Orion Algo Strategy v2.0Hi everyone.
I decided to make the latest Orion Algo open to people. I don't have enough time to work on it lately, so I figured it would be best that everyone can have it to work on it. I took out some stuff from the original but it should give an idea on how things work. I made two strategies with this so far so you can use that to come up with your own. I recommend the DCA strategy because it gives you the most bang for Orion Algo's buck. It's pretty good at finding long entries.
Overall I hope you guys like this one. Also, Banano is the best crypto currency :)
-INFO-
Orion Algo is a trading algorithm designed to help traders find the highs and lows of the market before, during, and after they happen. We wanted to give an indicator to people that was simple to use. In fact we created the algorithm in such a way that it currently only needs a single input from the user. Since no indicator can predict the market perfectly, Orion should be used as just another tool (although quite a sharp one) for you to trade with. Fundamental knowledge of price action and TA should be used with Orion Algo.
Being an oscillator, Orion currently has a bias towards market volatility . So you will want to be trading markets over 30% volatility . We have plans to develop future versions that take this into account and adjust automatically for dead conditions. Also, while there are some similarities across all oscillators, what sets ours apart is the prediction curve. The prediction curve looks at the current signal values and gives it a relative score to approximate tops and bottoms 1-2 bars ahead of the signal curve. We also designed a velocity curve that attempts to predict the signal curve 2+ bars ahead. You can find the relative change in velocity in the Info panel. The bottom momentum wave is based on the signal curve and helps find overall market direction of higher time-frames while in a lower one.
Settings and How to Use them:
User Agreement – Orion Algo is a tool for you to use while trading. We aren’t responsible for losses OR the gains you make with it. By clicking the checkbox on the left you are agreeing to the terms.
Super Smooth – Smooths the main signal line based on the value inside the box. Lower values shift the pivot points to the left but also make things more noisy. Higher values move things to the right making it lag a bit more while creating a smoother signal. 8 is a good value to start with.
Theme – Changes the color scheme of Orion.
Dashboard – Turns on a dashboard with useful stats, such as Delta v, Volatility , Rsi , etc. Changing the value box will move the dashboard left and right.
Prediction – A secondary prediction model that attempts to predict a reversal before it happens (0-2bars). This can be noisy some times so make your best judgement. Curve will toggle a curve view of the prediction. Pivots will toggle bull/bear dots.
∆v – Delta v (change in velocity). This shows momentum of the signal. Crossing 0 signals a reversal. If you see the delta v changing direction, it may signify a reversal in the several bars depending on the overall momentum of the market.
Momentum Wave – Uses the signal as a macro trend indicator. Changes in direction of the wave can signify macro changes in the market. Average will toggle an averaging algorithm of the momentum waves and makes it easy to understand.
-STRATEGIES-
Simple - Just buy and sell on the dots
DCA - Uses the settings in the script for entries. If a buy dot appears then it will buy, if the price goes below the percentage it will wait for another dot before entering. This drastically improves DCA potential.
Komut dosyalarını "algo" için ara
Risk Reward Calculator [lovealgotrading]
OVERVIEW:
This Risk Reward Calculator strategy can help you maximize your RR value with help of algorithmic trading.
INDICATOR:
I wanted to setup my trades more easier with this indicator, I didn't want to calculate everytime before orders, with help this indicator we can calculate R:R value, avarage price, stoploss price, take-profit price, order prices, all position cost and more ...
Our strategy is a risk revard calculation indicator that is made easy to use by using visualized lines and panels, and also has algorithmic trading support.
With the help of this indicator, we can quickly and easily calculate our risk reward values and enter the positions.
If we want to ensure that our balance grows regularly while trading in the stock market, we need to manage the risks and rewards otherwise we may fall below our initial balance at the end of the day, even if we seem to be winning.
What is the Risk-Reward value ?
This value is a value that shows how many times the amount of risk we take when entering the position is successful, we will earn.
- For example, you risked $100 while entering the trade, so if your trade stops, you will lose 100 $.
Your Risk-Reward(RR) value is 2 means that if your position is successful, you will have 200 $ in your pocket.
A trader's success is determined by the amount of R he earns monthly or yearly, not how much money he makes.
What is different in this indicator ?
I want to say thank you to © EvoCrypto. His Calculator (weighted) – evo indicator helped me when I was developed my indicator.
I want to explain what I have improved:
1-In this strategy, we can determine the time period in which we want to open our positions.
2-We can open a maximum of 4 positions in the same direction and close our positions at a single level. StopLoss or TakeProfit
3-This indicator, which works in the form of a strategy, shows where our positions have been opened or closed. With the help of this, it helps us to determine our strategy in our future positions more accurately.
4-The most important improvement is that we do not miss our positions with the help of alarms (WEB HOOK). if we want, we receive by quickly connecting all these positions to our robot, the software can enter and exit the position while we are busy.
IMPLEMENTATION DETAILS – SETTINGS:
1 - We can set the start and end dates of the positions we will take.
2- We can set our take profit, stoploss levels.
3- If your trade is stopped, we can determine the amount of the trade that we will lose.
4- We can adjust our entry levels to positions and our position sizes at entry levels.
(Sum of positions weight must be 100%)
5- We can receive our positions even if we are busy with the help of algorithmic trading. For this, we must paste our Jshon codes into the fields specified in the settings panel.
6- Finally, we can change the settings we want and don't want to have in our visual elements.
Let's make a LONG side example together
We have determined our positions to enter stoploss, take profit and long positions. We did not forget to set the start time of our strategy
Our strategy appear on the graph as follows.
Our strategy has calculated the total position size, our R-R value, the distance of the current price to the stop and take profit levels, in short, a lot of things we could look visually.
Notes:
If you're going to connect this bot to an automatic Long or Short direction,
Don’t forget! you need to Webhook URL,
Don’t miss paste this code to your message window {{strategy.order.alert_message}}
ALSO:
If you have any ideas what to add to my work to add more sources or make calculations cooler, feel free to write me.
iMoku (Ichimoku Complete Tool) - The Quant Science iMoku™ is a professional all-in-one solution for the famous Ichimoku Kinko Hyo indicator.
The algorithm includes:
1. Backtesting spot
2. Visual tool
3. Auto-trading functions
With iMoku you can test four different strategies.
Strategy 1: Cross Tenkan Sen - Kijun Sen
A long position is opened with 100% of the invested capital ($1000) when "Tenkan Sen" crossover "Kijun Sen".
Closing the long position on the opposite condition.
There are 3 different strength signals for this strategy: weak, normal, strong.
Weak : the signal is weak when the condition is true and the price is above the 'Kumo'
Normal : the signal is normal when the condition is true and the price is within the 'Kumo'
Strong : the signal is strong when the condition is true and the price is below the 'Kumo'
Strategy 2: Cross Price - Kijun Sen
A long position is opened with 100% of the invested capital ($1000) when the price crossover the 'Kijun Sen'.
Closing the long position on the opposite condition.
There are 3 different strength signals for this strategy: weak, normal, strong.
Weak : the signal is weak when the condition is true and the price is above the 'Kumo'
Normal : the signal is normal when the condition is true and the price is inside the 'Kumo'
Strong : the signal is strong when the condition is true and the price is below the 'Kumo'
Strategy 3: Kumo Breakout
A long position is opened with 100% of the invested capital ($1000) when the price breakup the 'Kumo'.
Closing the long position with a percentage stop loss and take profit on the invested capital.
Strategy 4: Kumo Twist
A long position is opened with 100% of the invested capital ($1000) when the 'Kumo' goes from negative to positive (called "Twist").
Closing the long position on the opposite condition.
There are 2 different strength signals for this strategy: weak, and strong.
Weak : the signal is weak when the condition is true and the price is above the 'Kumo'
Strong : the signal is strong when the condition is true and the price is below the 'Kumo'
This script is compliant with algorithmic trading.
You can use this script with trading terminals such as 3Commas or CryptoHopper. Connecting this script is very easy.
1. Enter the user interface
2. Select and activate a strategy
3. Copy your bot's links into the dedicated fields
4. Create and activate alert
Disclaimer: algorithmic trading involves risk, the user should consider aspects such as slippage, liquidity and costs when evaluating an asset. The Quant Science is not responsible for any kind of damage resulting from use of this script. By using this script you take all the responsibilities and risks.
Customizable Non-Repainting HTF MACD MFI Scalper Bot StrategyThis script was originally shared by Wunderbit as a free open source script for the community to work with.
WHAT THIS SCRIPT DOES:
It is intended for use on an algorithmic bot trading platform but can be used for scalping and manual trading.
This strategy is based on the trend-following momentum indicator . It includes the Money Flow index as an additional point for entry.
HOW IT DOES IT:
It uses a combination of MACD and MFI indicators to create entry signals. Parameters for each indicator have been surfaced for user configurability.
Take profits are fixed, but stop loss uses ATR configuration to minimize losses and close profitably.
HOW IS MY VERSION ORIGINAL:
I started trying to deploy this script myself in my algorithmic trading but ran into some issues which I have tried to address in this version.
Delayed Signals : The script has been refactored to use a time frame drop down. The higher time frame can be run on a faster chart (recommended on one minute chart for fastest signal confirmation and relay to algotrading platform.)
Repainting Issues : All indicators have been recoded to use the security function that checks to see if the current calculation is in realtime, if it is, then it uses the previous bar for calculation. If you are still experiencing repainting issues based on intended (or non intended use), please provide a report with screenshot and explanation so I can try to address.
Filtering : I have added to additional filters an ABOVE EMA Filter and a BELOW RSI Filter (both can be turned on and off)
Customizable Long and Close Messages : This allows someone to use the script for algorithmic trading without having to alter code. It also means you can use one indicator for all of your different alterts required for your bots.
HOW TO USE IT:
It is intended to be used in the 5-30 minute time frames, but you might be able to get a good configuration for higher time frames. I welcome feedback from other users on what they have found.
Find a pair with high volatility (example KUCOIN:ETH3LUSDT ) - I have found it works particularly well with 3L and 3S tokens for crypto. although it the limitation is that confrigurations I have found to work typically have low R/R ratio, but very high win rate and profit factor.
Ideally set one minute chart for bots, but you can use other charts for manual trading. The signal will be delayed by one bar but I have found configurations that still test well.
Select a time frame in configuration for your indicator calculations.
Select the strategy config for time frame. I like to use 5 and 15 minutes for scalping scenarios, but I am interested in hearing back from other community memebers.
Optimize your indicator without filters (trendFilter and RSI Filter)
Use the TrendFilter and RSI Filter to further refine your signals for entry. You will get less entries but you can increase your win ratio.
I will add screenshots and possibly a video provided that it passes community standards.
Limitations: this works rather well for short term, and does some good forward testing but back testing large data sets is a problem when switching from very small time frame to large time frame. For instance, finding a configuration that works on a one minute chart but then changing to a 1 hour chart means you lose some of your intra bar calclulations. There are some new features in pine script which might be able to address, this, but I have not had a chance to work on that issue.
Simple Decesion Matrix Classification Algorithm [SS]Hello everyone,
It has been a while since I posted an indicator, so thought I would share this project I did for fun.
This indicator is an attempt to develop a pseudo Random Forest classification decision matrix model for Pinescript.
This is not a full, robust Random Forest model by any stretch of the imagination, but it is a good way to showcase how decision matrices can be applied to trading and within Pinescript.
As to not market this as something it is not, I am simply calling it the "Simple Decision Matrix Classification Algorithm". However, I have stolen most of the aspects of this machine learning algo from concepts of Random Forest modelling.
How it works:
With models like Support Vector Machines (SVM), Random Forest (RF) and Gradient Boosted Machine Learning (GBM), which are commonly used in Machine Learning Classification Tasks (MLCTs), this model operates similarity to the basic concepts shared amongst those modelling types. While it is not very similar to SVM, it is very similar to RF and GBM, in that it uses a "voting" system.
What do I mean by voting system?
How most classification MLAs work is by feeding an input dataset to an algorithm. The algorithm sorts this data, categorizes it, then introduces something called a confusion matrix (essentially sorting the data in no apparently order as to prevent over-fitting and introduce "confusion" to the algorithm to ensure that it is not just following a trend).
From there, the data is called upon based on current data inputs (so say we are using RSI and Z-Score, the current RSI and Z-Score is compared against other RSI's and Z-Scores that the model has saved). The model will process this information and each "tree" or "node" will vote. Then a cumulative overall vote is casted.
How does this MLA work?
This model accepts 2 independent variables. In order to keep things simple, this model was kept as a three node model. This means that there are 3 separate votes that go in to get the result. A vote is casted for each of the two independent variables and then a cumulative vote is casted for the overall verdict (the result of the model's prediction).
The model actually displays this system diagrammatically and it will likely be easier to understand if we look at the diagram to ground the example:
In the diagram, at the very top we have the classification variable that we are trying to predict. In this case, we are trying to predict whether there will be a breakout/breakdown outside of the normal ATR range (this is either yes or no question, hence a classification task).
So the question forms the basis of the input. The model will track at which points the ATR range is exceeded to the upside or downside, as well as the other variables that we wish to use to predict these exceedences. The ATR range forms the basis of all the data flowing into the model.
Then, at the second level, you will see we are using Z-Score and RSI to predict these breaks. The circle will change colour according to "feature importance". Feature importance basically just means that the indicator has a strong impact on the outcome. The stronger the importance, the more green it will be, the weaker, the more red it will be.
We can see both RSI and Z-Score are green and thus we can say they are strong options for predicting a breakout/breakdown.
So then we move down to the actual voting mechanisms. You will see the 2 pink boxes. These are the first lines of voting. What is happening here is the model is identifying the instances that are most similar and whether the classification task we have assigned (remember out ATR exceedance classifier) was either true or false based on RSI and Z-Score.
These are our 2 nodes. They both cast an individual vote. You will see in this case, both cast a vote of 1. The options are either 1 or 0. A vote of 1 means "Yes" or "Breakout likely".
However, this is not the only voting the model does. The model does one final vote based on the 2 votes. This is shown in the purple box. We can see the final vote and result at the end with the orange circle. It is 1 which means a range exceedance is anticipated and the most likely outcome.
The Data Table Component
The model has many moving parts. I have tried to represent the pivotal functions diagrammatically, but some other important aspects and background information must be obtained from the companion data table.
If we bring back our diagram from above:
We can see the data table to the left.
The data table contains 2 sections, one for each independent variable. In this case, our independent variables are RSI and Z-Score.
The data table will provide you with specifics about the independent variables, as well as about the model accuracy and outcome.
If we take a look at the first row, it simply indicates which independent variable it is looking at. If we go down to the next row where it reads "Weighted Impact", we can see a corresponding percent. The "weighted impact" is the amount of representation each independent variable has within the voting scheme. So in this case, we can see its pretty equal, 45% and 55%, This tells us that there is a slight higher representation of z-score than RSI but nothing to worry about.
If there was a major over-respresentation of greater than 30 or 40%, then the model would risk being skewed and voting too heavily in favour of 1 variable over the other.
If we move down from there we will see the next row reads "independent accuracy". The voting of each independent variable's accuracy is considered separately. This is one way we can determine feature importance, by seeing how well one feature augments the accuracy. In this case, we can see that RSI has the greatest importance, with an accuracy of around 87% at predicting breakouts. That makes sense as RSI is a momentum based oscillator.
Then if we move down one more, we will see what each independent feature (node) has voted for. In this case, both RSI and Z-Score voted for 1 (Breakout in our case).
You can weigh these in collaboration, but its always important to look at the final verdict of the model, which if we move down, we can see the "Model prediction" which is "Bullish".
If you are using the ATR breakout, the model cannot distinguish between "Bullish" or "Bearish", must that a "Breakout" is likely, either bearish or bullish. However, for the other classification tasks this model can do, the results are either Bullish or Bearish.
Using the Function:
Okay so now that all that technical stuff is out of the way, let's get into using the function. First of all this function innately provides you with 3 possible classification tasks. These include:
1. Predicting Red or Green Candle
2. Predicting Bullish / Bearish ATR
3. Predicting a Breakout from the ATR range
The possible independent variables include:
1. Stochastics,
2. MFI,
3. RSI,
4. Z-Score,
5. EMAs,
6. SMAs,
7. Volume
The model can only accept 2 independent variables, to operate within the computation time limits for pine execution.
Let's quickly go over what the numbers in the diagram mean:
The numbers being pointed at with the yellow arrows represent the cases the model is sorting and voting on. These are the most identical cases and are serving as the voting foundation for the model.
The numbers being pointed at with the pink candle is the voting results.
Extrapolating the functions (For Pine Developers:
So this is more of a feature application, so feel free to customize it to your liking and add additional inputs. But here are some key important considerations if you wish to apply this within your own code:
1. This is a BINARY classification task. The prediction must either be 0 or 1.
2. The function consists of 3 separate functions, the 2 first functions serve to build the confusion matrix and then the final "random_forest" function serves to perform the computations. You will need all 3 functions for implementation.
3. The model can only accept 2 independent variables.
I believe that is the function. Hopefully this wasn't too confusing, it is very statsy, but its a fun function for me! I use Random Forest excessively in R and always like to try to convert R things to Pinescript.
Hope you enjoy!
Safe trades everyone!
Hybrid EMA AlgoLearner⭕️Innovative trading indicator that utilizes a k-NN-inspired algorithmic approach alongside traditional Exponential Moving Averages (EMAs) for more nuanced analysis. While the algorithm doesn't actually employ machine learning techniques, it mimics the logic of the k-Nearest Neighbors (k-NN) methodology. The script takes into account the closest 'k' distances between a short-term and long-term EMA to create a weighted short-term EMA. This combination of rule-based logic and EMA technicals offers traders a more sophisticated tool for market analysis.
⭕️Foundational EMAs: The script kicks off by generating a 50-period short-term EMA and a 200-period long-term EMA. These EMAs serve a dual purpose: they provide the basic trend-following capability familiar to most traders, akin to the classic EMA 50 and EMA 200, and set the stage for more intricate calculations to follow.
⭕️k-NN Integration: The indicator distinguishes itself by introducing k-NN (k-Nearest Neighbors) logic into the mix. This machine learning technique scans prior market data to find the closest 'neighbors' or distances between the two EMAs. The 'k' closest distances are then picked for further analysis, thus imbuing the indicator with an added layer of data-driven context.
⭕️Algorithmic Weighting: After the k closest distances are identified, they are utilized to compute a weighted EMA. Each of the k closest short-term EMA values is weighted by its associated distance. These weighted values are summed up and normalized by the sum of all chosen distances. The result is a weighted short-term EMA that packs more nuanced information than a simple EMA would.
AUTOMATIC GRID BOT STRATEGY [ilovealgotrading]
OVERVIEW:
This Grid trading strategy can help you maximize your profit in a ranging sideways market with no clear direction.
INDICATOR:
We can get some money by taking advantage of the movement of the price between the range we have determined.
Short positions are opened while the price is rising, long positions are opened while the price is falling.
Therefore, there is no need to predict the trend direction.
What is different in this indicator:
I want to say thank you to © thequantscience. His GRID SPOT TRADING ALGORITHM - GRID BOT TRADING strategy helped me when I was writing my indicator.
I want to explain what I have improved:
1- Grid strategy is a type of strategy that can be traded in very short time frames and users can trade this strategy algorithmically by connecting this strategy to their own accounts with the help of API systems. For this reason, I have developed a software that can give us signals by dynamically changing the long and short messages when users are trading.
2- We can change the start and end dates of our grid bot as we want. It is necessary to use this setting when setting up automatic bots, so that previously opened transactions are not taken into account.
3 - Lot or quantity size should not be excessively small when users are taking automatic trades because exchanges have limitations, to avoid this problem, I have prevented this error by automatically rounding up to the nearest quantity size inside the software.
4 - Users can avoid excessive losses by using stop loss on this grid bot if they wish.
5 - When our price is over the range high or below the range low, our open positions are closed, if the stop button is active. We can also change which close price time frame we take as a basis from the settings.
6 -Users can set how many dollars they can enter per transaction while performing their transactions automatically.
IMPLEMENTATION DETAILS – SETTINGS:
This script allows the user to choose the highs and lows leves of our range. Our bot trades in the specified range.
1. This strategy allows us to set start and end backtest dates.
2. We can change range high and range low leves of our bot
3. IF people want to trade algorithmically with the help of this bot, there are 6 different input systems that will receive the Json codes as an alarm
4. IF the price closes above the upper line or below the lower line, all transactions will be closed. We can determine in which time frame our transactions will be stopped if the price closes outside these levels.We can adjust how our bot works by activating or turning off the Stop Loss button.
5. In this strategy, you can determine your dollar cost for per position.
6. The user can also divide the interval we have determined into 10 parts or 20 equal parts.
7. The grid is divided and colored at the interval we set. At the same time, if we don't want we can turn off colored channels.
Notes:
If you're going to connect this bot to an automatic Long and Short direction,
Don’t forget! you need to Webhook URL,
Don’t miss paste this code to your message window {{strategy.order.alert_message}}
ALSO:
Set your range below the support zones and above the resistance zones.
Don't be afraid to take a wide range, it doesn't matter if you make a little money, the important thing is that you don't lose money.
If you have any ideas what to add to my work to add more sources or make calculations cooler, suggest in DM .
BTC and ETH Long strategy - version 1I will start with a small introduction about myself. I'm now trading cryto currencies manually for almost 2 years. I decided to start after watching a documentary on the TV showing people who made big money during the Bitcoin pump which happened at the end of 2017.
The next day, I asked myself "Why should I not give it a try and learn how to trade".
This was in February 2018 and the price of Bitcoin was around 11500USD.
I didn't know how to trade. In fact, I didn't know the trading industry at all.
So, my first step into trading was to open an account with a broken. Then I directly bought 200$ worst of BTC . At that time, I saw the graph and thought "This can only go back in the upward direction!" :)
I didn't know anything about Stop loss, Take profit and Risk management.
Today, almost 2 years after, I think that I know how to trade and can also confirm that I still hold this bag of 200$ of bitcoin from 2018 :)
I did spend the 2 last years to learn technical analysis , risk management and leverage trading.
Today (14/05/2020), I know what I'm doing and I'm happy to see that the 2 last years have been positive in terms of gains. Of course, I did not make crazy money with my saving but at least I made more than if I would have kept it in my bank account.
Even if I like trading, I have a full time job which requires my full energy and lots of focus, so, the biggest problem I had is that I didn't have enough time to look at the charts.
Also, I realized that sometimes, neither technical analysis , nor fundamentals worked with crypto currency (at least for short time trading). So, as I have a developer background I decided to try to have a look at algo trading.
The goal for me was neither to make complex algos nor to beat the market but just to automate my trading with simple bot catching the big waves.
I then started to take a look at TV pine script and played with it.
I did my first LONG script in February 2020 to Long the BTC Market. It has some limitations but works well enough for me for the time being. Even if the real trades will bring me half of what the back testing shows, this will still be a lot more than what I was used to win during the last 2 years with my manual trading.
So, here we are! Below you will find some details about my first LONG script. I'm happy to share it with you.
Feel free to play with it, give your comments and bring improvements to it.
But please note that it only works fine with the candle size and crypto pair that I have mentioned below. If you use other settings this algo might loose money!
- Crypto pairs : XBTUSD and ETHXBT
- Candle size: 2 Hours
- Indicator used: Volatility , MACD (12, 26, 7), SMA (100), SMA (200), EMA (20)
- Default StopLoss: -1.5%
- Entry in position if: Volatility < 2%
AND MACD moving up
AND AME (20) moving up
AND SMA (100) moving up
AND SMA (200) moving up
AND EMA (20) > SAM (100)
AND SMA (100) > SMA (200)
- Exit the postion if: Stoploss is reached
OR EMA (20) crossUnder SMA (100)
Here is a summary of the results for this script:
XBTUSD : 01/01/2019 --> 14/05/2020 = +107%
ETHXBT : 01/01/2019 --> 14/05/2020 = +39%
ETHUSD : 01/01/2019 --> 14/05/2020 = +112%
It is far away from being perfect. There are still plenty of things which can be done to improve it but I just wanted to share it :) .
Enjoy playing with it....
Long/Short Volatility AlgoA modification of my leveraged ETF algorithm. Giving out for free because it's a sloppy algorithm, and I personally use a much more refined algorithm developed by someone much smarter than me.
DTFX Algo Zones [SamuraiJack Mod]CME_MINI:NQ1!
Credits
This indicator is a modified version of an open-source tool originally developed by Lux Algo. I literally modded their indicator to create the DTFX Algo Zones version, incorporating additional features and refinements. Special thanks to Lux Algo for their original work and for providing the open-source code that made this development possible.
Introduction
DTFX Algo Zones is a technical analysis indicator designed to automatically identify key supply and demand zones on your chart using market structure and Fibonacci retracements. It helps traders spot high-probability reversal areas and important support/resistance levels at a glance. By detecting shifts in market structure (such as Break of Structure and Change of Character) and highlighting bullish or bearish zones dynamically, this tool provides an intuitive framework for planning trades. The goal is to save traders time and improve decision-making by focusing attention on the most critical price zones where market bias may confirm or reverse.
Logic & Features
• Market Structure Shift Detection (BOS & CHoCH): The indicator continuously monitors price swings and marks significant structure shifts. A Break of Structure (BOS) occurs when price breaks above a previous swing high or below a swing low, indicating a continuation of the current trend. A Change of Character (ChoCH) is detected when price breaks in the opposite direction of the prior trend, often signaling an early trend reversal. These moments are visually marked on the chart, serving as anchor points for new zones. By identifying BOS and ChoCH in real-time, the DTFX Algo Zones indicator ensures you’re aware of key trend changes as they happen.
• Auto-Drawn Fibonacci Supply/Demand Zones: Upon a valid structure shift, the indicator plots a Fibonacci-based zone between the breakout point and the preceding swing high/low (the source of the move). This creates a shaded area or band of Fibonacci retracement levels (for example 38.2%, 50%, 61.8%, etc.) representing a potential support zone in an uptrend or resistance zone in a downtrend. These supply/demand zones are derived from the natural retracement of the breakout move, highlighting where price is likely to pull back. Each zone is essentially an auto-generated Fibonacci retracement region tied to a market structure event, which traders can use to anticipate where the next pullback or bounce might occur.
• Dynamic Bullish and Bearish Zones: The DTFX Algo Zones indicator distinguishes bullish vs. bearish zones and updates them dynamically as new price action unfolds. Bullish zones (formed after bullish BOS/ChoCH) are typically highlighted in one color (e.g. green or blue) to indicate areas of demand/support where price may bounce upward. Bearish zones (formed after bearish BOS/ChoCH) are shown in another color (e.g. red/orange) to mark supply/resistance where price may stall or reverse downward. This color-coding and real-time updating allow traders to instantly recognize the market bias: for instance, a series of bullish zones implies an uptrend with multiple support levels on pullbacks, while consecutive bearish zones indicate a downtrend with resistance overhead. As old zones get invalidated or new ones appear, the chart remains current with the latest key levels, eliminating clutter from outdated levels.
• Flexible Customization: The indicator comes with several options to tailor the zones to your trading style. You can filter which zones to display – for example, show only the most recent N zones or limit to only bullish or only bearish zones – helping declutter the chart and focus on recent, relevant levels. There are settings to control zone extension (how far into the future the zones are drawn) and to automatically invalidate zones once they’re no longer relevant (for instance, if price fully breaks through a zone or a new structure shift occurs that supersedes it). Additionally, the Fibonacci retracement levels within each zone are customizable: you can choose which retracement percentages to plot, adjust their colors or line styles, and decide whether to fill the zone area for visibility. This flexibility ensures the DTFX Algo Zones can be tuned for different markets and strategies, whether you want a clean minimalist look or detailed zones with multiple internal levels.
Best Use Cases
DTFX Algo Zones is a versatile indicator that can enhance various trading strategies. Some of its best use cases include:
• Identifying High-Probability Reversal Zones: Each zone marks an area where price has a higher likelihood of stalling or reversing because it reflects a significant prior swing and Fibonacci retracement. Traders can watch these zones for entry opportunities when the market approaches them, as they often coincide with order block or strong supply/demand areas. This is especially useful for catching trend reversals or pullbacks at points where risk is lower and potential reward is higher.
• Spotting Key Support and Resistance: The automatically drawn zones act as dynamic support (below price) and resistance (above price) levels. Instead of manually drawing Fibonacci retracements or support/resistance lines, you get an instant map of the key levels derived from recent price action. This helps in quickly identifying where the next bounce (support) or rejection (resistance) might occur. Swing traders and intraday traders alike can use these zones to set alerts or anticipate reaction areas as the market moves.
• Trend-Following Entries: In a trending market, the indicator’s zones provide ideal areas to join the trend on pullbacks. For example, in an uptrend, when a new bullish zone is drawn after a BOS, it indicates a fresh demand zone – buying near the lower end of that zone on a pullback can offer a low-risk entry to ride the next leg up. Similarly, in a downtrend, selling rallies into the highlighted supply zones can position you in the direction of the prevailing trend. The zones effectively serve as a roadmap of the trend’s structure, allowing trend traders to buy dips and sell rallies with greater confidence.
• Mean-Reversion and Range Trading: Even in choppy or range-bound markets, DTFX Algo Zones can help find mean-reversion trades. If price is oscillating sideways, the zones at extremes of the range might mark where momentum is shifting (ChoCH) and price could swing back toward the mean. A trader might fade an extended move when it reaches a strong zone, anticipating a reversion. Additionally, if multiple zones cluster in an area across time (creating a zone overlap), it often signifies a particularly robust support/resistance level ideal for range trading strategies.
In all these use cases, the indicator’s ability to filter out noise and highlight structurally important levels means traders can focus on higher-probability setups and make more informed trading decisions.
Strategy – Pullback Trading with DTFX Algo Zones
One of the most effective ways to use the DTFX Algo Zones indicator is trading pullbacks in the direction of the trend. Below is a step-by-step strategy to capitalize on pullbacks using the zones, combining the indicator’s signals with sound price action analysis and risk management:
1. Identify a Market Structure Shift and Trend Bias: First, observe the chart for a recent BOS or ChoCH signal from the indicator. This will tell you the current trend bias. For instance, a bullish BOS/ChoCH means the market momentum has shifted upward (bullish bias), and a new demand zone will be drawn. A bearish structure break indicates downward momentum and creates a supply zone. Make sure the broader context supports the bias (e.g., if multiple higher timeframe zones are bullish, focus on long trades).
2. Wait for the Pullback into the Zone: Once a new zone appears, don’t chase the price immediately. Instead, wait for price to retrace back into that highlighted zone. Patience is key – let the market come to you. For a bullish setup, allow price to dip into the Fibonacci retracement zone (demand area); for a bearish setup, watch for a rally into the supply zone. Often, the middle of the zone (around the 50% retracement level) can be an optimal area where price might slow down and pivot, but it’s wise to observe price behavior across the entire zone.
3. Confirm the Entry with Price Action & Confluence: As price tests the zone, look for confirmation signals before entering the trade. This can include bullish reversal candlestick patterns (for longs) or bearish patterns (for shorts) such as engulfing candles, hammers/shooting stars, or doji indicating indecision turning to reversal. Additionally, incorporate confluence factors to strengthen the setup: for example, check if the zone overlaps with a key moving average, a round number price level, or an old support/resistance line from a higher timeframe. You might also use an oscillator (like RSI or Stochastic) to see if the pullback has reached oversold conditions in a bullish zone (or overbought in a bearish zone), suggesting a bounce is likely. The more factors aligning at the zone, the more confidence you can have in the trade. Only proceed with an entry once you see clear evidence of buyers defending a demand zone or sellers defending a supply zone.
4. Enter the Trade and Manage Risk: When you’re satisfied with the confirmation (e.g., price starts to react positively off a demand zone or shows rejection wicks in a supply zone), execute your entry in the direction of the original trend. Immediately set a stop-loss order to control risk: for a long trade, a common placement is just below the demand zone (a few ticks/pips under the swing low that formed the zone); for a short trade, place the stop just above the supply zone’s high. This way, if the zone fails and price continues beyond it, your loss is limited. Position size the trade so that this stop-loss distance corresponds to a risk you are comfortable with (for example, 1-2% of your trading capital).
5. Take Profit Strategically: Plan your take-profit targets in advance. A conservative approach is to target the origin of the move – for instance, in a long trade, you might take profit as price moves back up to the swing high (the 0% Fibonacci level of the zone) or the next significant zone or resistance level above. This often yields at least a 1:1 reward-to-risk ratio if you entered around mid-zone. More aggressive trend-following traders may leave a portion of the position running beyond the initial target, aiming for a larger move in line with the trend (for example, new higher highs in an uptrend). You can also trail your stop-loss upward behind new higher lows (for longs) or lower highs (for shorts) as the trend progresses, locking in profit while allowing for further gains.
6. Monitor Zone Invalidation: Even after entering, keep an eye on the behavior around the zone and any new zones that may form. If price fails to bounce and instead breaks decisively through the entire zone, respect that as an invalidation – the market may be signaling a deeper reversal or that the signal was false. In such a case, it’s better to exit early or stick to your stop-loss than to hold onto a losing position. The indicator will often mark or no longer highlight zones that have been invalidated by price, guiding you to shift focus to the next opportunity.
Risk Management Tips:
• Always use a stop-loss and don’t move it farther out in hope. Placing the stop just beyond the zone’s far end (the swing point) helps protect you if the pullback turns into a larger reversal.
• Aim for a favorable risk-to-reward ratio. With pullback entries near the middle or far end of a zone, you can often achieve a reward that equals or exceeds your risk. For example, risking 20 pips to make 20+ pips (1:1 or better) is a prudent starting point. Adjust targets based on market structure – if the next resistance is 50 pips away, consider that upside against your risk.
• Use confluence and context: Don’t take every zone signal in isolation. The highest probability trades come when the DTFX Algo Zone aligns with other analysis (trend direction, chart patterns, higher timeframe support/resistance, etc.). This filtered approach will reduce trades taken in weak zones or counter-trend traps.
• Embrace patience and selectivity: Not all zones are equal. It can be wise to skip very narrow or insignificant zones and wait for those that form after a strong BOS/ChoCH (indicating a powerful move). Larger zones or zones formed during high-volume times tend to produce more reliable pullback opportunities.
• Review and adapt: After each trade, note how price behaved around the zone. If you notice certain Fib levels (like 50% or 61.8%) within the zone consistently provide the best entries, you can refine your approach to focus on those. Similarly, adjust the indicator’s settings if needed – for example, if too many minor zones are cluttering your screen, limit to the last few or increase the structure length parameter to capture only more significant swings.
⸻
By combining the DTFX Algo Zones indicator with disciplined confirmation and risk management, traders can improve their timing on pullback entries and avoid chasing moves. This indicator shines in helping you trade what you see, not what you feel – the clearly marked zones and structure shifts keep you grounded in price action reality. Whether you’re a trend trader looking to buy the dip/sell the rally, or a reversal trader hunting for exhaustion points, DTFX Algo Zones provides a robust visual aid to elevate your trading decisions. Use it as a complementary tool in your analysis to stay on the right side of the market’s structure and enhance your trading performance.
Grid Spot Trading Algorithm V2 - The Quant ScienceGrid Spot Trading Algorithm V2 is the last grid trading algorithm made by our developer team.
Grid Spot Trading Algorithm V2 is a fixed 10-level grid trading algorithm. The grid is divided into an accumulation area (red) and a selling area (green).
In the accumulation area, the algorithm will place new buy orders, selling the long positions on the top of the grid.
BUYING AND SELLING LOGIC
The algorithm places up to 5 limit orders on the accumulation section of the grid, each time the price cross through the middle grid. Each single order uses 20% of the equity.
Positions are closed at the top of the grid by default, with the algorithm closing all orders at the first sell level. The exit level can be adjusted using the user interface, from the first level up to the fifth level above.
CONFIGURING THE ALGORITHM
1) Add it to the chart: Add the script to the current chart that you want to analyze.
2) Select the top of the grid: Confirm a price level with the mouse on which to fix the top of the grid.
3) Select the bottom of the grid: Confirm a price level with the mouse on which to fix the bottom of the grid.
4) Wait for the automatic creation of the grid.
USING THE ALGORITHM
Once the grid configuration process is completed, the algorithm will generate automatic backtesting.
You can add a stop loss that destroys the grid by setting the destruction price and activating the feature from the user interface. When the stop loss is activated, you can view it on the chart.
FunctionArrayMaxSubKadanesAlgorithmLibrary "FunctionArrayMaxSubKadanesAlgorithm"
Implements Kadane's maximum sum sub array algorithm.
size(samples) Kadanes algorithm.
Parameters:
samples : float array, sample data values.
Returns: float.
indices(samples) Kadane's algorithm with indices.
Parameters:
samples : float array, sample data values.
Returns: tuple with format .
Ichimoku Cloud Strategy Long Only [Bitduke]Slightly modificated and optimized for Pine Script 4.0, Ichimoku Cloud Strategy which, suddenly, good suitable for the several crypto assets.
Details:
Enter position when conversion line crosses base line up, and close it when the opposite happens.
Additional condition for open / close the trade is lagging span, it should be higher than cloud to open position and below - to close it.
Backtesting:
Backtested on SOLUSDT ( FTX, Binance )
+150% for 2021 year, 8% dd
+191% for all time, 32% dd
Disadvantages:
- Small number of trades
- Need to vary parameters for different coins (not very robust)
Should be tested carefully for other coins / stock market. Different parameters could be needed or even algo modifications.
Strategy doesn't repaint.
PriceLevels GBGoldbach Price Levels – Identify Algorithmic Key Zones
This open-source indicator is designed to help traders identify potential algorithmic key zones by highlighting price levels ending with specific numbers such as 03, 11, 29, 35, 65, and 71. These levels may act as inflection points or hesitation areas based on observed behavioral patterns in price movement.
What It Does:
📌 Scans and plots horizontal price levels where the price ends with one of the selected number combinations
🎯 Toggle on/off visibility for each number ending
🎨 Customize color and thickness for each level
🏷️ Shows price labels at the end of each line
🌗 Label styles (color/transparency) are adjustable for both dark and light chart themes
🧠 Why Use It:
This tool is ideal for discretionary traders who study market structure through static price anchors. It provides a visual reference for recurring numerical levels that may be used in algorithmic trading models or serve as psychological price zones.
⚠️ Disclaimer:
This script is open-source and intended for educational and analytical purposes only. No trading signals or performance guarantees are provided. Please use your own judgment when applying this tool in a trading context.
Market Structure AlgoThe "Market Structure Algo" (MS Algo) is a comprehensive tool developed by OmegaTools. This advanced indicator is designed to analyze the market's structure through a combination of pivot highs and lows, creating a nuanced understanding of potential market movements.
Core Functionality:
- Internal and External Market Structure (MS): The MS Algo differentiates between internal and external market structures by analyzing pivot points over different periods. This dual analysis allows for a deeper understanding of short-term and long-term market trends.
- Zone Distance and Visualization: The indicator introduces a novel approach to visualizing potential areas of interest or 'zones' around pivot points, adjustable through the 'Zone Distance' setting. This feature enhances the visual representation of zone created on the chart that can be used as a support and resistance area.
- Dynamic Signal Generation: Utilizing a comprehensive algorithm, the MS Algo identifies potential signals for entering and exiting trades based on the internal market structure. These signals are visually represented on the chart, aiding in decision-making. These signals are based on the acceptance and confirmed breakout or the refusal of the pivot points by the price.
Operational Mechanism:
- The MS Algo calculates pivot highs and lows over specified periods (input by the user) to determine the market's current structure. It then evaluates the market's position relative to these pivot points to assign a market structure score, which can range from bullish to bearish extremes.
- Signals for long and short positions, as well as exits, are generated based on the interaction between the close price and these pivot points.
- Additionally, the indicator plots zones around the moving average, adjusted for the ATR and the specified 'Zone Distance,' providing a visual guide to areas where the market might find support or resistance.
Usage Guidelines:
- To apply the MS Algo to your TradingView charts, adjust the 'Internal MS' and 'External MS' settings to align with your analysis preferences. The 'Zone Distance' input allows for customization of the zone visualization feature.
- The color-coded signals and zone fillings serve as guides to understanding the current market structure and potential areas of interest. These should be interpreted within the context of a broader trading strategy and risk management framework.
Understanding the Indicator's Originality:
The MS Algo stands out due to its unique blend of pivot analysis and zone visualization, providing traders with a detailed view of the market's structure that goes beyond traditional indicators. Its originality lies in the methodological integration of these components to offer a tool that enhances market analysis.
Responsible Use Disclaimer:
The financial markets are unpredictable, and the MS Algo is designed to serve as an analytical tool within a trader's arsenal, not a standalone solution for trading decisions. Traders should use this tool judiciously, alongside comprehensive market analysis and sound risk management practices. It's important to understand that the MS Algo does not guarantee trading success nor does it claim to predict specific price movements. Trading involves risks, including the potential loss of capital.
Zero Lag Trend Signals (MTF) [AlgoAlpha]// This Pine Script™ code is subject to the terms of the Mozilla Public License 2.0 at mozilla.org
// © AlgoAlpha
//@version=5
indicator("Zero Lag Trend Signals (MTF) ", shorttitle="AlgoAlpha - 0️⃣Zero Lag Signals", overlay=true)
length = input.int(70, "Length", tooltip = "The Look-Back window for the Zero-Lag EMA calculations", group = "Main Calculations")
mult = input.float(1.2, "Band Multiplier", tooltip = "This value controls the thickness of the bands, a larger value makes the indicato less noisy", group = "Main Calculations")
t1 = input.timeframe("5", "Time frame 1", group = "Extra Timeframes")
t2 = input.timeframe("15", "Time frame 2", group = "Extra Timeframes")
t3 = input.timeframe("60", "Time frame 3", group = "Extra Timeframes")
t4 = input.timeframe("240", "Time frame 4", group = "Extra Timeframes")
t5 = input.timeframe("1D", "Time frame 5", group = "Extra Timeframes")
green = input.color(#00ffbb, "Bullish Color", group = "Appearance")
red = input.color(#ff1100, "Bearish Color", group = "Appearance")
src = close
lag = math.floor((length - 1) / 2)
zlema = ta.ema(src + (src - src ), length)
volatility = ta.highest(ta.atr(length), length*3) * mult
var trend = 0
if ta.crossover(close, zlema+volatility)
trend := 1
if ta.crossunder(close, zlema-volatility)
trend := -1
zlemaColor = trend == 1 ? color.new(green, 70) : color.new(red, 70)
m = plot(zlema, title="Zero Lag Basis", linewidth=2, color=zlemaColor)
upper = plot(trend == -1 ? zlema+volatility : na, style = plot.style_linebr, color = color.new(red, 90), title = "Upper Deviation Band")
lower = plot(trend == 1 ? zlema-volatility : na, style = plot.style_linebr, color = color.new(green, 90), title = "Lower Deviation Band")
fill(m, upper, (open + close) / 2, zlema+volatility, color.new(red, 90), color.new(red, 70))
fill(m, lower, (open + close) / 2, zlema-volatility, color.new(green, 90), color.new(green, 70))
plotshape(ta.crossunder(trend, 0) ? zlema+volatility : na, "Bearish Trend", shape.labeldown, location.absolute, red, text = "▼", textcolor = chart.fg_color, size = size.small)
plotshape(ta.crossover(trend, 0) ? zlema-volatility : na, "Bullish Trend", shape.labelup, location.absolute, green, text = "▲", textcolor = chart.fg_color, size = size.small)
plotchar(ta.crossover(close, zlema) and trend == 1 and trend == 1 ? zlema-volatility*1.5 : na, "Bullish Entry", "▲", location.absolute, green, size = size.tiny)
plotchar(ta.crossunder(close, zlema) and trend == -1 and trend == -1 ? zlema+volatility*1.5 : na, "Bearish Entry", "▼", location.absolute, red, size = size.tiny)
s1 = request.security(syminfo.tickerid, t1, trend)
s2 = request.security(syminfo.tickerid, t2, trend)
s3 = request.security(syminfo.tickerid, t3, trend)
s4 = request.security(syminfo.tickerid, t4, trend)
s5 = request.security(syminfo.tickerid, t5, trend)
s1a = s1 == 1 ? "Bullish" : "Bearish"
s2a = s2 == 1 ? "Bullish" : "Bearish"
s3a = s3 == 1 ? "Bullish" : "Bearish"
s4a = s4 == 1 ? "Bullish" : "Bearish"
s5a = s5 == 1 ? "Bullish" : "Bearish"
if barstate.islast
var data_table = table.new(position=position.top_right, columns=2, rows=6, bgcolor=chart.bg_color, border_width=1, border_color=chart.fg_color, frame_color=chart.fg_color, frame_width=1)
table.cell(data_table, text_halign=text.align_center, column=0, row=0, text="Time Frame", text_color=chart.fg_color)
table.cell(data_table, text_halign=text.align_center, column=1, row=0, text="Signal", text_color=chart.fg_color)
table.cell(data_table, text_halign=text.align_center, column=0, row=1, text=t1, text_color=chart.fg_color)
table.cell(data_table, text_halign=text.align_center, column=1, row=1, text=s1a, text_color=chart.fg_color, bgcolor=s1a == "Bullish" ? color.new(green, 70) : color.new(red, 70))
table.cell(data_table, text_halign=text.align_center, column=0, row=2, text=t2, text_color=chart.fg_color)
table.cell(data_table, text_halign=text.align_center, column=1, row=2, text=s2a, text_color=chart.fg_color, bgcolor=s2a == "Bullish" ? color.new(green, 70) : color.new(red, 70))
table.cell(data_table, text_halign=text.align_center, column=0, row=3, text=t3, text_color=chart.fg_color)
table.cell(data_table, text_halign=text.align_center, column=1, row=3, text=s3a, text_color=chart.fg_color, bgcolor=s3a == "Bullish" ? color.new(green, 70) : color.new(red, 70))
table.cell(data_table, text_halign=text.align_center, column=0, row=4, text=t4, text_color=chart.fg_color)
table.cell(data_table, text_halign=text.align_center, column=1, row=4, text=s4a, text_color=chart.fg_color, bgcolor=s4a == "Bullish" ? color.new(green, 70) : color.new(red, 70))
table.cell(data_table, text_halign=text.align_center, column=0, row=5, text=t5, text_color=chart.fg_color)
table.cell(data_table, text_halign=text.align_center, column=1, row=5, text=s5a, text_color=chart.fg_color, bgcolor=s5a == "Bullish" ? color.new(green, 70) : color.new(red, 70))
/////////////////////////////////////////ALERTS FOR SMALL ARROWS (ENTRY SIGNALS)
alertcondition(ta.crossover(close, zlema) and trend == 1 and trend == 1, "Bullish Entry Signal",
message="Bullish Entry Signal detected. Consider entering a long position.")
alertcondition(ta.crossunder(close, zlema) and trend == -1 and trend == -1, "Bearish Entry Signal",
message="Bearish Entry Signal detected. Consider entering a short position.")
/////////////////////////////////////////ALERTS FOR TREND CONDITIONS
alertcondition(ta.crossover(trend, 0), "Bullish Trend")
alertcondition(ta.crossunder(trend, 0), "Bearish Trend")
alertcondition(ta.cross(trend, 0), "(Bullish or Bearish) Trend")
alertcondition(ta.crossover(s1, 0), "Bullish Trend Time Frame 1")
alertcondition(ta.crossunder(s1, 0), "Bearish Trend Time Frame 1")
alertcondition(ta.cross(s1, 0), "(Bullish or Bearish) Trend Time Frame 1")
alertcondition(ta.crossover(s2, 0), "Bullish Trend Time Frame 2")
alertcondition(ta.crossunder(s2, 0), "Bearish Trend Time Frame 2")
alertcondition(ta.cross(s2, 0), "(Bullish or Bearish) Trend Time Frame 2")
alertcondition(ta.crossover(s3, 0), "Bullish Trend Time Frame 3")
alertcondition(ta.crossunder(s3, 0), "Bearish Trend Time Frame 3")
alertcondition(ta.cross(s3, 0), "(Bullish or Bearish) Trend Time Frame 3")
alertcondition(ta.crossover(s4, 0), "Bullish Trend Time Frame 4")
alertcondition(ta.crossunder(s4, 0), "Bearish Trend Time Frame 4")
alertcondition(ta.cross(s4, 0), "(Bullish or Bearish) Trend Time Frame 4")
alertcondition(ta.crossover(s5, 0), "Bullish Trend Time Frame 5")
alertcondition(ta.crossunder(s5, 0), "Bearish Trend Time Frame 5")
alertcondition(ta.cross(s5, 0), "(Bullish or Bearish) Trend Time Frame 5")
alertcondition(ta.crossover(close, zlema) and trend == 1 and trend == 1, "Bullish Entry")
alertcondition(ta.crossunder(close, zlema) and trend == -1 and trend == -1, "Bearish Entry")
bullishAgreement = s1 == 1 and s2 == 1 and s3 == 1 and s4 == 1 and s5 == 1
bearishAgreement = s1 == -1 and s2 == -1 and s3 == -1 and s4 == -1 and s5 == -1
alertcondition(bullishAgreement, "Full Bullish Agreement", message="All timeframes agree on bullish trend.")
alertcondition(bearishAgreement, "Full Bearish Agreement", message="All timeframes agree on bearish trend.")
Trendline Breaks with Multi Fibonacci Supertrend StrategyTMFS Strategy: Advanced Trendline Breakouts with Multi-Fibonacci Supertrend
Elevate your algorithmic trading with institutional-grade signal confluence
Strategy Genesis & Evolution
This advanced trading system represents the culmination of a personal research journey, evolving from my custom " Multi Fibonacci Supertrend with Signals " indicator into a comprehensive trading strategy. Built upon the exceptional trendline detection methodology pioneered by LuxAlgo in their " Trendlines with Breaks " indicator, I've engineered a systematic framework that integrates multiple technical factors into a cohesive trading system.
Core Fibonacci Principles
At the heart of this strategy lies the Fibonacci sequence application to volatility measurement:
// Fibonacci-based factors for multiple Supertrend calculations
factor1 = input.float(0.618, 'Factor 1 (Weak/Fibonacci)', minval = 0.01, step = 0.01)
factor2 = input.float(1.618, 'Factor 2 (Medium/Golden Ratio)', minval = 0.01, step = 0.01)
factor3 = input.float(2.618, 'Factor 3 (Strong/Extended Fib)', minval = 0.01, step = 0.01)
These precise Fibonacci ratios create a dynamic volatility envelope that adapts to changing market conditions while maintaining mathematical harmony with natural price movements.
Dynamic Trendline Detection
The strategy incorporates LuxAlgo's pioneering approach to trendline detection:
// Pivotal swing detection (inspired by LuxAlgo)
pivot_high = ta.pivothigh(swing_length, swing_length)
pivot_low = ta.pivotlow(swing_length, swing_length)
// Dynamic slope calculation using ATR
slope = atr_value / swing_length * atr_multiplier
// Update trendlines based on pivot detection
if bool(pivot_high)
upper_slope := slope
upper_trendline := pivot_high
else
upper_trendline := nz(upper_trendline) - nz(upper_slope)
This adaptive trendline approach automatically identifies key structural market boundaries, adjusting in real-time to evolving chart patterns.
Breakout State Management
The strategy implements sophisticated state tracking for breakout detection:
// Track breakouts with state variables
var int upper_breakout_state = 0
var int lower_breakout_state = 0
// Update breakout state when price crosses trendlines
upper_breakout_state := bool(pivot_high) ? 0 : close > upper_trendline ? 1 : upper_breakout_state
lower_breakout_state := bool(pivot_low) ? 0 : close < lower_trendline ? 1 : lower_breakout_state
// Detect new breakouts (state transitions)
bool new_upper_breakout = upper_breakout_state > upper_breakout_state
bool new_lower_breakout = lower_breakout_state > lower_breakout_state
This state-based approach enables precise identification of the exact moment when price breaks through a significant trendline.
Multi-Factor Signal Confluence
Entry signals require confirmation from multiple technical factors:
// Define entry conditions with multi-factor confluence
long_entry_condition = enable_long_positions and
upper_breakout_state > upper_breakout_state and // New trendline breakout
di_plus > di_minus and // Bullish DMI confirmation
close > smoothed_trend // Price above Supertrend envelope
// Execute trades only with full confirmation
if long_entry_condition
strategy.entry('L', strategy.long, comment = "LONG")
This strict requirement for confluence significantly reduces false signals and improves the quality of trade entries.
Advanced Risk Management
The strategy includes sophisticated risk controls with multiple methodologies:
// Calculate stop loss based on selected method
get_long_stop_loss_price(base_price) =>
switch stop_loss_method
'PERC' => base_price * (1 - long_stop_loss_percent)
'ATR' => base_price - long_stop_loss_atr_multiplier * entry_atr
'RR' => base_price - (get_long_take_profit_price() - base_price) / long_risk_reward_ratio
=> na
// Implement trailing functionality
strategy.exit(
id = 'Long Take Profit / Stop Loss',
from_entry = 'L',
qty_percent = take_profit_quantity_percent,
limit = trailing_take_profit_enabled ? na : long_take_profit_price,
stop = long_stop_loss_price,
trail_price = trailing_take_profit_enabled ? long_take_profit_price : na,
trail_offset = trailing_take_profit_enabled ? long_trailing_tp_step_ticks : na,
comment = "TP/SL Triggered"
)
This flexible approach adapts to varying market conditions while providing comprehensive downside protection.
Performance Characteristics
Rigorous backtesting demonstrates exceptional capital appreciation potential with impressive risk-adjusted metrics:
Remarkable total return profile (1,517%+)
Strong Sortino ratio (3.691) indicating superior downside risk control
Profit factor of 1.924 across all trades (2.153 for long positions)
Win rate exceeding 35% with balanced distribution across varied market conditions
Institutional Considerations
The strategy architecture addresses execution complexities faced by institutional participants with temporal filtering and date-range capabilities:
// Time Filter settings with flexible timezone support
import jason5480/time_filters/5 as time_filter
src_timezone = input.string(defval = 'Exchange', title = 'Source Timezone')
dst_timezone = input.string(defval = 'Exchange', title = 'Destination Timezone')
// Date range filtering for precise execution windows
use_from_date = input.bool(defval = true, title = 'Enable Start Date')
from_date = input.time(defval = timestamp('01 Jan 2022 00:00'), title = 'Start Date')
// Validate trading permission based on temporal constraints
date_filter_approved = time_filter.is_in_date_range(
use_from_date, from_date, use_to_date, to_date, src_timezone, dst_timezone
)
These capabilities enable precise execution timing and market session optimization critical for larger market participants.
Acknowledgments
Special thanks to LuxAlgo for the pioneering work on trendline detection and breakout identification that inspired elements of this strategy. Their innovative approach to technical analysis provided a valuable foundation upon which I could build my Fibonacci-based methodology.
This strategy is shared under the same Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license as LuxAlgo's original work.
Past performance is not indicative of future results. Conduct thorough analysis before implementing any algorithmic strategy.
Ichimoku Score Indicator [tanayroy]The Ichimoku Cloud is a comprehensive indicator that provides a clear view of market states through five key components. By analyzing the interaction between these components and the asset's price, traders can gain insights into trend direction, momentum, and potential reversals.
Introducing the Ichimoku Score System
I have developed a scoring system that quantifies these interactions, offering an objective method to evaluate market conditions. The score is calculated based on the relative positioning of Ichimoku components, with adjustable weightings via user input.
Scoring Criteria
Each component contributes to the overall score as follows:
Price vs. Cloud (Kumo) & Other Components
Price vs. Kumo → 2 Points
Price vs. Kumo Shadow → 0.5 Points
Tenkan vs. Kijun
Tenkan vs. Kijun → 2 Points
Tenkan vs. Kumo → 0.5 Points
Kijun vs. Kumo → 0.5 Points
Tenkan Slope → 0.5 Points
Kijun Slope → 0.5 Points
Chikou Span Interactions
Price vs. Chikou → 2 Points
Chikou vs. Kumo → 0.5 Points
Chikou Consolidation → 0.5 Points
Senkou Span Analysis
Senkou A vs. Senkou B → 2 Points
Senkou Slope → 0.5 Points
Price vs. Key Levels
Price vs. Tenkan → 2 Points
Price vs. Kijun → 2 Points
Interpreting the Score
The aggregate score functions as an oscillator, fluctuating between a range of ±16.0.
A higher score indicates strong bullish momentum.
A lower score suggests bearish market conditions.
To enhance readability and smooth fluctuations, a 9-period SMA is applied to the score.
Application in Algorithmic Trading
This scoring system helps integrate Ichimoku Cloud principles into algorithmic trading strategies by providing a structured and quantifiable method for assessing market conditions.
Would love to hear your feedback! 🚀 Let me know how this system works for you.
ICT Algorithmic Macro Tracker° (Open-Source) by toodegreesDescription:
The ICT Algorithmic Macro Tracker° Indicator is a powerful tool designed to enhance your trading experience by clearly and efficiently plotting the known ICT Macro Times on your chart.
Based on the teachings of the Inner Circle Trader , these Time windows correspond to periods when the Interbank Price Delivery Algorithm undergoes a series of checks ( Macros ) and is probable to move towards Liquidity.
The indicator allows traders to visualize and analyze these crucial moments in NY Time:
- 2:33-3:00
- 4:03-4:30
- 8:50-9:10
- 9:50-10:10
- 10:50-11:10
- 11:50-12:10
- 13:10-13:50
- 15:15-15:45
By providing a clean and clutter-free representation of ICT Macros, this indicator empowers traders to make more informed decisions, optimize and build their strategies based on Time.
Massive shoutout to @reastruth for his ICT Macros Indicator , and for allowing to create one of my own, go check him out!
Indicator Features:
– Track ongoing ICT Macros to aid your Live analysis.
- Gain valuable insights by hovering over the plotted ICT Macros to reveal tooltips with interval information.
– Plot the ICT Macros in one of two ways:
"On Chart": visualize ICT Macro timeframes directly on your chart, with automatic adjustments as Price moves.
Pro Tip: toggle Projections to see exactly where Macros begin and end without difficulty.
"New Pane": move the indicator two a New Pane to see both Live and Upcoming Macro events with ease in a dedicated section
Pro Tip: this section can be collapsed by double-clicking on the main chart, allowing for seamless trading preparation.
This indicator is available only on the TradingView platform.
⚠️ Open Source ⚠️
Coders and TV users are authorized to copy this code base, but a paid distribution is prohibited. A mention to the original author is expected, and appreciated.
⚠️ Terms and Conditions ⚠️
This financial tool is for educational purposes only and not financial advice. Users assume responsibility for decisions made based on the tool's information. Past performance doesn't guarantee future results. By using this tool, users agree to these terms.
MarkovAlgorithmLibrary "MarkovAlgorithm"
Markov algorithm is a string rewriting system that uses grammar-like rules to operate on strings of
symbols. Markov algorithms have been shown to be Turing-complete, which means that they are suitable as a
general model of computation and can represent any mathematical expression from its simple notation.
~ wikipedia
.
reference:
en.wikipedia.org
rosettacode.org
parse(rules, separator)
Parameters:
rules (string)
separator (string)
Returns: - `array _rules`: List of rules.
---
Usage:
- `parse("|0 -> 0||\n1 -> 0|\n0 -> ")`
apply(expression, rules)
Aplies rules to a expression.
Parameters:
expression (string) : `string`: Text expression to be formated by the rules.
rules (rule ) : `string`: Rules to apply to expression on a string format to be parsed.
Returns: - `string _result`: Formated expression.
---
Usage:
- `apply("101", parse("|0 -> 0||\n1 -> 0|\n0 -> "))`
apply(expression, rules)
Parameters:
expression (string)
rules (string)
Returns: - `string _result`: Formated expression.
---
Usage:
- `apply("101", parse("|0 -> 0||\n1 -> 0|\n0 -> "))`
rule
String pair that represents `pattern -> replace`, each rule may be ordinary or terminating.
Fields:
pattern (series string) : Pattern to replace.
replacement (series string) : Replacement patterns.
termination (series bool) : Termination rule.
Level 1 - Learn to code simply - PineScriptThe goal of this script is honestly to help everyone learn about trading with bots and algos.
At least, to get started.
Level 1:
10 lines of code.
learn to plot 2 moving averages on your chart.
learn to create a signal from a crossover.
learn the very basics of Pine Script algo.
Pivot Reversal Strategy + alerts via TradingConnector to indicesSoftware part of algotrading is simpler than you think. TradingView is a great place to do this actually. To present it, I'm publishing each of the default strategies you can find in Pinescript editor's "built-in" list with slight modification - I'm only adding 2 lines of code, which will trigger alerts, ready to be forwarded to your broker via TradingConnector and instantly executed there. Alerts added in this script: 14 and 22.
How it works:
1. TradingView alert fires.
2. TradingConnector catches it and forwards to MetaTrader4/5 you got from your broker.
3. Trade gets executed inside MetaTrader within 1 second of fired alert.
When configuring alert, make sure to select "alert() function calls only" in CreateAlert popup. One alert per ticker is required.
Adding stop-loss, take-profit, trailing-stop, break-even or executing pending orders is also possible. These topics have been covered in other example posts.
This routing works for Forex, indices, stocks, crypto - anything your broker offers via their MetaTrader4 or 5.
Disclaimer: This concept is presented for educational purposes only. Profitable results of trading this strategy are not guaranteed even if the backtest suggests so. By no means this post can be considered a trading advice. You trade at your own risk.
If you are thinking to execute this particular strategy, make sure to find the instrument, settings and timeframe which you like most. You can do this by your own research only.
RSI Strategy with alerts via TradingConnector to ForexSoftware part of algotrading is simpler than you think. TradingView is a great place to do this actually. To present it, I'm publishing each of the default strategies you can find in Pinescript editor's "built-in" list with slight modification - I'm only adding 2 lines of code, which will trigger alerts, ready to be forwarded to your broker via TradingConnector and instantly executed there. Alerts added in this script: 12 and 17.
How it works:
1. TradingView alert fires.
2. TradingConnector catches it and forwards to MetaTrader4/5 you got from your broker.
3. Trade gets executed inside MetaTrader within 1 second of fired alert.
When configuring alert, make sure to select "alert() function calls only" in CreateAlert popup. One alert per ticker is required.
Adding stop-loss, take-profit, trailing-stop, break-even or executing pending orders is also possible. These topics have been covered in other example posts.
This routing works for Forex, indices, stocks, crypto - anything your broker offers via their MetaTrader4 or 5.
Disclaimer: This concept is presented for educational purposes only. Profitable results of trading this strategy are not guaranteed even if the backtest suggests so. By no means this post can be considered a trading advice. You trade at your own risk.
If you are thinking to execute this particular strategy, make sure to find the instrument, settings and timeframe which you like most. You can do this by your own research only.
