Multi-Regression StrategyIntroducing the "Multi-Regression Strategy" (MRS) , an advanced technical analysis tool designed to provide flexible and robust market analysis across various financial instruments.
This strategy offers users the ability to select from multiple regression techniques and risk management measures, allowing for customized analysis tailored to specific market conditions and trading styles.
Core Components:
Regression Techniques:
Users can choose one of three regression methods:
1 - Linear Regression: Provides a straightforward trend line, suitable for steady markets.
2 - Ridge Regression: Offers a more stable trend estimation in volatile markets by introducing a regularization parameter (lambda).
3 - LOESS (Locally Estimated Scatterplot Smoothing): Adapts to non-linear trends, useful for complex market behaviors.
Each regression method calculates a trend line that serves as the basis for trading decisions.
Risk Management Measures:
The strategy includes nine different volatility and trend strength measures. Users select one to define the trading bands:
1 - ATR (Average True Range)
2 - Standard Deviation
3 - Bollinger Bands Width
4 - Keltner Channel Width
5 - Chaikin Volatility
6 - Historical Volatility
7 - Ulcer Index
8 - ATRP (ATR Percentage)
9 - KAMA Efficiency Ratio
The chosen measure determines the width of the bands around the regression line, adapting to market volatility.
How It Works:
Regression Calculation:
The selected regression method (Linear, Ridge, or LOESS) calculates the main trend line.
For Ridge Regression, users can adjust the lambda parameter for regularization.
LOESS allows customization of the point span, adaptiveness, and exponent for local weighting.
Risk Band Calculation:
The chosen risk measure is calculated and normalized.
A user-defined risk multiplier is applied to adjust the sensitivity.
Upper and lower bounds are created around the regression line based on this risk measure.
Trading Signals:
Long entries are triggered when the price crosses above the regression line.
Short entries occur when the price crosses below the regression line.
Optional stop-loss and take-profit mechanisms use the calculated risk bands.
Customization and Flexibility:
Users can switch between regression methods to adapt to different market trends (linear, regularized, or non-linear).
The choice of risk measure allows adaptation to various market volatility conditions.
Adjustable parameters (e.g., regression length, risk multiplier) enable fine-tuning of the strategy.
Unique Aspects:
Comprehensive Regression Options:
Unlike many indicators that rely on a single regression method, MRS offers three distinct techniques, each suitable for different market conditions.
Diverse Risk Measures: The strategy incorporates a wide range of volatility and trend strength measures, going beyond traditional indicators to provide a more nuanced view of market dynamics.
Unified Framework:
By combining advanced regression techniques with various risk measures, MRS offers a cohesive approach to trend identification and risk management.
Adaptability:
The strategy can be easily adjusted to suit different trading styles, timeframes, and market conditions through its various input options.
How to Use:
Select a regression method based on your analysis of the current market trend (linear, need for regularization, or non-linear).
Choose a risk measure that aligns with your trading style and the market's current volatility characteristics.
Adjust the length parameter to match your preferred timeframe for analysis.
Fine-tune the risk multiplier to set the desired sensitivity of the trading bands.
Optionally enable stop-loss and take-profit mechanisms using the calculated risk bands.
Monitor the regression line for potential trend changes and the risk bands for entry/exit signals.
By offering this level of customization within a unified framework, the Multi-Regression Strategy provides traders with a powerful tool for market analysis and trading decision support. It combines the robustness of regression analysis with the adaptability of various risk measures, allowing for a more comprehensive and flexible approach to technical trading.
Linear
YinYang RSI Volume Trend StrategyThere are many strategies that use RSI or Volume but very few that take advantage of how useful and important the two of them combined are. This strategy uses the Highs and Lows with Volume and RSI weighted calculations on top of them. You may be wondering how much of an impact Volume and RSI can have on the prices; the answer is a lot and we will discuss those with plenty of examples below, but first…
How does this strategy work?
It’s simple really, when the purchase source crosses above the inner low band (red) it creates a Buy or Long. This long has a Trailing Stop Loss band (the outer low band that's also red) that can be adjusted in the Settings. The Stop Loss is based on a % of the inner low band’s price and by default it is 0.1% lower than the inner band’s price. This Stop Loss is not only a stop loss but it can also act as a Purchase Available location.
You can get back into a trade after a stop loss / take profit has been hit when your Reset Purchase Availability After condition has been met. This can either be at Stop Loss, Entry or None.
It is advised to allow it to reset in case the stop loss was a fake out but the call was right. Sometimes it may trigger stop loss multiple times in a row, but you don’t lose much on stop loss and you gain lots when the call is right.
The Take Profit location is the basis line (white). Take Profit occurs when the Exit Source (close, open, high, low or other) crosses the basis line and then on a different bar the Exit Source crosses back over the basis line. For example, if it was a Long and the bar’s Exit Source closed above the basis line, and then 2 bars later its Exit Source closed below the basis line, Take Profit would occur. You can disable Take Profit in Settings, but it is very useful as many times the price will cross the Basis and then correct back rather than making it all the way to the opposing zone.
Longs:
If for instance your Long doesn’t need to Take Profit and instead reaches the top zone, it will close the position when it crosses above the inner top line (green).
Please note you can change the Exit Source too which is what source (close, open, high, low) it uses to end the trades.
The Shorts work the same way as the Long but just opposite, they start when the purchase source crosses under the inner upper band (green).
Shorts:
Shorts take profit when it crosses under the basis line and then crosses back.
Shorts will Stop loss when their outer upper band (green) is crossed with the Exit Source.
Short trades are completed and closed when its Exit Source crosses under the inner low red band.
So, now that you understand how the strategy works, let’s discuss why this strategy works and how it is profitable.
First we will discuss Volume as we deem it plays a much bigger role overall and in our strategy:
As I’m sure many of you know, Volume plays a huge factor in how much something moves, but it also plays a role in the strength of the movement. For instance, let’s look at two scenarios:
Bitcoin’s price goes up $1000 in 1 Day but the Volume was only 10 million
Bitcoin’s price goes up $200 in 1 Day but the Volume was 40 million
If you were to only look at the price, you’d say #1 was more important because the price moved x5 the amount as #2, but once you factor in the volume, you know this is not true. The reason why Volume plays such a huge role in Price movement is because it shows there is a large Limit Order battle going on. It means that both Bears and Bulls believe that price is a good time to Buy and Sell. This creates a strong Support and Resistance price point in this location. If we look at scenario #2, when there is high volume, especially if it is drastically larger than the average volume Bitcoin was displaying recently, what can we decipher from this? Well, the biggest take away is that the Bull’s won the battle, and that likely when that happens we will see bullish movement continuing to happen as most of the Bears Limit Orders have been fulfilled. Whereas with #2, when large price movement happens and Bitcoin goes up $1000 with low volume what can we deduce? The main takeaway is that Bull’s pressured the price up with Market Orders where they purchased the best available price, also what this means is there were very few people who were wanting to sell. This generally dictates that Whale Limit orders for Sells/Shorts are much higher up and theres room for movement, but it also means there is likely a whale that is ready to dump and crash it back down.
You may be wondering, what did this example have to do with YinYang RSI Volume Trend Strategy? Well the reason we’ve discussed this is because we use Volume multiple times to apply multiplications in our calculations to add large weight to the price when there is lots of volume (this is applied both positively and negatively). For instance, if the price drops a little and there is high volume, our strategy will move its bounds MUCH lower than the price actually dropped, and if there was low volume but the price dropped A LOT, our strategy will only move its bounds a little. We believe this reflects higher levels of price accuracy than just price alone based on the examples described above.
Don’t believe us?
Here is with Volume NOT factored in (VWMA = SMA and we remove our Volume Filter calculation):
Which produced -$2880 Profit
Here is with our Volume factored in:
Which produced $553,000 (55.3%)
As you can see, we wen’t from $-2800 profit with volume not factored to $553,000 with volume factored. That's quite a big difference! (Please note previous success does not predict future success we are simply displaying the $ amounts as example).
Now how about RSI and why does it matter in this strategy?
As I’m sure most of you are aware, RSI is one of the leading indicators used in trading. For this reason we figured it would only make sense to incorporate it into our calculations. We fiddled with RSI for quite awhile and sometimes what logically seems to be the right way to use it isn’t. Now, because of this, our RSI calculation is a little odd, but basically what we’re doing is we calculate the RSI, then turn it into a percentage (between 0-1) that can easily be multiplied to the price point we need. The price point we use is the difference between our high purchase zone and our low purchase zone. This allows us to see how much price movement there is between zones. We multiply our zone size with our RSI multiplication and we get the amount we will add +/- to our basis line (white line). This officially creates the NEW high and low purchase zones that we are actually using and displaying in our trades.
If you found that confusing, here are some examples to why it is an important calculation for this strategy:
Before RSI factored in:
Which produced 27.8% Profit
After RSI factored in:
Which produced 553% Profit
As you can see, the RSI makes not only the purchase zones more accurate, but it also greatly increases the profit the strategy is able to make. It also helps ensure an relatively linear profit slope so you know it is reliable with its trades.
This strategy can work on pretty much anything, but you should tweak the values a bit for each pair you are trading it with for best results.
We hope you can find some use out of this simple but effective strategy, if you have any questions, comments or concerns please let us know.
HAPPY TRADING!
Linear Cross Trading StrategyLinear Cross Trading Strategy
The Linear Cross trading strategy is a technical analysis strategy that uses linear regression to predict the future price of a stock. The strategy is based on the following principles:
The price of a stock tends to follow a linear trend over time.
The slope of the linear trend can be used to predict the future price of the stock.
The strategy enters a long position when the predicted price crosses above the current price, and exits the position when the predicted price crosses below the current price.
The Linear Cross trading strategy is implemented in the TradingView Pine script below. The script first calculates the linear regression of the stock price over a specified period of time. The script then plots the predicted price and the current price on the chart. The script also defines two signals:
Long signal: The long signal is triggered when the predicted price crosses above the current price.
Short signal: The short signal is triggered when the predicted price crosses below the current price.
The script enters a long position when the long signal is triggered and exits the position when the short signal is triggered.
Here is a more detailed explanation of the steps involved in the Linear Cross trading strategy:
Calculate the linear regression of the stock price over a specified period of time.
Plot the predicted price and the current price on the chart.
Define two signals: the long signal and the short signal.
Enter a long position when the long signal is triggered.
Exit the long position when the short signal is triggered.
The Linear Cross trading strategy is a simple and effective way to trade stocks. However, it is important to note that no trading strategy is guaranteed to be profitable. It is always important to do your own research and backtest the strategy before using it to trade real money.
Here are some additional things to keep in mind when using the Linear Cross trading strategy:
The length of the linear regression period is a key parameter that affects the performance of the strategy. A longer period will smooth out the noise in the price data, but it will also make the strategy less responsive to changes in the price.
The strategy is more likely to generate profitable trades when the stock price is trending. However, the strategy can also generate profitable trades in ranging markets.
The strategy is not immune to losses. It is important to use risk management techniques to protect your capital when using the strategy.
I hope this blog post helps you understand the Linear Cross trading strategy better. Booost and share with your friend, if you like.
HK Percentile Interpolation One
This script is designed to execute a trading strategy based on Heikin Ashi candlesticks, moving averages, and percentile levels.
Please note that you should keep your original chart in normal candlestick mode and not switch it to Heikin Ashi mode. The script itself calculates Heikin Ashi values from regular candlesticks. If your chart is already in Heikin Ashi mode, the script would be calculating Heikin Ashi values based on Heikin Ashi values, which would produce incorrect results.
The strategy begins trading from a start date that you can specify by modifying the `startDate` parameter. The format of the date is "YYYY MM DD". So, for example, to start the strategy from January 1, 2022, you would set `startDate = timestamp("2022 01 01")`.
The script uses Heikin Ashi candlesticks, which are plotted in the chart. This approach can be useful for spotting trends and reversals more easily than with regular candlestick charts. This is particularly useful when backtesting in TradingView's "Rewind" mode, as you can see how the Heikin Ashi candles behaved at each step of the strategy.
Buy and sell signals are generated based on two factors:
1. The crossing over or under of the Heikin Ashi close price and the 75th percentile price level.
2. The Heikin Ashi close price being above certain moving averages.
You have the flexibility to adjust several parameters in the script, including:
1. The stop loss and trailing stop percentages (`stopLossPercentage` and `trailStopPercentage`). These parameters allow the strategy to exit trades if the price moves against you by a certain percentage.
2. The lookback period (`lookback`) used to calculate percentile levels. This determines the range of past bars used in the percentile calculation.
3. The lengths of the two moving averages (`yellowLine_length` and `purplLine_length`). These determine how sensitive the moving averages are to recent price changes.
4. The minimum holding period (`holdPeriod`). This sets the minimum number of bars that a trade must be kept open before it can be closed.
Please adjust these parameters according to your trading preferences and risk tolerance. Happy trading!
Chandelier Exit ZLSMA StrategyIntroducing a Powerful Trading Indicator: Chandelier Exit with ZLSMA
If you're a trader, you know the importance of having the right tools and indicators to make informed decisions. That's why we're excited to introduce a powerful new trading indicator that combines the Chandelier Exit and ZLSMA: two widely-used and effective indicators for technical analysis.
The Chandelier Exit (CE) is a popular trailing stop-loss indicator developed by Chuck LeBeau. It's designed to follow the price trend of a security and provide an exit signal when the price crosses below the CE line. The CE line is based on the Average True Range (ATR), which is a measure of volatility. This means that the CE line adjusts to the volatility of the security, making it a reliable indicator for trailing stop-losses.
The ZLEMA (Zero Lag Exponential Moving Average) is a type of exponential moving average that's designed to reduce lag and improve signal accuracy. The ZLSMA takes into account not only the current price but also past prices, using a weighted formula to calculate the moving average. This makes it a smoother indicator than traditional moving averages, and less prone to giving false signals.
When combined, the CE and ZLSMA create a powerful indicator that can help traders identify trend changes and make more informed trading decisions. The CE provides the trailing stop-loss signal, while the ZLSMA provides a smoother trend line to help identify potential entry and exit points.
In our indicator, the CE and ZLSMA are plotted together on the chart, making it easy to see both the trailing stop-loss and the trend line at the same time. The CE line is displayed as a dotted line, while the ZLSMA line is displayed as a solid line.
Using this indicator, traders can set their stop-loss levels based on the CE line, while also using the ZLSMA line to identify potential entry and exit points. The combination of these two indicators can help traders reduce their risk and improve their trading performance.
In conclusion, the Chandelier Exit with ZLSMA is a powerful trading indicator that combines two effective technical analysis tools. By using this indicator, traders can identify trend changes, set stop-loss levels, and make more informed trading decisions. Try it out for yourself and see how it can improve your trading performance.
Warning: The results in the backtest are from a repainting strategy. Don't take them seriously. You need to do a dry live test in order to test it for its useability.
-
Here is a description of each input field in the provided source code:
length: An integer input used as the period for the ATR (Average True Range) calculation. Default value is 1.
mult: A float input used as a multiplier for the ATR value. Default value is 2.
showLabels: A boolean input that determines whether to display buy/sell labels on the chart. Default value is false.
isSignalLabelEnabled: A boolean input that determines whether to display signal labels on the chart. Default value is true.
useClose: A boolean input that determines whether to use the close price for extrema calculations. Default value is true.
zcolorchange: A boolean input that determines whether to enable rising/decreasing highlighting for the ZLSMA (Zero-Lag Exponential Moving Average) line. Default value is false.
zlsmaLength: An integer input used as the length for the ZLSMA calculation. Default value is 50.
offset: An integer input used as an offset for the ZLSMA calculation. Default value is 0.
-
Ty for checking this out and good luck on your trading journey! Likes and comments are appreciated. 👍
--
Credits to:
▪ @everget – Chandelier Exit (CE)
▪ @netweaver2022 – ZLSMA
Linear Channel - Scalp Strategy 15MSimple way how to use Linear Regression for trading.
What we use:
• Linear Regression
• HMA as a trend filter
Logic:
Firstly we make simple linear regression moving. It is the white line which appears on the chart.
Then we make second line (named: band2) on the chart by multiplying linreg and value difference.
The third step is to ad HMA as a trend filter.
The trade open when price is below band2, but still upper than Hullma. The trade close when price again upper than linreg.
Linear trendSimple way how to use Linear Regression for trading.
What we use:
• Linear Regression
• EMA 200 as a trend filter
Logic:
Firstly we make two different linear regression movings as oscillator. For this we need to subtract slow moving from fast moving, so we get the single moving around zero. This is the green/red line which appears on the chart.
The trade open when LR cross over the threshold. The trade close when LR cross under the threshold below. Crossing over the threshold is the same as faster moving cross over slower moving.
Also we use EMA as a filter. The trades would be only when the price is over than EMA 200.
Linear SSL ShortThis script consist of two parts: linear SSL and DEMA. The difference between original SSL and current is that it calculated by linear regression. The logic is simple: when SSL "crossunder" and DEMA is above the price - we get short signal. When price became above DEMA and SSL "crossover" - close short.
MACD of Linear Regression Slope Indicator I used MACD to find peak and trough points in the Linear Regression Slope
[STRATEGY] Follow the Janet YellenIn the era of central bank's helicopter money, the market will always be skyrocketing up and up given enough time.
What's the strategy to profit from indices?
Only short the market when its in a state of euphoria /irrational exuberance bubble, or sell when it is confirmed (20% drawdown). Otherwise, you really have no reason not to long at every chance.
Conclusion:
Follow the printing press like a sheep.
