.
Why this adapter is needed
Most TradingView indicators are not available as strategies.
Traders often receive visual signals or alerts but have no access to objective statistics such as win rate, drawdown, or profit factor.
This adapter solves that problem by providing a generic framework that transforms indicator signals into a backtestable strategy — without modifying indicator code and without requiring Pine Script knowledge.
Input source–based design (including closed indicators)
All conditions in TRALADAL are built using input sources, which means you can connect:
Event-based signals (1 / non-zero values, arrows, shapes)
Indicator lines and values (EMA, VWAP, RSI, MACD, etc.)
Outputs from invite-only or closed-source indicators
If an indicator produces a visible signal or alert-compatible output, it can be evaluated and tested using this adapter, even when the source code is locked.
Three-level signal logic
The strategy uses a three-layer condition model commonly applied in discretionary and systematic trading:
Signal — primary entry trigger
Confirmation — directional validation
Filter — additional noise reduction
Each level can be enabled independently and combined using AND / OR logic, allowing traders to test multi-indicator systems without writing complex scripts.
Risk management and alert execution
The adapter supports practical risk parameters:
Stop Loss (pips)
Take Profit (pips)
Trailing Stop (pips)
Two execution modes are available:
Strategy Mode — risk rules are applied inside the TradingView Strategy Tester
Alert Mode — risk parameters are embedded into structured TradingView alerts and handled by AlgoWay during execution
Position sizing follows TradingView conventions (percent of equity, cash, or contracts) to keep strategy results and alerts aligned.
Typical use cases
This TradingView alert adapter is intended for:
Indicator-based trading systems
Backtesting signals from closed or invite-only scripts
Comparing multiple indicators within a single strategy
Sending TradingView alerts to external trading platforms via AlgoWay
The adapter does not generate signals or trading recommendations.
Its purpose is to provide a transparent and testable workflow from indicator signals to TradingView alerts and automated execution.
Day Trading MA Crossover IndicatorDay Trading MA Crossover Indicator Overview The Day Trading MA Crossover Indicator is a simple yet effective tool designed for day traders to identify potential buy and sell opportunities based on moving average crossovers. It plots two customizable moving averages on your chart and generates clear visual signals when they cross, helping you spot trend reversals or continuations in fast-paced markets.This indicator is ideal for intraday trading on lower timeframes (e.g., 5-min, 15-min charts) but can be adapted for swing trading or higher timeframes. It's built with flexibility in mind, allowing you to tweak the MA lengths and types to suit your strategy.Key FeaturesMoving Average Crossovers: Generates "BUY" signals when the fast MA crosses above the slow MA (potential bullish entry) and "SELL" signals when it crosses below (potential bearish entry or exit).
Visual Signals: Green "BUY" labels below bars for long entries and red "SELL" labels above bars for short entries or exits. Optional subtle background coloring highlights signals for quick spotting.
Customizable Parameters:Fast MA Length (default: 9): Period for the shorter moving average.
Slow MA Length (default: 21): Period for the longer moving average.
MA Type (default: EMA): Choose between SMA (Simple), EMA (Exponential), or WMA (Weighted) for different smoothing behaviors.
Overlay Mode: Plots directly on your price chart without cluttering separate panes.
Lightweight and Efficient: Minimal computation for real-time performance on TradingView.
How It WorksMoving Averages Calculation: The indicator computes two MAs based on your selected type and lengths using closing prices.
Signal Detection: A buy signal triggers on an upward crossover (fast MA > slow MA), indicating potential momentum shift to the upside. A sell signal triggers on a downward crossunder (fast MA < slow MA), signaling possible downside momentum.
Visual Aids: Signals appear as labeled shapes with optional background tints to emphasize key bars.
Usage TipsFor Day Trading: Apply on volatile instruments like forex pairs, stocks, or crypto. Combine with support/resistance levels or other indicators (e.g., RSI for overbought/oversold confirmation) to filter false signals in ranging markets.
Backtesting: Test on historical data to optimize MA lengths for your asset—shorter periods for aggressive trading, longer for smoother trends.
Risk Management: Always use stop-losses and position sizing. Signals are not foolproof and work best in trending conditions.
Customization: Adjust inputs via the indicator settings panel after adding it to your chart.
Example SetupOn a 5-min EUR/USD chart: Use EMA (9/21) for quick crossovers. Look for buy signals above key support with increasing volume.
Avoid choppy markets where frequent false crossovers ("whipsaws") can occur.
This indicator is provided for educational and informational purposes only. It is not financial advice, and past performance does not guarantee future results. Trading involves risk; consult a professional advisor before using any strategy. If you have feedback or suggestions for improvements, feel free to comment!
Key Support and ResistanceKEY SUPPORT AND RESISTANCE - USER GUIDE
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OVERVIEW
This indicator automatically identifies and displays key support and resistance levels based on swing highs and swing lows. It uses pivot point detection to mark significant price levels where the market has previously shown reactions, helping traders identify potential entry/exit points and key decision zones.
KEY FEATURES
• Automatic Level Detection: Identifies swing highs (resistance) and swing lows (support) using pivot point analysis
• Dynamic Line Management: Displays only recent levels within a specified lookback period to keep charts clean
• Auto-Extending Lines: Projects support/resistance levels forward to anticipate future price interactions
• Color-Coded Levels: Red lines for resistance, green lines for support for easy visual identification
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PARAMETERS
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Left Bars (Default: 10)
• Minimum: 5 bars
• Number of bars to the left of the pivot point
• Higher values = more significant levels but fewer signals
• Lower values = more sensitive detection but may include minor swings
Right Bars (Default: 10)
• Minimum: 5 bars
• Number of bars to the right of the pivot point
• Must be confirmed by price action before the level is drawn
• Balances between confirmation delay and signal accuracy
Show Last N Bars (Default: 200)
• Minimum: 10 bars
• Only displays support/resistance levels detected within the most recent N bars
• Keeps your chart clean by removing outdated levels
• Adjust based on your trading timeframe and style
Line Extension Length (Default: 48)
• Minimum: 1 bar
• How many bars forward the support/resistance lines extend
• Helps visualize potential future price interactions
• Longer extensions useful for swing trading, shorter for day trading
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HOW TO USE
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FOR SWING TRADERS
1. Use default settings (10/10) or increase to 15/15 for more significant levels
2. Set "Show Last N Bars" to 300-500 to capture longer-term levels
3. Look for price reactions when approaching these levels
4. Combine with volume analysis for confirmation
FOR DAY TRADERS
1. Consider reducing Left/Right Bars to 7-8 for more frequent signals
2. Set "Show Last N Bars" to 100-150 to focus on recent action
3. Reduce "Line Extension Length" to 20-30 bars
4. Watch for intraday bounces or breakouts at these levels
TRADING STRATEGIES
Bounce Trading (Mean Reversion)
• Enter long when price approaches green support lines
• Enter short when price approaches red resistance lines
• Use stop loss just beyond the support/resistance level
• Best in ranging or consolidating markets
Breakout Trading (Trend Following)
• Wait for price to break through resistance (bullish) or support (bearish)
• Confirm with increased volume
• Previous resistance becomes new support (and vice versa)
• Best in trending markets
Multi-Timeframe Analysis
• Check higher timeframe levels for major support/resistance zones
• Use lower timeframe levels for precise entry/exit timing
• Confluence of multiple timeframe levels creates strong zones
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IMPORTANT NOTES
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Line Confirmation Delay
• Lines appear with a delay equal to "Right Bars" parameter
• This delay ensures the pivot point is confirmed
• Real-time level detection requires price action confirmation
Chart Clarity
• Maximum 500 lines can be displayed (TradingView limitation)
• Adjust "Show Last N Bars" if chart becomes too cluttered
• Old lines automatically delete when outside the lookback period
False Signals
• Not all support/resistance levels will hold
• Use additional confirmation (volume, candlestick patterns, other indicators)
• Markets can break through levels, especially during high-impact news
BEST PRACTICES
1. Combine with Other Analysis: Use alongside trend indicators, volume, and price action patterns
2. Context Matters: Consider overall market trend and structure
3. Risk Management: Always use stop losses; don't rely solely on S/R levels
4. Market Conditions: More effective in liquid, actively traded markets
5. Backtesting: Test settings on your specific instrument and timeframe before live trading
TROUBLESHOOTING
Too Many Lines?
• Increase "Left Bars" and "Right Bars" values
• Decrease "Show Last N Bars" value
Too Few Lines?
• Decrease "Left Bars" and "Right Bars" values
• Increase "Show Last N Bars" value
Lines Not Appearing?
• Ensure sufficient price data is loaded on your chart
• Check that "Right Bars" have passed since the last swing point
• Verify indicator is properly loaded (refresh if needed)
TECHNICAL DETAILS
• Uses ta.pivothigh() and ta.pivotlow() functions for level detection
• Implements array-based line management for efficient rendering
• Automatic cleanup of outdated lines to maintain performance
• Overlay indicator - displays directly on price chart
Disclaimer: This indicator is for educational and informational purposes only. It does not constitute financial advice. Always conduct your own research and risk assessment before making trading decisions.
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中文使用指南
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概述
本指標自動識別並顯示基於波段高點和低點的關鍵支撐阻力位。使用樞軸點檢測標記市場先前反應的重要價格水平，幫助交易者識別潛在的進出場點和關鍵決策區域。
主要功能
• 自動水平檢測：使用樞軸點分析識別波段高點（阻力）和波段低點（支撐）
• 動態線條管理：僅顯示指定回看期內的近期水平，保持圖表清晰
• 自動延伸線條：將支撐阻力水平向前投影，預測未來價格互動
• 顏色編碼：紅線表示阻力，綠線表示支撐，便於視覺識別
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參數說明
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左側K棒數（預設：10）
• 最小值：5根K棒
• 樞軸點左側的K棒數量
• 數值越高 = 水平越重要但訊號越少
• 數值越低 = 檢測更敏感但可能包含次要波動
右側K棒數（預設：10）
• 最小值：5根K棒
• 樞軸點右側的K棒數量
• 必須經過價格行為確認後才繪製水平
• 在確認延遲和訊號準確性之間取得平衡
顯示最近N根K棒內的點（預設：200）
• 最小值：10根K棒
• 僅顯示最近N根K棒內檢測到的支撐阻力水平
• 透過移除過時水平保持圖表清晰
• 根據您的交易時間框架和風格調整
線條延伸長度（預設：48）
• 最小值：1根K棒
• 支撐阻力線向前延伸的K棒數
• 幫助視覺化潛在的未來價格互動
• 較長延伸適合波段交易，較短適合當沖交易
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使用方法
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波段交易者
1. 使用預設設定（10/10）或增加至15/15以獲得更重要的水平
2. 將「顯示最近N根K棒」設為300-500以捕捉長期水平
3. 觀察價格接近這些水平時的反應
4. 結合成交量分析進行確認
當沖交易者
1. 考慮將左右側K棒減少至7-8以獲得更頻繁的訊號
2. 將「顯示最近N根K棒」設為100-150以專注於近期行情
3. 將「線條延伸長度」減少至20-30根K棒
4. 觀察日內在這些水平的反彈或突破
交易策略
反彈交易（均值回歸）
• 當價格接近綠色支撐線時做多
• 當價格接近紅色阻力線時做空
• 在支撐阻力水平之外設置止損
• 在區間或盤整市場中效果最佳
突破交易（趨勢跟隨）
• 等待價格突破阻力（看漲）或支撐（看跌）
• 以增加的成交量確認
• 先前的阻力成為新的支撐（反之亦然）
• 在趨勢市場中效果最佳
多時間框架分析
• 檢查更高時間框架的主要支撐阻力區域
• 使用較低時間框架進行精確的進出場時機
• 多個時間框架水平的匯合創造強大區域
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重要注意事項
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線條確認延遲
• 線條出現時會有等於「右側K棒數」參數的延遲
• 此延遲確保樞軸點被確認
• 實時水平檢測需要價格行為確認
圖表清晰度
• 最多可顯示500條線（TradingView限制）
• 如果圖表變得太雜亂，請調整「顯示最近N根K棒」
• 超出回看期的舊線會自動刪除
假訊號
• 並非所有支撐阻力水平都會守住
• 使用額外確認（成交量、K棒型態、其他指標）
• 市場可能突破水平，特別是在重大新聞期間
最佳實踐
1. 結合其他分析：與趨勢指標、成交量和價格行為型態一起使用
2. 背景很重要：考慮整體市場趨勢和結構
3. 風險管理：始終使用止損；不要僅依賴支撐阻力水平
4. 市場條件：在流動性高、活躍交易的市場中更有效
5. 回測：在實盤交易前，在您的特定商品和時間框架上測試設定
故障排除
線條太多？
• 增加「左側K棒數」和「右側K棒數」數值
• 減少「顯示最近N根K棒」數值
線條太少？
• 減少「左側K棒數」和「右側K棒數」數值
• 增加「顯示最近N根K棒」數值
線條未出現？
• 確保圖表上載入了足夠的價格數據
• 檢查自上次波動點以來是否已過「右側K棒數」
• 驗證指標是否正確載入（如需要請刷新）
技術細節
• 使用 ta.pivothigh() 和 ta.pivotlow() 函數進行水平檢測
• 實施基於陣列的線條管理以實現高效渲染
• 自動清理過時線條以保持性能
• 疊加指標 - 直接顯示在價格圖表上
免責聲明：本指標僅供教育和資訊目的。不構成財務建議。在做出交易決策前，請務必進行自己的研究和風險評估。
Watermark | Bar Time | Average Daily RangeMulti Info Panel & Watermark
Multi Info Panel & Watermark is a utility indicator that displays several pieces of chart information in a single, customizable panel. It is designed to support intraday and swing analysis by making key data—such as symbol details, date, and average daily range—easy to see at a glance, as well as providing simple tools for notes and backtesting.
Features
Watermark / Custom Note
Optional text overlay that can be used as a watermark or personal note.
Can display a strategy name, reminder, or any other user-defined label on the chart.
Ticker Info
Shows information about the currently active symbol on the chart (for example, symbol name and other basic details depending on the inputs).
Helps keep track of which market or pair is being analyzed, especially when using multiple charts.
Current Date
Displays the current date directly on the chart.
Useful for screenshots, journaling, and documenting analysis.
Average Daily Range (ADR)
Calculates the average daily range of the active symbol over a user-defined number of recent days.
Helps visualize how much price typically moves in a day, which can support position sizing, target setting, or volatility awareness within your own trading approach.
Open Bar Time Marker
Marks the open time of a selected bar (for example, a session open or a specific reference bar).
Primarily intended as a visual aid for manual backtesting and reviewing historical price action.
Usage
Use the watermark and ticker info to keep your charts labeled and organized.
Refer to the ADR readout to understand typical daily volatility of the instrument you are studying.
Use the date and open bar time marker when creating screenshots, trade journals, or when replaying historical sessions for review.
This script does not generate trading signals and does not guarantee any performance or results. It is provided solely as an informational and visualization tool. Always combine it with your own analysis, risk management, and decision-making. Nothing in this indicator or description should be considered financial advice.
BTC Mon 8am Buy / Wed 2pm Sell (NY Time, Daily + Intraday)This strategy implements a fixed weekly time-based trading schedule for Bitcoin, using New York market hours as the reference clock. It is designed to test whether a consistent pattern exists between early-week accumulation and mid-week distribution in BTC price behavior.
Entry Rule — Monday 8:00 AM (NY Time)
The strategy enters a long position every Monday at exactly 08:00 AM Eastern Time, one hour after the U.S. equities market pre-open activity begins influencing global liquidity.
This timing attempts to capture early-week directional moves in Bitcoin, which sometimes occur as traditional markets come online.
Exit Rule — Wednesday 2:00 PM (NY Time)
The strategy closes the position every Wednesday at 2:00 PM Eastern Time, a point in the week where:
U.S. equity markets are still open
BTC often experiences mid-week volatility rotations
Liquidity is generally high
This exit removes exposure before later-week uncertainty and gives a consistent, measurable time window for each trade.
Timeframe Compatibility
Works on intraday charts (recommended 1h or lower) using precise time-based triggers.
Also runs on daily charts, where entries and exits occur on the Monday and Wednesday bars respectively (daily charts cannot show intraday timestamps).
All timestamps are synced to America/New_York regardless of the exchange’s native timezone.
Trading Frequency
Exactly one trade per week, preventing overtrading and allowing comparison of weekly performance across years of historical BTC price data.
Purpose of the Strategy
This is not a value-based or trend-following system, but a behavioral/time-cycle analysis tool.
It helps evaluate whether a repeating short-term edge exists based solely on:
Weekday timing
Liquidity cycles
Institutional market influence
BTC’s habitual early-week momentum patterns
It is ideal for:
Backtesting weekly BTC behavior
Studying time-based edges
Comparing alternative weekday/time combinations
Visualizing weekly P&L structure
Risk Notes
This strategy does not attempt to predict price direction and should not be assumed profitable without robust backtesting.
Time-based edges can appear, disappear, or invert depending on macro conditions.
There is no stop loss or risk management included by default, so the strategy reflects raw timing-based performance.
