Scale In : Scale OutScale In : Scale Out strategy is an adaptation and extension of dollar-cost-averaging.
As the name implies it not only scales in - allocates a given percentage of available capital to buy at each bar - it also scales out - sells a given percentage of holdings at each bar when a target profit level is reached.
The strategy can potentially mitigate risks associated with market timing.
Although dollar-cost-averaging is often recommended as a strategy for building a position, the management of taking and retaining profits is not often addressed. This strategy demonstrates the potential benefits of managing both the building and (full or partial) liquidation of an investment.
We do not provide any mechanism for managing stop losses. We assume a scale in/out strategy will typically be applied to investing in assets with a high conviction thesis based on criteria external to the strategy. If the strategy does not perform, then the thesis may need to be re-evaluated, and the position liquidated. Even in this case, scaling out should still be considered.
Cerca negli script per "the strat"
Mean Reversion with Incremental Entry by HedgerLabsThe "Mean Reversion with Incremental Entry" strategy, designed by HedgerLabs, is an advanced TradingView strategy script focusing on the mean reversion technique in financial markets. This strategy is engineered for traders who prefer a systematic approach with an emphasis on incremental entries based on price movements relative to a moving average.
Key Features:
Moving Average Based Strategy: Central to this strategy is the simple moving average (SMA), around which all trade entries and exits revolve. Traders can customize the MA length, making it flexible for various trading styles and timeframes.
Incremental Entry Mechanism: Unique to this strategy is the incremental entry system. The strategy initiates an initial trade when the price deviates from the MA by a specified percentage. Subsequent entries are made at incremental steps, defined by the trader, as the price moves further away from the MA. This method can potentially capitalize on increasing market volatility.
Dynamic Position Management: The strategy intelligently manages positions by entering long when the price is below the MA and short when above, allowing for adaptive positioning in different market conditions.
Automated Exit Logic: Exit points are determined when the price touches the MA, aiming to close positions at potential reversal points for optimized trade outcomes.
Continuous Market Analysis: With 'calc_on_every_tick' enabled, the strategy constantly evaluates market conditions, ensuring prompt reaction to price movements.
Usage Scenario:
This strategy is particularly beneficial in markets exhibiting mean-reverting behavior. It is suitable for traders focusing on swing trading or those who prefer to scale into positions during periods of high volatility.
Disclaimer:
Please remember that this strategy is for informational and educational purposes only and is not intended as financial or investment advice. Trading in financial markets carries risks, including the potential loss of capital. We advise doing your own research and consulting with a financial expert before making any investment decisions.
COSTAR Strategy [SS]A little late posting this but here it is, as promised!
This is the companion to the COSTAR indicator.
What it does:
It creates a co-integration paired relationship with a separate, cointegrated ticker. It then plots out the expected range based on the value of the cointegrated pair. When the current ticker is below the value of its co-integrated partner, it becomes a "Buy" and should be longed. When it becomes overvalued in comparison, it becomes a "Sell" and should be shorted.
The example above is with BA and USO, which have a strong inverse relationship.
How it works:
I made the strategy version a bit more intuitive. Instead of you selecting the parameters for your model, it will autoselect the ideal parameters based on your desired co-integrated pair. You simply enter the ticker you want to compare against, and it will sort through the values at various lags to find significance and stationarity. It will then create a model and plot the model out for you on your chart, as you can see above.
The premise of the strategy:
The premise of the strategy is as stated before. You long when the ticker is undervalued in comparison to its co-integrated pair, and short when it is overvalued. The conditions for entry are simply a co-integrated pair being over the expected range (short) or below the expected range (long).
The condition to exit is a "re-integration", or a crossover of the expected value of the ticker (the centreline).
What if it can't find a relationship?
In some instances, the indicator will not be able to determine a co-integrated relationship, owning to a lack of stationarity between the data. When this happens, you will get the following error:
The indicator provides you with prompts, such as switching the timeframe or trying an alternative ticker. In the case displayed above, if we simply switch to the 1 hour timeframe, we have a viable model with great backtest results:
You can toggle in the settings menu the various parameters, such as timeframe, fills and displays.
And that is the strategy in a nutshell, be sure to check out its partner indicator, COSTAR, for more information on the premise of using co-integrated models for trading. And let me know your questions below!
Safe trades everyone!
RMI Trend Sync - Strategy [presentTrading]█ Introduction and How It Is Different
The "RMI Trend Sync - Strategy " combines the strength of the Relative Momentum Index (RMI) with the dynamic nature of the Supertrend indicator. This strategy diverges from traditional methodologies by incorporating a dual analytical framework, leveraging both momentum and trend indicators to offer a more holistic market perspective. The integration of the RMI provides an enhanced understanding of market momentum, while the Super Trend indicator offers clear insights into the end of market trends, making this strategy particularly effective in diverse market conditions.
BTC 4h long/short performance
█ Strategy: How It Works - Detailed Explanation
- Understanding the Relative Momentum Index (RMI)
The Relative Momentum Index (RMI) is an adaptation of the traditional Relative Strength Index (RSI), designed to measure the momentum of price movements over a specified period. While RSI focuses on the speed and change of price movements, RMI incorporates the direction and magnitude of those movements, offering a more nuanced view of market momentum.
- Principle of RMI
Calculation Method: RMI is calculated by first determining the average gain and average loss over a given period (Length). It differs from RSI in that it uses the price change (close-to-close) rather than absolute gains or losses. The average gain is divided by the average loss, and this ratio is then normalized to fit within a 0-100 scale.
- Momentum Analysis in the Strategy
Thresholds for Decision Making: The strategy uses predetermined thresholds (pmom for positive momentum and nmom for negative momentum) to trigger trading decisions. When RMI crosses above the positive threshold and other conditions align (e.g., a bullish trend), it signals a potential long entry. Similarly, crossing below the negative threshold in a bearish trend may trigger a short entry.
- Super Trend and Trend Analysis
The Super Trend indicator is calculated based on a higher time frame, providing a broader view of the market trend. This indicator uses the Average True Range (ATR) to adapt to market volatility, making it an effective tool for identifying trend reversals.
The strategy employs a Volume Weighted Moving Average (VWMA) alongside the Super Trend, enhancing its capability to identify significant trend shifts.
ETH 4hr long/short performance
█ Trade Direction
The strategy offers flexibility in selecting the trading direction: long, short, or both. This versatility allows traders to adapt to their market outlook and risk tolerance, whether looking to capitalize on bullish trends, bearish trends, or a combination of both.
█ Usage
To effectively use the "RMI Trend Sync" strategy, traders should first set their preferred trading direction and adjust the RMI and Super Trend parameters according to their risk appetite and trading goals.
The strategy is designed to adapt to various market conditions, making it suitable for different asset classes and time frames.
█ Default Settings
RMI Settings: Length: 21, Positive Momentum Threshold: 70, Negative Momentum Threshold: 30
Super Trend Settings: Length: 10, Higher Time Frame: 480 minutes, Super Trend Factor: 3.5, MA Source: WMA
Visual Settings: Display Range MA: True, Bullish Color: #00bcd4, Bearish Color: #ff5252
Additional Settings: Band Length: 30, RWMA Length: 20
Ironman [Decentrader]Ironman
What is it? how it does it? And how to use it:
i) Ironman is a multifaceted strategy builder, which uses coloured candles which represent certain customisable inputs being in confluence with one another and the set scenario.
ii) There are 7 customised technical indicators which can be input as a basis for the analytical review.
iii) Determine a primary indicator which dictates a bullish or bearish trend (and colour) and then optionally add up to 6 other indicators to be required to be in confluence which adds another colour to be represented.
An example might be two moving averages crossing as the main trend determination. The primary determinant is dictated as the trend being “bullish or bearish” and the added confluence adds an additional layer being “very bullish or very bearish”
iv) Users select which conditions they wish to enter and exit trades on using the Bullish / Very Bullish and Bearish / Very Bearish settings. This can be combined with other timeframes.
v) The selected inputs for each indicator will show in a table contained in the bottom right-hand corner. Active indicators within the system will be highlighted.
vi) Ironman is built to include various take profit and stop loss options such as trailing stops, and fixed percentage targets which can be included in the strategy. Different timeframes can be used to determine the stop if users wish to do so.
vii) Users can require that there is also confluence with a differing time period or choose long and short-only options which can be dictated independently or based upon filtering criteria using moving averages.
viii) Using the strategy settings, users are also able to choose backtesting periods.
ix) Position label settings allow users to show various backtesting options such as profit by position, total backtesting results and results for the active position.
x) Ironman enables users to automate trading easily using the input boxes under Alert messages which also allows connection to a third party which can conduct execution. Always make sure to thoroughly test the strategy if it is being automated.
xi) To get the best out of Ironman, build up a strategy for the timeframe and asset you are looking at and back-test outcomes as variables are layered in. Ensure to backtest over a suitable length of time.
xii) When optimising input variables, it can sometimes visually assist in having the underlying inputs on the screen via the standard indicators.
xiii) There are many boxes of information in the input variables, which explain how to use each part. Users can also add features such as a marker showing on the chart where all indicators are bullish/bearish, or where RSI is overbought / over sold.
xiv) Users can further customise the style of the tool under the style tab in the indicator settings.
EUR/USD 45 MIN Strategy - FinexBOTThis strategy uses three indicators:
RSI (Relative Strength Index) - It indicates if a stock is potentially overbought or oversold.
CCI (Commodity Channel Index) - It measures the current price level relative to an average price level over a certain period of time.
Williams %R - It is a momentum indicator that shows whether a stock is at the high or low end of its trading range.
Long (Buy) Trades Open:
When all three indicators suggest that the stock is oversold (RSI is below 25, CCI is below -130, and Williams %R is below -85), the strategy will open a buy position, assuming there is no current open trade.
Short (Sell) Trades Open:
When all three indicators suggest the stock is overbought (RSI is above 75, CCI is above 130, and Williams %R is above -15), the strategy will open a sell position, assuming there is no current open trade.
SL (Stop Loss) and TP (Take Profit):
SL (Stop Loss) is 0.45%.
TP (Take Profit) is 1.2%.
The strategy automatically sets these exit points as a percentage of the entry price for both long and short positions to manage risks and secure profits. You can easily adopt these inputs according to your strategy. However, default settings are recommended.
Indian NIFTY Correlation Daytrade/Swing StrategyINTRODUCTION :
This is a daytrading/swing strategy designed mainly for indian market where internally has been adapted to NIFTY market and as well using for internal calculations the values of the candles from NIFTY asset.
With it we search to use with the most correlated asset from the indian market.
For this example I choosed BANKNIFTY
STRATEGY:
The strategy initially uses as candle values the data from the NIFTY asset.
With them I am dividing the work into two calculation parts such as :
-For first part logic, I am doing calculations regarding the volatility of NIFTY, where I initially take into consideration INDIAVIX to have an idea of the expected implied volatility of NIFTY asset and then I compare it with different tools such as ATR, BB and Percentile location of the volatility.
Based on all these factors I take into account the location of the volatility which is atm and if there is a possibility of a strong movement(trend) or sidemarket situation.
-Once I am done with the values of the volatility, the next process in the script logic is to start looking into the trend.
For it I am using different tools such as volume checker, support and resistence key points, pivot points, price actions patterns and different moving averages.
-Risk management part : once we are done with calculation for the entry, the next part is to have an idea where to exit. In this case I am making use of a dynamic risk management which is compressed from multiple ideas such as : we can exit if there were a big gap on the next day in our initial direction, we can also exit based of an internal daily ATR calculation value(we use initially 15min timeframe chart) and lastly if we are around some key points like support/resistence or other different chart patterns like double top, double bot and so on.
CASE EXAMPLE:
As I said before we are initially using for calculation the NIFTY chart with 15min timeframe. With it we can apply to any indian etf,stocks,future. All the assets are going to have the same time of entry and the same time of exit(we get this from NIFTY) and we plot it on the chart we are using, so its key point to look for assets which have a min 75-80% correlation with NIFTY. For this example I used BANKNIFTY chart.
So a type of entry would be this way
Lets assume NIFTY50 is on 19.000 level
INDIAVIX level is currently at 11 which can be translated : 11 / sqrt(250)
So 11 means that on a yearly base we expect the asset to move 11% upwards or downwards
and in a year we have aprox 250 days. So we divide the 11 by sqrt of 250 to get an idea of a daily expected move from the implied volatility of india VIX
11/15.87 = +-0.69%
So INDIAVIX tells us that the values for today nifty is 19000+0,69% and 19000-0.69%
After that I am looking into the daily ATR, and I see that the expected is around 0.8% and is ascending over the last 2 weeks.
Lastly I am looking at the percentile which is currently the volatility on both ATR and INDIAVIX, and I get a value of 90th percentile.
With this my biased is that we are going to expect a short trend, but i cant confirm on the volatility alone so next step is start looking into technical analysis.
I look at volume and is increasing, I look at different price actions paterns and pivots and I see a lower low and a lower high (a descending pattern).
I also see the price is below the key MA like SMA50/100/200, VWAP and so on.
With all of this I get more confirmation that the asset is in a short trend.
Internally once a certain specific % of confirmation from all the logics is achieved, it will trigger a long/short entry, so in this case lets assume we have 80% of our indicators pointing to the short, is going to enter a short.
Now for a long scenario the scene would be , indiavix is around 9,5, ATR is descending. We are around 40th percentile of the volatility.
Our asset is above multiple moving averages, vwap , etc
We have an increasing volume towards bullish side.
And so on( overall 75% of our indicators are pointing towards the long side)
Now for the exit, since we are dealing with a daytrade/swing mentality, short on average we keep the trade open for a less period of time than long ( 19 bars of 15min candles, compared to 57 bars of 15min for long) , so most of the times for short we are going to exit next day and if the trend is still in our favour we re enter the trade.
For long we can stay much more time, sometimes even weeks and we exit mainly when the % of confirmation of indicators point out a reversal/short confirmation fo a big pice action pattern.
STRATEGY RESULTS
For strategy analysis I have used BANKNIFTY NSE with deep history to get access to data from 2011 until present( giving more than 2500 trades) .
For inputs I am using 0.02% comission total ( the comission applied from ZERODHA indian exchange is close to 0.0175% total) so I used it a bit higher in order to take into account some slippages.
For capital THE REASON I USED 100% of the capital allocation is to make a proper comparison with the buy an hold from the same period
Lets assume we had an account of 1M ruppes initially in 2011, we start using 100% of it and then the new values automatically compounded with the new profits and losses so directly compare with 1M of rupees in shares on BANKNIFTY ETFs bought in 2011(buy n hold) until present day.
STRATEGY ACCESS
Strategy is free to be tested for everyone, just let me know in private that you wish to get access to it.
Multi-TF AI SuperTrend with ADX - Strategy [PresentTrading]
## █ Introduction and How it is Different
The trading strategy in question is an enhanced version of the SuperTrend indicator, combined with AI elements and an ADX filter. It's a multi-timeframe strategy that incorporates two SuperTrends from different timeframes and utilizes a k-nearest neighbors (KNN) algorithm for trend prediction. It's different from traditional SuperTrend indicators because of its AI-based predictive capabilities and the addition of the ADX filter for trend strength.
BTC 8hr Performance
ETH 8hr Performance
## █ Strategy, How it Works: Detailed Explanation (Revised)
### Multi-Timeframe Approach
The strategy leverages the power of multiple timeframes by incorporating two SuperTrend indicators, each calculated on a different timeframe. This multi-timeframe approach provides a holistic view of the market's trend. For example, a 8-hour timeframe might capture the medium-term trend, while a daily timeframe could capture the longer-term trend. When both SuperTrends align, the strategy confirms a more robust trend.
### K-Nearest Neighbors (KNN)
The KNN algorithm is used to classify the direction of the trend based on historical SuperTrend values. It uses weighted voting of the 'k' nearest data points. For each point, it looks at its 'k' closest neighbors and takes a weighted average of their labels to predict the current label. The KNN algorithm is applied separately to each timeframe's SuperTrend data.
### SuperTrend Indicators
Two SuperTrend indicators are used, each from a different timeframe. They are calculated using different moving averages and ATR lengths as per user settings. The SuperTrend values are then smoothed to make them suitable for KNN-based prediction.
### ADX and DMI Filters
The ADX filter is used to eliminate weak trends. Only when the ADX is above 20 and the directional movement index (DMI) confirms the trend direction, does the strategy signal a buy or sell.
### Combining Elements
A trade signal is generated only when both SuperTrends and the ADX filter confirm the trend direction. This multi-timeframe, multi-indicator approach reduces false positives and increases the robustness of the strategy.
By considering multiple timeframes and using machine learning for trend classification, the strategy aims to provide more accurate and reliable trade signals.
BTC 8hr Performance (Zoom-in)
## █ Trade Direction
The strategy allows users to specify the trade direction as 'Long', 'Short', or 'Both'. This is useful for traders who have a specific market bias. For instance, in a bullish market, one might choose to only take 'Long' trades.
## █ Usage
Parameters: Adjust the number of neighbors, data points, and moving averages according to the asset and market conditions.
Trade Direction: Choose your preferred trading direction based on your market outlook.
ADX Filter: Optionally, enable the ADX filter to avoid trading in a sideways market.
Risk Management: Use the trailing stop-loss feature to manage risks.
## █ Default Settings
Neighbors (K): 3
Data points for KNN: 12
SuperTrend Length: 10 and 5 for the two different SuperTrends
ATR Multiplier: 3.0 for both
ADX Length: 21
ADX Time Frame: 240
Default trading direction: Both
By customizing these settings, traders can tailor the strategy to fit various trading styles and assets.
2 Moving Averages | Trend FollowingThe trading system is a trend-following strategy based on two moving averages (MA) and Parabolic SAR (PSAR) indicators.
How it works:
The strategy uses two moving averages: a fast MA and a slow MA.
It checks for a bullish trend when the fast MA is above the slow MA and the current price is above the fast MA.
It checks for a bearish trend when the fast MA is below the slow MA and the current price is below the fast MA.
The Parabolic SAR (PSAR) indicator is used for additional trend confirmation.
Long and short positions can be turned on or off based on user input.
The strategy incorporates risk management with stop-loss orders based on the Average True Range (ATR).
Users can filter the backtest date range and display various indicators.
The strategy is designed to work with the date range filter, risk management, and user-defined positions.
Features:
Trend-following strategy.
Two customizable moving averages.
Parabolic SAR for trend confirmation.
User-defined risk management with stop-loss based on ATR.
Backtest date range filter.
Flexibility to enable or disable long and short positions.
This trading system provides a comprehensive approach to trend-following and risk management, making it suitable for traders looking to capture trends with controlled risk.
Heatmap MACD StrategyHello traders
A customer gave me the idea indirectly after I made an update to that script:
Supertrend MTF Heatmap
Important Notes
The backtest results aren't relevant for this educational script publication.
I used realistic backtesting data but didn't look too much into optimizing the results, as this isn't the point of why I'm publishing this script.
I wanted to showcase that any Heatmap script can be converted into a strategy.
The strategy default settings are:
Initial Capital: 100000 USD
Position Size: 1 contract
Commission Percent: 0.075%
Slippage: 1 tick
No margin/leverage used
For example, those are realistic settings for trading CFD indices with low timeframes, but not the best possible settings for all assets/timeframes.
Concept
The Heatmap MACD Strategy allows selecting one MACD in five different timeframes.
You'll get an exit signal whenever one of the 5 MACDs changes direction.
Then, the strategy re-enters whenever all the MACDs are in the same direction again.
It takes:
long trades when all the 5 MACD histograms are bullish
short trades when all the 5 MACD histograms are bearish
You can select the same timeframe multiple times if you don't need five timeframes.
For example, if you only need the 30min, the 1H, and 2H, you can set your timeframes as follow:
30m
30m
30m
1H
2H
Risk Management Features
Nothing too fancy
All the features below are pips-based
Stop-Loss
Trailing Stop-Loss
Stop-Loss to Breakeven after a certain amount of pips has been reached
Take Profit 1st level and closing X% of the trade
Take Profit 2nd level and close the remaining of the trade
What's next?
I'll publish this script's open-source Pineconnector, ProfitView, and AutoView versions for educational purposes.
Thank you
Dave
Stochastic StrategyThis strategy is designed to make trading decisions based on the Stochastic Oscillator (Stoch) indicator with settings of (7,2,2). The strategy opens a long (buy) position when the Stoch indicator crosses above the 50 level from below. Conversely, it opens a short (sell) position when the Stoch indicator crosses below the 50 level from above. Additionally, when a long position is opened, any existing short position is closed, and vice versa.
Key Parameters:
Stochastic Oscillator Settings: Length = 7, SmoothK = 2, SmoothD = 2.
Overbought Level: 80.
Oversold Level: 20.
Strategy Description:
The Stochastic Oscillator (Stoch) is calculated based on the closing price, high price, and low price with a period of 7, and both the %K and %D lines are smoothed with periods of 2.
When the %K line crosses above the oversold level (20), it generates a long (buy) signal.
When the %K line crosses below the overbought level (80), it generates a short (sell) signal.
The strategy visually marks long and short signals on the chart using upward and downward triangles, respectively.
The strategy automatically enters long or short positions when the respective conditions are met.
If a long position is opened, any existing short position is closed, and vice versa.
Please note that this is a basic example of a trading strategy and does not take into account all possible risk factors or optimizations. Before using this strategy in live trading, it's essential to thoroughly test and customize it to suit your specific needs, and carefully analyze the results. Trading carries risks, and it's important to use proper risk management techniques when implementing any trading strategy.
Market Breadth Strategy/Introduction
The Market Breadth Strategy (MBS) is a versatile strategy for trading the US stock market. MBS is suitable for traders with low, medium and high risk tolerance who prefer trading equities as an asset class on the 1 day timeframe. It combines mean reversion with trend following to keep you participating in the stock market for as long as is profitable.
/Signals
The strategy is long only. Four different signals are generated to ensure all opportunities the market presents are seized for profit. The first category of signals are triggered after a prolonged period of falling prices; usually during a bear market or severe correction, open your largest positions on this signal. The second category of signals are triggered at the end of the bear market, early in the recovery. They ensure you do not miss out on an early entry if you get stopped out of your initial positions, size them equal to the first category signal positions. The third category of signals are triggered late in the recovery from a bear market, severe correction or deep pullback. Open your smallest positions on this signal. The fourth category of signals are triggered at all times when the market experiences a significant pullback or time correction, these positions should be medium sized.
For optimum performance, whenever signals are triggered, traders are advised to open at least, a new long position. Buying the index is recommended for traders with low risk tolerance, buying sector, industry or thematic ETFs (after sufficient analysis) is recommended for traders with medium risk tolerance, while buying stocks (after sufficient analysis) is recommended for traders who want to take on higher risk for higher returns. Such traders may also combine positions in indices, groups and individual stocks for better performance.
/Interpretation
MBS will display an upward blue arrow signifying a buy signal after the candle closes. A label below the arrow will describe which signal was triggered and a number depicting the number of positions (they can be deactivated in the style settings). MBS will also display a downwards pink arrow above the candle, after a specified decline from the high, again when the candle closes. All open positions will be closed on this signal, it is the risk management feature of the strategy.
/Construction
The strategy is built using market breadth data from the US Exchanges where stocks are listed, it is not a mash-up of different indicators. A combination of the following data is used:
(i) the number of advancing and declining issues
(ii) the number of issues reaching new highs
(iii) the closing prices of issues relative to key moving averages
This data is analysed and used to generate the four categories of signals described previously, they are named;
(i) Bottom Signal - for buying at the market's potential bottom
(ii) Follow-Up Signal - for ensuring you do not miss the bottom
(iii)Follow-Through Signal - for buying strength after a downtrend
(iv) Buy-The-Dip Signal - for buying throwbacks in uptrends and pullbacks in downtrends
/Settings
This strategy works best with the default settings. Although the input parameters can be changed to suit your needs, it is not advisable to do so as it may affect the strategy's performance.
(i) The market regime filter checks to see if the market is in a regime of rising prices (bull market) or falling prices (bear market), long signals are avoided in bear market conditions.
(ii) The risk size is equivalent to a stop loss. It triggers an exit when price declines by a certain amount.
(iii) 'Downside' measures the participation of issues to the downside during a decline while 'Upside' measures the participation of issues to the upside after the decline; this is called 'follow through'.
(iv) The bottom interval determines the frequency of bottom signals issued in days.
(v) Dip size quantifies the dip to determine if it is large enough for a buy signal, the lower the number, the larger the dip.
(vi) Following interval sets the duration for following up on the bottom.
(vii) Bottoming interval resets the bottom for the next follow-up
/Strategy Results
The backtest results are based on a starting capital of $13,700 (convenient amount for retail traders) with $1000 position size (7% of equity and enough for two shares of SPY) and pyramiding of 10 consecutive positions. Commissions of 0.03% and slippage of 2 ticks are used to ensure the results are representative of real world trading conditions. The backtest results are available to view at the bottom of this page.
Note that past results are not indicative of future results. The strategy is backtested in ideal conditions, it has no predictive abilities and results from live trading may not achieve the 2.235 profit factor shown here as each trader may introduce subjectivity or interfere with its performance or market conditions might change significantly. Since the strategy was designed for the US stock market, it has been backtested on the SPY (representative of the US stock market) ETF (for consistency in price across brokers).
/Tickers
This strategy should be used preferably with the SPY ticker which is the ETF for the S&P500. Alternatively, it could be used with VOO and several other S&P500 ETFs or a CFD ticker such as SPX500USD and several others which are based on the futures product. The strategy may not be suitable for futures tickers like ES according to TradingView.
/Access
The MBS is an Invite-Only script hence, traders interested in this strategy should contact me privately to request access.
3kilos BTC 15mThe "3kilos BTC 15m" is a comprehensive trading strategy designed to work on a 15-minute timeframe for Bitcoin (BTC) or other cryptocurrencies. This strategy combines multiple indicators, including Triple Exponential Moving Averages (TEMA), Average True Range (ATR), and Heikin-Ashi candlesticks, to generate buy and sell signals. It also incorporates risk management features like take profit and stop loss.
Indicators
Triple Exponential Moving Averages (TEMA): Three TEMA lines are used with different lengths and sources:
Short TEMA (Red) based on highs
Long TEMA 1 (Blue) based on lows
Long TEMA 2 (Green) based on closing prices
Average True Range (ATR): Custom ATR calculation with EMA smoothing is used for volatility measurement.
Supertrend: Calculated using ATR and a multiplier to determine the trend direction.
Simple Moving Average (SMA): Applied to the short TEMA to smooth out its values.
Heikin-Ashi Close: Used for additional trend confirmation.
Entry & Exit Conditions
Long Entry: Triggered when the short TEMA is above both long TEMA lines, the Supertrend is bullish, the short TEMA is above its SMA, and the Heikin-Ashi close is higher than the previous close.
Short Entry: Triggered when the short TEMA is below both long TEMA lines, the Supertrend is bearish, the short TEMA is below its SMA, and the Heikin-Ashi close is lower than the previous close.
Take Profit and Stop Loss: Both are calculated as a percentage of the entry price, and they are set for both long and short positions.
Risk Management
Take Profit: Set at 1% above the entry price for long positions and 1% below for short positions.
Stop Loss: Set at 3% below the entry price for long positions and 3% above for short positions.
Commission and Pyramiding
Commission: A 0.07% commission is accounted for in the strategy.
Pyramiding: The strategy does not allow pyramiding.
Note
This strategy is designed for educational purposes and should not be considered as financial advice. Always do your own research and consider consulting a financial advisor before engaging in trading.
Linear On MACDUnlocking the Magic of Linear Regression in TradingView
In the ever-evolving world of financial markets, traders and investors seek effective tools to gauge price movements, make informed decisions, and achieve their financial goals. One such tool that has proven its worth over time is linear regression, a mathematical concept that has found its way into technical analysis and trading strategies. In this blog post, we will explore the magic behind linear regression, delve into its history, and understand how it's widely used as a technical indicator.
The Birth of Linear Regression: From Mathematics to Trading
Linear regression is a statistical method that aims to model the relationship between two variables by fitting a linear equation to observed data. The formula for a linear regression line is typically expressed as y = a + bx, where y is the dependent variable, x is the independent variable, a is the intercept, and b is the slope.
While the roots of linear regression trace back to the field of statistics, it didn't take long for traders and investors to recognize its potential in the financial world. By applying linear regression to historical price data, traders can identify trends, assess the relationship between variables, and even predict potential future price levels.
The Linear On MACD Strategy
Let's take a closer look at a powerful example of how linear regression is employed in a trading strategy right within TradingView. The "Linear On MACD" strategy harnesses the potential of linear regression in conjunction with the Moving Average Convergence Divergence (MACD) indicator. The goal of this strategy is to generate buy and sell signals based on the interactions between the predicted stock price and the MACD indicator.
Here's a breakdown of the strategy's components:
Calculation of Linear Regression: The strategy begins by calculating linear regression coefficients for the historical stock price based on volume. This helps predict potential future price levels.
Predicted Stock Price: The linear regression results are then used to plot the predicted stock price on the chart. This provides a visual representation of where the price could trend based on historical data.
Buy and Sell Signals: The strategy generates buy signals when certain conditions are met. These conditions include the predicted stock price being between the open and close prices, a rising MACD, and other factors that suggest a potential bullish trend. On the other hand, sell signals are generated based on MACD trends and predicted price levels.
Risk Management: The strategy also incorporates risk tolerance levels to determine entry and exit points. This ensures that traders take into account their risk appetite when making trading decisions.
Embracing the Magic of Linear Regression
As we explore the "Linear On MACD" strategy, we uncover the power of linear regression in aiding traders and investors. Linear regression, a mathematical marvel, seamlessly merges with technical analysis to provide insights into potential price movements. Its historical significance in statistics blends perfectly with the demands of modern financial markets.
Whether you're a seasoned trader or a curious investor, the Linear On MACD strategy exemplifies how a robust mathematical concept can be harnessed to make informed trading decisions. By embracing the magic of linear regression, you're tapping into a tool that continues to evolve alongside the financial world it empowers.
Disclaimer: The information provided in this blog post is for educational purposes only and does not constitute financial advice. Trading and investing carry risks, and it's important to conduct thorough research and consider seeking professional advice before making any trading decisions.
Financial Ratios Fundamental StrategyWhat are financial ratios?
Financial ratios are basic calculations using quantitative data from a company’s financial statements. They are used to get insights and important information on the company’s performance, profitability, and financial health.
Common financial ratios come from a company’s balance sheet, income statement, and cash flow statement.
Businesses use financial ratios to determine liquidity, debt concentration, growth, profitability, and market value.
The common financial ratios every business should track are
1) liquidity ratios
2) leverage ratios
3)efficiency ratio
4) profitability ratios
5) market value ratios.
Initially I had a big list of 20 different ratios for testing, but in the end I decided to stick for the strategy with these ones :
Current ratio: Current Assets / Current Liabilities
The current ratio measures how a business’s current assets, such as cash, cash equivalents, accounts receivable, and inventories, are used to settle current liabilities such as accounts payable.
Interest coverage ratio: EBIT / Interest expenses
Companies generally pay interest on corporate debt. The interest coverage ratio shows if a company’s revenue after operating expenses can cover interest liabilities.
Payables turnover ratio: Cost of Goods sold (or net credit purchases) / Average Accounts Payable
The payables turnover ratio calculates how quickly a business pays its suppliers and creditors.
Gross margin: Gross profit / Net sales
The gross margin ratio measures how much profit a business makes after the cost of goods and services compared to net sales.
With this data, I have created the long and long exit strategy:
For long, if any of the 4 listed ratios,such as current ratio or interest coverage ratio or payable turn ratio or gross margin ratio is ascending after a quarter, its a potential long entry.
For example in january the gross margin ratio is at 10% and in april is at 15%, this is an increase from a quarter to another, so it will get a long entry trigger.
The same could happen if any of the 4 listed ratios follow the ascending condition since they are all treated equally as important
For exit, if any of the 4 listed ratios are descending after a quarter, such as current ratio or interest coverage ratio or payable turn ratio or gross margin ratio is descending after a quarter, its a potential long exit.
For example in april we entered a long trade, and in july data from gross margin comes as 12% .
In this case it fell down from 15% to 12%, triggering an exit for our trade.
However there is a special case with this strategy, in order to make it more re active and make use of the compound effect:
So lets say on july 1 when the data came in, the gross margin data came descending (indicating an exit for the long trade), however at the same the interest coverage ratio came as positive, or any of the other 3 left ratios left . In that case the next day after the trade closed, it will enter a new long position and wait again until a new quarter data for the financial is being published.
Regarding the guidelines of tradingview, they recommend to have more than 100 trades.
With this type of strategy, using Daily timeframe and data from financials coming each quarter(4 times a year), we only have the financial data available since 2016, so that makes 28 quarters of data, making a maximum potential of 28 trades.
This can however be "bypassed" to check the integrity of the strategy and its edge, by taking for example multiple stocks and test them in a row, for example, appl, msft, goog, brk and so on, and you can see the correlation between them all.
At the same time I have to say that this strategy is more as an educational one since it miss a risk management and other additional filters to make it more adapted for real live trading, and instead serves as a guiding tool for those that want to make use of fundamentals in their trades
If you have any questions, please let me know !
Gaussian Detrended ReversionThis strategy, titled "Gaussian Detrended Reversion Strategy," aims to identify potential price reversals using the customized Gaussian Detrended Price Oscillator (GDPO) in combination with smoothed price cycles.
Key Elements of the Strategy:
GDPO Calculation: The strategy first calculates the Detrended Price Oscillator (DPO) by comparing the close price to an Exponential Moving Average (EMA) of a specified period. This calculation helps identify short-term price cycles by detrending the price data.
Gaussian Smoothing: The DPO values are then smoothed using the Arnaud Legoux Moving Average (ALMA), applying a Gaussian smoothing technique. This smoothed version of the DPO is intended to filter out noise and provide a clearer picture of price trends.
Entry and Exit Conditions: The strategy defines conditions for both long and short entry points as well as exit points. It looks for specific crossover events between the smoothed GDPO and its lagged version. The strategy enters a long position when the smoothed GDPO crosses above the lag and is negative, and exits the long position when the smoothed GDPO crosses below the lag or the zero line. Similarly, the strategy enters a short position when the smoothed GDPO crosses below the lag and is positive, and exits the short position when the smoothed GDPO crosses above the lag or the zero line.
Visualization: The smoothed GDPO and its lag are plotted on the chart using distinct colors. The zero line is also displayed as a reference point. Additionally, the chart background changes color when the strategy enters a long or short position. Cross markers are also plotted at the crossover points as exit cues.
Overall, this strategy aims to capture potential price reversals using the GDPO and Gaussian smoothing, with specific entry and exit conditions to guide trading decisions.
