Jerry J5 Dashboard & Buy Sell Strategy
----- Strategy
The strategy allows you to select from multiple moving averages and uses the concave function and the price being above or below a user defined EMA to provide buy and sell signals. You can select long trades, short trades, or both.
Concavity describes the direction of the curve and how it bends. And, just like a direction the concavity of curves can change and we call these changing points (technically they are inflection points). These changing points are used in conjunction with the stock price relationship to user defined EMAs and provide buy and sell signals when the trend is changing.
Moving Averages available in this strategy are the Exponential Moving Average (EMA), Simple Moving Average (SMA), Weighted Moving Average (WMA), Hull Moving Average (HMA), and Arnaud Legoux Moving Average (ALMA). Best results are typically with HMA and ALMA.
The indicator can be used for every type of market: indices, stocks, cryptocurrencies, currencies and others.
----- 27 Data Point Dashboard
Short Term Trend
Total Trades
Profit Factor
Win Rate %
Net Profit $, % & ROI
Buy & Hold Profit
Max Win $ & %
Max Loss $ & %
Avg Trade $, ROI%, & Bars
Avg Win $, ROI%, & Bars
Avg Loss $, ROI%, & Bars
Max Drawdown $ & %
Open Trade P&L $ & ROI%
For the dashboard you are able to set the target profit factor, win rate, net profit target ROI, winning trade target ROI, Strategy Start Date, and the Buy and Hold Start Date. The inputs are used to color dashboard labels making it easier to identify winning and losing trades based on your criteria.
Option Traders will likely appreciate the ROI% and number of bars being readily available. For example if the Average Winning Trade has a 5% ROI over 5 bars this can help you decide when to enter a trade and how long to stay in the trade.
Some of the label data is available in the TradingView Strategy Tester. However, I wanted a custom dashboard that was always visible on my chart and the J5 dashboard displays a total of 27 data points plus buy and sell signals .
Note: We calculate the ROI for Net Profit, Avg Trade, Avg Win, Avg Loss, and Open Trade based on the capital used for those trade(s). TradingView uses a different method and calculates the return percentage based on initial capital.
Indicators are not a magic pill and should be used to support trading decisions, not to make them for you. Past performance is not a guarantee of future returns. The results of individual stocks with any strategy do not constitute proof they will repeat in the future.
I hope you enjoy the J5.
DISCLAIMER: The information contained in our scripts/indicators/ideas does not constitute financial advice or a solicitation to buy or sell any securities of any type. Trading and investing in the stock market and cryptocurrencies involves substantial risk of loss and is not suitable for every investor. I’m NOT a financial adviser. All trading strategies are used at your own risk.
Please Use the link below for more information.
스크립트에서 "profit factor"에 대해 찾기
MA trading tool v1.0Background to the tool
The tool was built out of frustration. Having traded for many years with a reasonable level of success I was always frustrated that my trading never went up a level. The world of trading is filled with people having so much more success than me and this level of FOMO really bothered me and resulted in inconsistency and countless hours sitting in front of a screen, hoping for the best. I also became a little bit of an indicator junkie - was there a holy grail indicator out there for me? I always felt that as a retail trader I was behind the curve. I started to investigate how the major market participants trade and make money and I was astounded at the level of success that they get from creating strategies and sticking to it. The market is driven largely by a "black boxes" which, for us retail traders are outside of our ability to access. I wanted to build a tool that could give me a traders edge.
Another factor that has always bothered me was when reading investing books there is a general assumption that a standard entry, say 8/13 cross over, works on all stocks. However, it is not the case and it can be frustrating for a trader using a set up and not realizing that the set up was/is the problem, not the trader. This realization alone has made a huge impact on my trading. The big boxes that control the market know this already.
Also, a lot of indicators that are available don’t take advantage of the backtesting capability provided in Tradingview. It is fairly simple to find 8-9 trades where a set up worked and then fall into the trade of assuming that it cannot fail. Knowing which set ups work and how frequently it will print will change the way that you trade.
The goal with the tool is to identify setups that have worked in the past with a high degree of profitability, high profit factor and low drawdown and using the planning tool allows you to customize the setup to find exactly what you are looking for across any tradeable asset on TradingView.
Over the past 20 years I have realized the following:
1) Not all entries and signals work the same on all stocks and knowing the historical performance of a strategy is critical
2) Not having a plan in advance lowers your probability of success
3) Developing consistency in analysis is critical
4) Developing confidence in your own plan is more important than whose trades you try to copy
5) Having 30 indicators does not help you trade better - it leads to more frustration
So here is the product of these realisations:
1) The tool looks across the most common entry strategies (RMA / EMA/ SMA/ HMA/WMA cross on 5 dimensions of type and 5 common crossovers) and can be used on 19 different time frames giving you guidance on what the best set up is for the stock you are analysing
2) It incorporates volatility into the strategy – when stocks are trading outside of a predetermined volatility band, a trade will not be entered. This accommodates traders who tend to get shaken out of trades too early.
3) It looks at the impact of “buying the dip” – often a common strategy employed by many traders which now can be backtested and reviewed to see if it actually helped or hindered the trade.
4) It measures your trade plan against your R – what you are willing to risk – and calculates your target profit based on your R multiple
5) It provides a non repaint signal on your base strategy and provides you with signals to trade smaller or shorter signals within the bigger strategy.
There are some additional visual tools:
• Squeeze signals - I am a big fan of the TTM squeeze however the Squeeze by itself can be hard to trade. Seeing a squeeze fire long on a chart can add to trade confidence.
• Seeing zones of support and resistance rather than single lines can also give you some leeway in terms of not getting pushed out of a trade too soon.
The backtester is always reviewed on a 2 to 3 year period to get an understanding of win rate %, profit ratio and average duration of trade. As an option trader knowing that a high probability move is playing out allows me to make sure that I don’t undercut the time frame for the expiration of the option relative to the historical average duration of a trade. Backtesting on shorter times is unrealistic.
Key benefits
1) It will save you a ton of time. I don’t have to sit in front of a screen watching ticks each day. I can plan for an entry, set an alert for a trade and when the conditions are met the TradingView system sends me a message and I will go and confirm a trade, execute it, set my alerts for control and move on with my life.
2) It allows me to review trade ideas in a consistent manner using the best trade plan and set up for a stock.
3) It forces me to be patient and not panic (always a good thing). With an adjustable volatility feature I can modify the volatility band in the trade plan to accommodate choppy market conditions.
4) It looks at both sides of the market (long and short) and you can calculate the impact of being market neutral or having a directional bias.
I hope this tool helps you to achieve some degree of peace in your trading.
To get access to the tool, please contact the author.
© Investoz Monthly Overlook Strategy“The best time to get involved with cyclicals is when the economy is at its weakest, earnings are at their lowest, and public sentiment is at its bleakest.”
Peter Lynch
Before I begin, read the following.
Important! The script for this strategy is only intended to work correctly for the monthly time period and does not work for other time periods, as it is based on monthly data.
The strategy
This strategy is simple and based on monthly buy and sell. However, the strategy is only intended to go long and no short positions are therefore possible.
This strategy is perfectly suited for all indices, but also for cyclical companies and cyclical markets.
Monthly analysis
You do not need to use this strategy for a buying signal only. You can use this for a more comprehensive analysis of how return per month has looked historically. It's easy to analyze, by flipping through all the months of the year. You will see average return per month, but also the largest return and decrease for each individual month.
It is also possible to choose between which time periods you want to analyze. You can go all the way back to the year 1900.
This is necessary if you are going to analyze markets that have a long historical data to analyze.
How it works
1. Start by choosing which time period the analysis or strategy should apply between.
2. Choose which month the first and second buy will be valid from. Here it is important to know that depending on which month you choose, is meant a buy after the current month's closing.
Example: 1 = January. Buy will therefore be after January closes, ie the first trading day in February. It will be displayed as 1st trading day in February. If you only want one buy option, then choose the same month for both first and second buy.
3. Choose which month the first and second sell will be valid from. The same applies here as above.
4. Choose money management according to the criteria that suit you and press "OK".
Result
Based on the choices you have made, you will now get a result of how the strategy has performed over the given period. You will be able to read following data.
Data
Start Capital -the trading capital you have from the beginning.
Actual Capital -the current trading capital you have at the moment.
Growth -percentage growth over the total trading period.
Annual growth -average return for the chosen period.
Profit factor -the profit factor looks at the total gains and losses.
Payoff ratio -looks at the average profit and loss.
Expectancy per trade -the expected profit or loss of a single trade.
Expectancy in total based on number of trades -the expected profit or loss for all the trades made.
Expectancy ratio -the power of an edge. With this, you are always looking for a positive expectancy to show you that the trade is profitable.
Expectancy % -the expected return in percentage based on all the trades
Maximun drawdown -drawdown for a single trade based on current trading capital
Fractional Kelly Criterion -the optimal amount to invest based on the strategy and multiplying it by a certain fraction (%). This results in less volatile returns and a lower chance of the account balance hitting zero.
Kelly % -percentage of capital to be put into a single trade.
Risk of ruin -the chance that you will lose all the amount you typed in for "Maximum loss of portfolio".
Conclusion
It is important to know if you have an edge in your strategy and above all to understand risk. With the help of all this information, it will therefore be easier to adapt the risk to the strategy, not the other way around. This approach applies to all types of strategies. Be aware of the risk, first!
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DISCLAIMER
Any data and information is provided 'as is' solely for informational purposes, and is not intended for trading purposes or advice. Past performance is not indicative of future results.
Educate yourself on the risks associated with trading, and seek advice from an independent financial or tax advisor if you have any questions.
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MACD/RVSI/Stoch/RSI/EXP(Drawdown)I have been trying for several months to get a script to work on the 1min and this one gives some good backtest results. This script will also work on higher timeframes however, I've not extensively tested on higher timeframes. My aim was to get results on about 20 crypto coins then run the 1min bots in parallel looking for small frequent profits across all the coins. If you would like me to try and fit backtest results to any coin or pair on any timeframe please do get in touch anytime.
It's based on several indicators which are combined and then a newish way for the stop loss to implement based on an exponential rising which limits the time in each trade unless the price moves in the direction of the trade. The other useful feature is drawdown minimization which previously made all of my 1minute bot attempts non-practical due to differences between backtesting and actually running the bot(s) live.
Its possible at the top to paste in strategy comments which can be used through web-hooks for auto trading bots. Leaving these blank just defaults to the pre-programmed comments that provide some indication of why a trade was exited.
It is possible to select for Short and/or Long trades. Note however, that there are exponential markers on the charts for both long and short trades in any setting. I found that this way the bot worked well with regards to timing.
The next part of the user interface settings gets a bit tricky so try and use the sample parameters provided below. For example, select a crypto coin then try some of the options below until a reasonable backtest result in obtained (or select the best from the parameter groups tested) then move down the settings interface to optimise with the remaining settings.
So 'Use MACD/RVSI', 'RSI clause' and 'Use Stochastic' are set to true for the below sample settings (1min timeframe).
MACD/RVSI Confluence Resolution (1min, 2min, 5min, 10min, 1hour)
Timeframe RSI (1min, 2min, 3min, 15min, 1hour)
FastStoch, SlowStoch (1min, 45min: 5min, 30min: 1min, 1hour: 5min, 1hour)
Eg. for FTX:ETHPERP (MACD/RVSI Confluence Resolution=1hour, Timeframe RSI= 1min, FastStoch = 1min, SlowStoch = 45min)
Setting the timings is tricky - there is a lot going on. Have a look at the chart and select/deselect the options. The MACD/RVSI Confluence Resolution shows red and green vertical regions on the chart background. The Timeframe RSI colors the candle bodies red and green. These go green if the RSI crossed over 31% or red in the RSI crossed under 69%. The MACD/RVSI Confluence Resolution is explained in more detail in one of my other scripts. Then the Slow Stoch colors above and below the price action with red or green lines depending if on an uptrend or downtrend (approximately). Where there is also an up/down trend on the faster timeframe stoch there are vertical shaded fill regions between the slow stop above/below lines.
With all the above conditions selected to represent the data (looking at strategy backtest results whilst adjusting) there is a reasonable approximation to a credible trade.
So once an ok backtest result is obtained by selecting timing settings. Its ontot the Stop Ramp Settings. This is an exponential line which rises rapidly after a period of time thus exiting the trade or going upwards with the trade. It kind of limits the maximum time a trade will stay in position which forms part of the timing aspect of this bot. Look at the chart exponential red lines and adjust the settings, along with the backtest results to select a good timing.
Then its the Drawdown Catcher and the Take Profit Setting. Start with the drawdown catcher disabled i.e. set to zero. Put in a conservative Take Profit, for example if a Take Profit at 6% gives the best backtest results, go for 4% to account for differences between backtest results and actual live bot performance.
Then start to increase the Drawdown Catcher. This shades a lime region where the bot will not enter a trade. I found that with most trades using this bot, if the price action moved in the direction of the trade (long or short) at the onset - this gave most of the good results (high probability of positive trade). Also if a trade entered at the start price and when south, the accumilated drawdown from these failing trades made all previous 1min bot attempt non=profitable in practice (even with good backtest results). The exp timing and also this drawdown reduction strategies seem to be the thing which makes this approach credible.
Try to go for settings that give a very high change of positive trade. For example, an 85% profitable trades will probably provide say 55% positive trades in practice as its always highly possible to just fit the parameters to the exact position/trade timings - and in reality going forwards these don't play out the same. Also a Profit Factor of 2 is about the minimum I would accept - again this provides for example a Profit Factor of 1.2 in practive.
However all being said - I think its possible with this bot on the 1min across lots of coins - with regularly updating settings - to make profits. (Not financial advice)
Please do get in touch if you would like me to fit this bot to anytimeframe to any trade.
MoonFlag PhD
ELLIPSE: Bidirectional Swing Trading Strategy (Strategy Version)The eternal question that has occupied humanity since its early existence is what is the meaning of life and why am I here? On a daily basis this quest for meaning is distilled into a somewhat simpler question: What is the reason for getting up every morning?
For many of us, these thoughts arise even more as autumn arrives and it gets dark, bleak and cold outside. I guess it is easier to forget about the meaning of your life, while swimming on a sandy beach, enjoying a cocktail. Than you are living you life and you don’t need to rationalize it. Everything makes perfect sense!
In winter however, you need to get more “creative”. I, for example, would always try to change my perspective of things by doing something that makes my heart beat faster, like drinking a bottle of Heineken on a Friday evening or having endless conversations with my mates about stupid things, or kicking a ball against BALLONTHEROOF 7 on a Saturday morning. During the week, I would take out my frustrations on the fitness equipment at the local gym.
But what if all of this is canceled by CORONA? All that’s left is to work for the boss and run your 10km lap twice a week. The question is, what do you do now, with this huge amount of ”free” time than any old person would give anything for. When you are young time is never ending, when you are older it is never enough. Time has reached a different dimension in these days.
However, you can still do 2 things. You can slowly let the walls come to you and give up or you can actually do something useful with your time and find something that you are good at.
For us this choice was easy. After the success of our positioning trading strategy the MATRIX, at the time of the corona lockdown, we started making a swing trading strategy for the 4H timeframe, called ELLIPSE. We have included all feedback and any improvements we received about the positioning strategy and integrated it into the 4H script.
The main requirements of the script that we had set ourselves were:
Bidirectional
Low max drawdown
High profit factor
Works on all main crypto coins
By fully focusing ourselves on the script over the past few months, I can’t help but (unhumbly) say that we have not only succeeded in our mission, but that we have absolutely surpassed ourselves!
The only bright spot in this heavy corona time is, if a drug becomes available, there is extra money in the bank!
***The script is invite-only, message us to get script access***
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User Guidelines:
The trading strategy was designed and optimized for trading cryptocurrencies only; furthermore it works best on established cryptocurrencies that have a clear historical trend such as:
BTCUSD
ETHUSD
LTCUSD
XRPUSD
ADAUSD
The trading strategy is based on swing trading methodology. The script must therefore be used on 4h candles only .
Use USD trading pairs only (e.g. use ETHUSD instead of the ETHBTC) since the individual trend is captured more effectively and therefore gives better results.
The trading strategy is bidirectional , both long and short entries are generated.
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Indicators used in this strategy:
Ichimoku Cloud ; acts as the leading indicator.
Volume ; without strong volume , a market move is not valid.
MACD and Vortex ; both being used as confirmation indicators.
Choppiness index ; avoids trading in choppy markets.
Simple and Exponential Moving Averages ; prevents trading against the trend.
The trading strategy is easy to use, bidirectional, trend based and without repainting, meaning once a signal has been made it is permanent and that no future data is used in the decision making. It detects the trend and filters out market noise based on more than 10 technical indicators. ONLY when all indicators align with each other the algorithm prints a LONG or SHORT signal. The trading strategy provides high probability trading signals and minimizes risk! This script aims to capture the profit from short to medium trending moves and by doing so filters out non-substantial trends and avoids the associated risks with these trades.
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Features:
NO Repaint once candle is closed.
Stop loss feature ; set your own stop loss to manage your risks.
Customizable Display for the Ichimoku cloud indicator display.
Bidirectional ; both long and short trading positions can be enabled.
Full backtest feature ; Easily generate your own backtest results for each asset (Strategy Version Script).
Alerts ; Get notified via email / pop-up / sms / app once a signal is given! (Alert Version Script).
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Backtest results
Below are the back test results. Only well established cryptocurrencies are displayed with a clear historical trend:
Long and short trading positions,
Signal to signal trading (no multiple orders),
Initial Capital: 10 000 USD,
Order size: 10% of equity per trade,
commission fee 0.1%, period: start of chart,
Exchange-----Asset------Timeframe---Percent Profitable----Profit Factor---Total Trades----Max Drawdown----------Net Profit------
Bínance------BTCUSDT------4H-----------------54.4---------------5.32-----------------57----------------1.58%------------40.34%-(4034 USD)
Bínance------ETHUSD-------4H-----------------50.9---------------5.01---------------- 57----------------2.96%------------54.93%-(5493 USD)
Bínance------LTCUSD--------4H-----------------61.0---------------5.08-----------------59----------------2.09%------------57.06%-(5706 USD)
Bínance------XRPUSD-------4H-----------------43.13--------------3.52-----------------51----------------2.42%------------43.13%-(4313 USD)
Bínance------ADAUSD-------4H-----------------57.5---------------3.36-----------------47----------------3.46%------------40.82%-(4082 USD)
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Reminder: Use this trading strategy at your own risk and trade responsibly. We are not responsible for any financial loss using this strategy.
***The script is invite-only, message us to get script access***
Dompeet Pompeet (Breakout bot)Dompeet Pompeet is my first attempt at a viable swingtrading algo.
It uses volatility and some trend analysis to enter trade when the market is about to breakout or break down. Having a trailing stop locks in profits and prevents runaway losses for low drawdown and 2:1 profit factor.
Settings to use:
BTCUSD or XBTUSD
4hr Timeframe or 2hr or 1hr (not shorter)
Bars window: 13, 16 or 20 bars
Moving average settings: 100/10 EMA to confirm trend
Trade the Trend - check on to only take trades long in a confirmed uptrend (vice versa short), otherwise it will attempt to buy and sell counter trend, which increases profits but also increases loss rate.
Trailing stop, values from 2-5% give the best results.
Take with a pinch of salt, there are some bugs in pine script which are difficult to track down but overall I'm pleased with the idea.
TrendMaster, v. 5.1TrendMaster (ver 5.0, a.k.a. "Mr. T") is a trend-anticipating system which shows eye-popping results. Rather than a simple "trend-rider" which continually lags, TrendMaster uses pattern recognition to anticipate trend changes. It hunts for turns in market cycles using a proprietary decision matrix of 27 "trade triggers" (variants of classic technical indicators). In backtesting the annualized return (July 2017-July2018) of the top 100 largest capitalized equities, not only did its results beat the indexes, but it outstripped a buy-and-hold strategy by a whopping 700%: i.e., while the 100 largest equities had an average annual yield of 17%, the strategy produced a 129% return across the entire range of stocks.* Even more staggering results can be seen when using the system in strongly-trending markets like cryptos. (See, for instance, the particularly stunning results for Bitcoin Cash in the screen shot at the end). [ ]
The following charts give you some sample tests of best performing (top chart) and worst performing (bottom chart) equities, ETFs, and cryptos (July 2017-July2018 time range).
TrendMaster / "Mr T" 5.1, Example Annualized returns
>>> Higher Performing Instruments (best performing interval between 15min - 1 day) <<<
Symbol Description Interval % Return Trades % Profitable Profit Factor Max Drawdown Sharpe Ratio Buy & Hold
DUST 3x Short Gold miners 30 min 3050% 421 43% 1.5 ( -17% ) 1.8 ( - 27 % )
NUGT 3x Long Gold miners 15 min 3300% 423 44 1.2 ( -11% ) 1.2 ( - 10 % )
APPL Apple, Inc 1 hour 118% 354 46 2.2 ( -6% ) 1.3 32%
GOOG Alphabet, Inc 30 min 128% 660 44 1.9 ( -5% ) 1 27%
BABA Alibaba Group 30 min 389% 642 46 2.3 ( -3% ) 1.6 37%
BTC/USDT Bitcoin 1 hour 1000000% 1573 53 3.1 ( -8% ) 1 57%
ETH/USDT Ethereum 1 hour 13000000% 1533 55 3.2 ( -12% ) 1.2 56%
LTC/ USDT Litecoin 30 min 5000000% 1946 54 2.8 ( -12% ) 1.2 ( - 71% )
>>> Lower Performing Instruments and/or Lower Performing Market Settings (worst performing interval between 15 min - 1 day) <<<
Symbol Description Interval % Return Trades % Profitable Profit Factor Max Drawdown Sharpe Ratio Buy & Hold
PSQ Short Nasdaq ETF 5 min ( - 14% ) 1894 28% ( - 0.9 % ) ( -20% ) ( -0.3 ) ( - 20 % )
SH Short S&P ETF 1 day 20% 35 51% 2.8 ( -7% ) 0.5 ( - 12 % )
MSFT Microsoft 1 hour 37% 249 37% 1.4 ( -4% ) 0.5 45%
AZN Astrazeneca, PLC 1 day 22% 32 50% 1.9 ( -6% ) 0.3 ( - 4 % )
MUFG Mitsubishi UFJ 1 day 296% 1255 39% ( - 0.6 ) ( -27% ) ( - 0.3 ) ( - 16 % )
BTC/USDT Bitcoin 1 min ( - 33 % ) 1349 25% ( - 0.5 ) ( - 33% ) ( - 1.1 ) 5%
ETH/USDT Ethereum 1 min ( - 40 % ) 1595 24% ( - 0.6 ) ( -40% ) ( - 1.1 ) 6%
LTC/USDT Litecoin 1 day 123 14 57% 4.8 ( - 5% ) 1.1 ( - 48% )
Note, of course, that any trend-anticipating bot has limitations. The Lower Performing results above show that because the system seeks gains in trends, it can underperform in choppy, aimless markets. Similarly, very short time frames (i.e. 1 - 5 minutes) can provide too many gaps and too few runs for it to reliably track. Backtesting is therefore essential to make sure this system is suitable for your (a) market, (b) time frame, and (c) trading mindset. On the other hand, a broad range of data show that almost any trending market, in almost any time frame, can be very productive.
Contact us if you would like to experience this bot.
I_Heikin Ashi CandleWhen apply a strategy to Heikin Ashi Candle chart (HA candle), the strategy will use the open/close/high/low values of the Heikin Ashi candle to calculate the Profit and Loss, hence also affecting the Percent Profitable, Profit Factor, etc., often resulting a unrealistic high Percent Profitable and Profit Factor, which is misleading. But if you want to use the HA candle's values to calculate your indicator / strategy, but pass the normal candle's open/close/high/low values to the strategy to calculate the Profit / Loss, you can do this:
1) set up the main chart to be a normal candle chart
2) use this indicator script to plot a secondary window with indicator looks exactly like a HA-chart
3) to use the HA-candle's open/close/high/low value to calculate whatever indicator you want (you may need to create a separate script if you want to plot this indicator in a separate indicator window)
[AutoView] MovingAvg Cross - Video AttachedThere is nothing special or spectacular about this script. It's your standard Moving Average Cross Strategy. It is actually a built in script everyone has access to already. I only changed some of the settings and flipped the orders.
The reason I actually published this, is because people have been asking me what the best way to find the best settings for a strategy. So I made a YouTube video showing people how I personally do it. I took this built in strategy and within 5 minutes took it from a net profit loss and profit factor of 0.5 to a net profit win with a profit factor of 3-5.
Of course this is only on the 1 minute candles, so forward testing the strategy is a must as I do not recommend straight up taking this and trading it.
You can watch the video here:
www.youtube.com
Hope this helps everyone speed up their back testing and fine tuning their strategies.
CM Stochastic POP Method 2-Jake Bernstein_V1Yesterday Jake Bernstein authorized me to post his updated results with the Stochastic Pop Trading System he developed many years ago.
You can take a look at the Original System with Updated Settings at
This indicator is a different set of rules Jake mentioned in the PDF he allowed me to post.
To view the PDF use this link:
dl.dropboxusercontent.com
Today we’re releasing the version described in the PDF that uses the StochK values of 55, 50, and 45. The rules are discussed in the PDF but here is a simple breakdown:
Enter Long when StochK is below 50 and Crosses Above 55
Exit Long on Cross Below 55
Enter Short when StochK is Above 50 and crosses Below 45
Exit Short on Cross Above 45
Two Important Items to understand about this method:
To code the rules Precisely we need a function that will be available when Strategy Capabilities are released on TradingView.
There is one of Jakes Profit Maximizing Strategies that needs to be integrated with this code…which again we need the Strategy based Function that will be coming soon.
To Compare this system to the Stochastic Pop Method 1 System shown yesterday at I used the same Symbol and dates for you to compare…but remember to give this Method 2 System a Fair Look/Evaluation…we need the Soon To Be Released…TradingView Strategy Capabilities.
BackTesting Results Example: EUR-USD Daily Chart Since 01/01/2005
Strategy 1 – Stochastic Pop Method 2 System:
Go Long When Stochasticis below 50 and Crosses Above 55. Go Short When Stochastic is above 50 and Crosses Below 45. Exit Long/Short When Stochastic has a Reverse Cross of Entry Value.
Results:
Total Trades = 151
Profit = 40,758 Pips
Win% = 37.1%
Profit Factor = 1.26
Avg Trade = 270 Pips Profit
***Most Consecutive Wins = 4 ... Most Consecutive Losses = 7
Strategy 2:
Rules - Proprietary Optimization Jake Will Teach. Only Added 1 Additional Exit Rule.
Results:
Total Trades = 151
Profit = 60.305 Pips
Win% = 37.1%
Profit Factor = 1.38
Avg Trade = 399 Pips Profit
***Most Consecutive Wins = 4 ... Most Consecutive Losses = 7
Indicator Includes:
-Ability to Color Candles (CheckBox In Inputs Tab)
Green = Long Trade
Blue = No Trade
Red = Short Trade
Jake Bernstein will be a contributor on TradingView when Backtesting/Strategies are released. Jake is one of the Top Trading System Developers in the world with 45+ years experience and he is going to teach TradingView.com’s community how to create Trading Systems and how to Optimize the correct way.
Link To PDF:
dl.dropboxusercontent.com
Link to Original Version of Indicator with Updated Settings.
CM Stochastic POP Method 1 - Jake Bernstein_V1A good friend ucsgears recently published a Stochastic Pop Indicator designed by Jake Bernstein with a modified version he found.
I spoke to Jake this morning and asked if he had any updates to his Stochastic POP Trading Method. Attached is a PDF Jake published a while back (Please read for basic rules, which also Includes a New Method). I will release the Additional Method Tomorrow.
Jake asked me to share that he has Updated this Method Recently. Now across all symbols he has found the Stochastic Values of 60 and 30 to be the most profitable. NOTE - This can be Significantly Optimized for certain Symbols/Markets.
Jake Bernstein will be a contributor on TradingView when Backtesting/Strategies are released. Jake is one of the Top Trading System Developers in the world with 45+ years experience and he is going to teach how to create Trading Systems and how to Optimize the correct way.
Below are a few Strategy Results....Soon You Will Be Able To Find Results Like This Yourself on TradingView.com
BackTesting Results Example: EUR-USD Daily Chart Since 01/01/2005
Strategy 1:
Go Long When Stochastic Crosses Above 60. Go Short When Stochastic Crosses Below 30. Exit Long/Short When Stochastic has a Reverse Cross of Entry Value.
Results:
Total Trades = 164
Profit = 50, 126 Pips
Win% = 38.4%
Profit Factor = 1.35
Avg Trade = 306 Pips Profit
***Most Consecutive Wins = 3 ... Most Consecutive Losses = 6
Strategy 2:
Rules - Proprietary Optimization Jake Will Teach. Only Added 1 Additional Exit Rule.
Results:
Total Trades = 164
Profit = 62, 876 Pips!!!
Win% = 38.4%
Profit Factor = 1.44
Avg Trade = 383 Pips Profit
***Most Consecutive Wins = 3 ... Most Consecutive Losses = 6
Strategy 3:
Rules - Proprietary Optimization Jake Will Teach. Only added 1 Additional Exit Rule.
Results:
Winning Percent Increases to 72.6%!!! , Same Amount of Trades.
***Most Consecutive Wins = 21 ...Most Consecutive Losses = 4
Indicator Includes:
-Ability to Color Candles (CheckBox In Inputs Tab)
Green = Long Trade
Blue = No Trade
Red = Short Trade
-Color Coded Stochastic Line based on being Above/Below or In Between Entry Lines.
Link To Jakes PDF with Rules
dl.dropboxusercontent.com
Autoback Grid Lab [trade_lexx]Autoback Grid Lab: Your personal laboratory for optimizing grid strategies.
Introduction
First of all, it is important to understand that Autoback Grid Lab is a powerful professional tool for backtesting and optimization, created specifically for traders using both grid strategies and regular take profit with stop loss.
The main purpose of this script is to save you weeks and months of manual testing and parameter selection. Instead of manually testing one combination of settings after another, Autoback Grid Lab automatically tests thousands of unique strategies on historical data, providing you with a comprehensive report on the most profitable and, more importantly, sustainable ones.
If you want to find mathematically sound, most effective settings for your grid strategy on a specific asset and timeframe, then this tool was created for you.
Key Features
My tool has functionality that transforms the process of finding the perfect strategy from a routine into an exciting exploration.
🧪 Mass testing of thousands of combinations
The script is able to systematically generate and run a huge number of unique combinations of parameters through the built-in simulator. You set the ranges, and the indicator does all the work, testing all possible options for the following grid settings:
* Number of safety orders (SO Count)
* Grid step (SO Step)
* Step Multiplier (SO Multiplier) for building nonlinear grids
* Martingale for controlling the volume of subsequent orders
* Take Profit (%)
* Stop Loss (%), with the possibility of calculating both from the entry point and from the dynamic breakeven line
* The volume of the base order (Volume BO) as a percentage of the deposit
🏆 Unique `FinalScore` rating system
Sorting strategies by net profit alone is a direct path to self—deception and choosing strategies that are "tailored" to history and will inevitably fail in real trading. To solve this problem, we have developed FinalScore, a comprehensive assessment of the sustainability and quality of the strategy.
How does it work?
FinalScore analyzes each combination not one by one, but by nine key performance metrics at once, including Net Profit, Drawdown, Profit Factor, WinRate, Sharpe coefficients, Sortino, Squid and Omega. Each of these indicators is normalized, that is, reduced to a single scale. Then, to test the strategy for strength, the system performs 30 iterations, each time assigning random weights to these 9 metrics. A strategy gets a high FinalScore only if it shows consistently high results under different evaluation criteria. This proves her reliability and reduces the likelihood that her success was an accident.
📈 Realistic backtesting engine
The test results are meaningless if they do not take into account the actual trading conditions. Our simulator simulates real trading as accurately as possible, taking into account:
* Leverage: Calculation of the required margin to open and hold positions.
* Commission: A percentage commission is charged each time an order is opened and closed.
* Slippage: The order execution price is adjusted by a set percentage to simulate real market conditions.
* Liquidation model: This is one of the most important functions. The script continuously monitors the equity of the account (capital + unrealized P&L). If equity falls below the level of the supporting margin (calculated from the current value of the position), the simulator forcibly closes the position, as it would happen on a real exchange. This eliminates unrealistic scenarios where the strategy survives after a huge drawdown.
🔌 Integration with external signals
The indicator operates in two modes:
1. `No Signal': Standard mode. The trading cycle starts immediately as soon as the previous one has been closed. Ideal for testing the "pure" mechanics of the grid.
2. `External Signal`: In this mode, a new trading cycle will start only when a signal is received from an external source. You can connect any other indicator (such as the RSI, MACD, or your own strategy) to the script and use it as a trigger to log in. This allows you to combine the power of a grid strategy with your own entry points.
📊 Interactive and informative results panel
Upon completion of the calculations, a detailed table with the TOP N best strategies appears on the screen, sorted according to your chosen criterion. For each strategy in the rating, you will see not only the key metrics (Profit, Drawdown, duration of transactions), but also all the parameters that led to this result. You can immediately take these settings and apply them in your trading.
Application Options: How To Solve Your Problems
Autoback Grid Lab is a flexible tool that can be adapted to solve various tasks, from complete grid optimization to fine—tuning existing strategies. Here are some key scenarios for its use:
1. Complete Optimization Of The Grid Strategy
This is the basic and most powerful mode of use. You can find the most efficient grid configuration for any asset from scratch.
* How to use: Set wide ranges for all key grid parameters ('SO Count`, SO Step, SO Multiplier, Martingale, TP, etc.).
* In the `No Signal` mode: You will find the most stable grid configuration that works as an independent, constantly active strategy, regardless of which-or entrance indicators.
* In the `External Signal` mode: You can connect your favorite indicator for input (for example, RSI, MACD or a complex author's script) and find the optimal grid parameters that best complement your input signals. This allows you to turn a simple signaling strategy into a full-fledged grid system.
2. Selecting the Optimal Take Profit and Stop Loss for Your Strategy
Do you already have an entry strategy, but you are not sure where it is best to put Take Profit and Stop Loss? Autoback Grid Lab can solve this problem as well.
* How to use:
1. Disable optimization of all grid parameters (uncheck SO Count, SO Step, Martingale, etc.). Set the Min value for SO Count to 0.
2. Set the ranges for iteration only for 'Take Profit` and `Stop Loss'.
3. Turn on the External Signal mode and connect your indicator with input signals.
* Result: The script will run your historical entry signals with hundreds of different TP and SL combinations and show you which stop order levels bring maximum profit with minimal risk specifically for your entry points.
3. Building a Secure Network with Risk Management
Many traders are afraid of grid strategies because of the risk of large drawdowns. With the help of the optimizer, you can purposefully find the parameters for such a grid, which includes mandatory risk management through Stop Loss.
* How to use: Enable and set the range for Stop Loss, along with other grid parameters. Don't forget to test both types of SL calculations (`From entry point` and `From breakeven line`) to determine which one works more efficiently.
* Result: You will find balanced strategies in which the grid parameters (number of orders, martingale) and the Stop Loss level are selected in such a way as to maximize profits without going beyond the acceptable risk level for you.
How To Use The Indicator (Step-By-Step Guide)
Working with the Autoback Grid Lab is a sequential process consisting of four main steps: from initial setup to analysis of the finished results. Follow this guide to get the most out of the tool.
Step 1: Initial Setup
1. Add the indicator to the chart of your chosen asset and timeframe.
2. Open the script settings. The first thing you should pay attention to is the ⚙️ Optimization Settings ⚙️ group.
3. Set the `Bars Count'. This parameter determines how much historical data will be used for testing.
* Important: The more bars you specify, the more statistically reliable the backtest results will be. We recommend using the maximum available value (25,000) to test strategies at different market phases.
* Consider: The indicator performs all calculations on the last historical bar. After applying the TradingView settings, it will take some time to load all the specified bars. The results table will appear only after the data is fully loaded. Don't worry if it doesn't appear instantly. And if an error occurs, simply switch the number of combinations to 990 and back to 1000 until the table appears.
Step 2: Optimization Configuration
At this stage, you define the "universe" of parameters that our algorithm will explore.
1. Set the search ranges (🛠 Optimization Parameters 🛠 group).
For each grid parameter that you want to optimize (for example, SO Count or `Take Profit'), you must specify three values:
* Min: The minimum value of the range.
* Max: The maximum value of the range.
* Step: The step with which the values from Min to Max will be traversed.
*Example:* If you set Min=5, Max=10, and Step=1 for SO Count, the script will test strategies with 5, 6, 7, 8, 9, and 10 safety orders.
* Tip for users: To get the first results quickly, start with a larger step (for example, TP from 0.5% to 2.5% in 0.5 increments instead of 0.1). After you identify the most promising areas, you can perform a deeper analysis by expanding the ranges around these values.
2. Set Up Money Management (Group `💰 Money Management Settings 💰`).
Fill in these fields with the values that best match your actual trading conditions. This is critically important for obtaining reliable results.
* Capital: Your initial deposit.
* Leverage: Leverage.
* Commission (%): Your trading commission as a percentage.
* Slippage (%): Expected slippage.
* Liquidation Level (%): The level of the supporting margin (MMR in %). For example, for Binance Futures, this value is usually between 0.4% and 2.5%, depending on the asset and position size. Specify this value for your exchange.
3. Select the Sorting Criterion and the Direction (Group `⚙️ Optimization Settings ⚙️').
* `Sort by': Specify the main criteria by which the best strategies will be selected and sorted. I strongly recommend using finalScore to find the most balanced and sustainable strategies.
* `Direction': Choose which trades to test: Long, Short or Both.
Step 3: Start Testing and Work with "Parts"
The total number of unique combinations generated based on your ranges can reach tens of millions. TradingView has technical limitations on the number of calculations that the script can perform at a time. To get around this, I implemented a "Parts" system.
1. What are `Part` and `Combinations in Part'?
* `Combinations in Part': This is the number of backtests that the script performs in one run (1000 by default).
* `Part`: This is the number of the "portion" of combinations that you want to test.
2. How does it work in practice?
* After you have everything set up, leave Part:1 and wait for the results table to appear. You will see the TOP N best strategies from the first thousand tested.
* Analyze them. Then, to check the next thousand combinations, just change the Part to 2 in the settings and click OK. The script will run a test for the next batch.
* Repeat this process by increasing the Part number (`3`, 4, 5...), until you reach the last available part.
* Where can I see the total number of parts? In the information row below the results table, you will find Total parts. This will help you figure out how many more tests are left to run.
Step 4: Analyze the Results in the Table
The results table is your main decision—making tool. It displays the best strategies found, sorted by the criteria you have chosen.
1. Study the performance metrics:
* Rating: Position in the rating.
* Profit %: Net profit as a percentage of the initial capital.
* Drawdown%: The maximum drawdown of the deposit for the entire test period.
* Max Length: The maximum duration of one transaction in days, hours and minutes.
* Trades: The total number of completed trades.
2. Examine the winning parameters:
* To the right of the performance metrics are columns showing the exact settings that led to this result ('SO Count`, SO Step, TP (%), etc.).
3. How to choose the best strategy?
* Don't chase after the maximum profit! The strategy with the highest profit often has the highest drawdown, which makes it extremely risky.
* Seek a balance. The ideal strategy is a compromise between high profitability, low drawdown (Drawdown) and the maximum length of trades acceptable to you (Max Length).
* finalScore was created to find this balance. Trust him — he often highlights not the most profitable, but the most stable and reliable options.
Detailed Description Of The Settings
This section serves as a complete reference for each parameter available in the script settings. The parameters are grouped in the same way as in the indicator interface for your convenience.
Group: ⚙️ Optimization Settings ⚙️
The main parameters governing the testing process are collected here.
* `Enable Optimizer': The main switch. Activates or deactivates all backtesting functionality.
* `Direction': Determines which way trades will be opened during the simulation.
* Long: Shopping only.
* Short: Sales only.
* Both: Testing in both directions. Important: This mode only works in conjunction with an External Signal, as the script needs an external signal to determine the direction for each specific transaction.
* `Signal Mode`: Controls the conditions for starting a new trading cycle (opening a base order).
* No Signal: A new cycle starts immediately after the previous one is completed. This mode is used to test "pure" grid mechanics without reference to market conditions.
* External Signal: A new cycle begins only when a signal is received from an external indicator connected via the Signal field.
* `Signal': A field for connecting an external signal source (works only in the `External Signal` mode). You can select any other indicator on the chart.
* For Long** trades, the signal is considered received if the value of the external indicator ** is greater than 0.
* For Short** trades, the signal is considered received if the value of the external indicator ** is less than 0.
* `Bars Count': Sets the depth of the history in the bars for the backtest. The maximum value (25000) provides the most reliable results.
* `Sort by`: A key criterion for selecting and ranking the best strategies in the final table.
* FinalScore: Recommended mode. A comprehensive assessment that takes into account 9 metrics to find the most balanced and sustainable strategies.
* Profit: Sort by net profit.
* Drawdown: Sort by minimum drawdown.
* Max Length: Sort by the minimum length of the longest transaction.
* `Combinations Count': Indicates how many of the best strategies (from 1 to 50) will be displayed in the results table.
* `Close last trade`: If this option is enabled, any active trade will be forcibly closed at the closing price of the last historical bar. For grid strategies, it is recommended to always enable this option in order to get the correct calculation of the final profit and eliminate grid strategies that have been stuck for a long time.
Group: 💰 Money Management Settings 💰
The parameters in this group determine the financial conditions of the simulation. Specify values that are as close as possible to your actual values in order to get reliable results.
* `Capital': The initial deposit amount for the simulation.
* `Leverage`: The leverage used to calculate the margin.
* `Slippage` (%): Simulates the difference between the expected and actual order execution price. The specified percentage will be applied to each transaction.
* `Commission` (%): The trading commission of your exchange as a percentage. It is charged at the execution of each order (both at opening and closing).
* `Liquidation Level' (%): Maintenance Margin Ratio. This is a critical parameter for a realistic test. Liquidation in the simulator occurs if the Equity of the account (Capital + Unrealized P&L) falls below the level of the supporting margin.
Group: 🛠 Optimization Parameters 🛠
This is the "heart" of the optimizer, where you set ranges for iterating through the grid parameters.
* `Part`: The portion number of the combinations to be tested. Start with 1, and then increment (`2`, 3, ...) sequentially to check all generated strategies.
* `Combinations in Part': The number of backtests performed at a time (in one "Part"). Increasing the value may speed up the process, but it may cause the script to error due to platform limitations. If an error occurs, it is recommended to switch to the step below and back.
Three fields are available for each of the following parameters (`SO Count`, SO Step, SO Multiplier, etc.):
* `Min`: Minimum value for testing.
* `Max': The maximum value for testing.
* `Step`: The step with which the values in the range from Min to Max will be iterated over.
There is also a checkbox for each parameter. If it is enabled, the parameter will be optimized in the specified range. If disabled, only one value specified in the Min field will be used for all tests.
* 'Stop Loss': In addition to the standard settings Min, Max, Step, it has an additional parameter:
* `Type`: Defines how the stop loss price is calculated.
* From entry point: The SL level is calculated once from the entry price (base order price).
* From breakeven line: The SL level is dynamically recalculated from the average position price after each new safety order is executed.
Group: ⚡️Filters⚡️
Filters allow you to filter out those results from the final table that do not meet your minimum requirements.
For each filter (`Max Profit`, Min Drawdown, `Min Trade Length`), you can:
1. Turn it on or off using the checkbox.
2. Select the comparison condition: Greater (More) or Less (Less).
3. Set a threshold value.
*Example:* If you set Less and 20 for the Min Drawdown filter, only those strategies with a maximum drawdown of less than 20% will be included in the final table.
Group: 🎨 Visual Settings 🎨
Here you can customize the appearance of the results table.
* `Position': Selects the position of the table on the screen (for example, Bottom Left — bottom left).
* `Font Size': The size of the text in the table.
* `Header Background / Data Background`: Background colors for the header and data cells.
* `Header Font Color / Data Font Color`: Text colors for the header and data cells.
Important Notes and Limitations
So that you can use the Autoback Grid Lab as efficiently and consciously as possible, please familiarize yourself with the following key features of its work.
1. It is a Tool for Analysis, not for Signals
It is extremely important to understand that this script does not generate trading signals in real time. Its sole purpose is to conduct in—depth research (**backtesting**) on historical data.
* The results you see in the table are a report on how a particular strategy would have worked in the past.
* The script does not provide alerts and does not draw entry/exit points on the chart for the current market situation.
* Your task is to take the best sets of parameters found during optimization and use them in your real trading, for example, when setting up a trading bot or in a manual trading system.
2. Features Of Calculations (This is not a "Repainting")
You will notice that the results table appears and is updated only once — when all historical bars on the chart are loaded. It does not change in real time with each tick of the price.
This is correct and intentional behavior.:
* To test thousands, and sometimes millions of combinations, the script needs to perform a huge amount of calculations. In the Pine Script™ environment, it is technically possible to do this only once, at the very last bar in history.
* The script does not show false historical signals, which then disappear or change. It provides a static report on the results of the simulation, which remains unchanged for a specific historical period.
3. Past Results do not Guarantee Future Results.
This is the golden rule of trading, and it fully applies to the results of backtesting. Successful strategy performance in the past is not a guarantee that it will be as profitable in the future. Market conditions, volatility and trends are constantly changing.
My tool, especially when sorting by finalScore, is aimed at finding statistically stable and reliable strategies to increase the likelihood of their success in the future. However, it is a tool for managing probabilities, not a crystal ball for predicting the future. Always use proper risk management.
4. Dependence on the Quality and Depth of the Story
The reliability of the results directly depends on the quantity and quality of the historical data on which the test was conducted.
* Always strive to use the maximum number of bars available (`Bars Count: 25,000`) so that your strategy is tested on different market cycles (rise, fall, flat).
* The results obtained on data for one month may differ dramatically from the results obtained on data for two years. The longer the testing period, the higher the confidence in the parameters found.
Conclusion
The Autoback Grid Lab is your personal research laboratory, designed to replace intuitive guesses and endless manual selection of settings with a systematic, data—driven approach. Experiment with different assets, timeframes, and settings ranges to find the unique combinations that best suit your trading style.
HorizonSigma Pro [CHE]HorizonSigma Pro
Disclaimer
Not every timeframe will yield good results . Very short charts are dominated by microstructure noise, spreads, and slippage; signals can flip and the tradable edge shrinks after costs. Very high timeframes adapt more slowly, provide fewer samples, and can lag regime shifts. When you change timeframe, you also change the ratios between horizon, lookbacks, and correlation windows—what works on M5 won’t automatically hold on H1 or D1. Liquidity, session effects (overnight gaps, news bursts), and volatility do not scale linearly with time. Always validate per symbol and timeframe, then retune horizon, z-length, correlation window, and either the neutral band or the z-threshold. On fast charts, “components” mode adapts quicker; on slower charts, “super” reduces noise. Keep prior-shift and calibration enabled, monitor Hit Rate with its confidence interval and the Brier score, and execute only on confirmed (closed-bar) values.
For example, what do “UP 61%” and “DOWN 21%” mean?
“UP 61%” is the model’s estimated probability that the close will be higher after your selected horizon—directional probability, not a price target or profit guarantee. “DOWN 21%” still reports the probability of up; here it’s 21%, which implies 79% for down (a short bias). The label switches to “DOWN” because the probability falls below your short threshold. With a neutral-band policy, for example ±7%, signals are: Long above 57%, Short below 43%, Neutral in between. In z-score mode, fixed z-cutoffs drive the call instead of percentages. The arrow length on the chart is an ATR-scaled projection to visualize reach; treat it as guidance, not a promise.
Part 1 — Scientific description
Objective.
The indicator estimates the probability that price will be higher after a user-defined horizon (a chosen number of bars) and emits long, short, or neutral decisions under explicit thresholds. It combines multi‑feature, z‑normalized inputs, adaptive correlation‑based weighting, a prior‑shifted sigmoid mapping, optional rolling probability calibration, and repaint‑safe confirmation. It also visualizes an ATR‑scaled forward projection and prints a compact statistics panel.
Data and labeling.
For each bar, the target label is whether price increased over the past chosen horizon. Learning is deliberately backward‑looking to avoid look‑ahead: features are associated with outcomes that are only known after that horizon has elapsed.
Feature engineering.
The feature set includes momentum, RSI, stochastic %K, MACD histogram slope, a normalized EMA(20/50) trend spread, ATR as a share of price, Bollinger Band width, and volume normalized by its moving average. All features are standardized over rolling windows. A compressed “super‑feature” is available that aggregates core trend and momentum components while penalizing excessive width (volatility). Users can switch between a “components” mode (weighted sum of individual features) and a “super” mode (single compressed driver).
Weighting and learning.
Weights are the rolling correlations between features (evaluated one horizon ago) and realized directional outcomes, smoothed by an EMA and optionally clamped to a bounded range to stabilize outliers. This produces an adaptive, regime‑aware weighting without explicit machine‑learning libraries.
Scoring and probability mapping.
The raw score is either the weighted component sum or the weighted super‑feature. The score is standardized again and passed through a sigmoid whose steepness is user‑controlled. A “prior shift” moves the sigmoid’s midpoint to the current base rate of up moves, estimated over the evaluation window, so that probabilities remain well‑calibrated when markets drift bullish or bearish. Probabilities and standardized scores are EMA‑smoothed for stability.
Decision policy.
Two modes are supported:
- Neutral band: go long if the probability is above one half plus a user‑set band; go short if it is below one half minus that band; otherwise stay neutral.
- Z‑score thresholds: use symmetric positive/negative cutoffs on the standardized score to trigger long/short.
Repaint protection.
All values used for decisions can be locked to confirmed (closed) bars. Intrabar updates are available as a preview, but confirmed values drive evaluation and stats.
Calibration.
An optional rolling linear calibration maps past confirmed probabilities to realized outcomes over the evaluation window. The mapping is clipped to the unit interval and can be injected back into the decision logic if desired. This improves reliability (probabilities that “mean what they say”) without necessarily improving raw separability.
Evaluation metrics.
The table reports: hit rate on signaled bars; a Wilson confidence interval for that hit rate at a chosen confidence level; Brier score as a measure of probability accuracy; counts of long/short trades; average realized return by side; profit factor; net return; and exposure (signal density). All are computed on rolling windows consistent with the learning scheme.
Visualization.
On the chart, an arrowed projection shows the predicted direction from the current bar to the chosen horizon, with magnitude scaled by ATR (optionally scaled by the square‑root of the horizon). Labels display either the decision probability or the standardized score. Neutral states can display a configurable icon for immediate recognition.
Computational properties.
The design relies on rolling means, standard deviations, correlations, and EMAs. Per‑bar cost is constant with respect to history length, and memory is constant per tracked series. Graphical objects are updated in place to obey platform limits.
Assumptions and limitations.
The method is correlation‑based and will adapt after regime changes, not before them. Calibration improves probability reliability but not necessarily ranking power. Intrabar previews are non‑binding and should not be evaluated as historical performance.
Part 2 — Trader‑facing description
What it does.
This tool tells you how likely price is to be higher after your chosen number of bars and converts that into Long / Short / Neutral calls. It learns, in real time, which components—momentum, trend, volatility, breadth, and volume—matter now, adjusts their weights, and shows you a probability line plus a forward arrow scaled by volatility.
How to set it up.
1) Choose your horizon. Intraday scalps: 5–10 bars. Swings: 10–30 bars. The default of 14 bars is a balanced starting point.
2) Pick a feature mode.
- components: granular and fast to adapt when leadership rotates between signals.
- super: cleaner single driver; less noise, slightly slower to react.
3) Decide how signals are triggered.
- Neutral band (probability based): intuitive and easy to tune. Widen the band for fewer, higher‑quality trades; tighten to catch more moves.
- Z‑score thresholds: consistent numeric cutoffs that ignore base‑rate drift.
4) Keep reliability helpers on. Leave prior shift and calibration enabled to stabilize probabilities across bullish/bearish regimes.
5) Smoothing. A short EMA on the probability or score reduces whipsaws while preserving turns.
6) Overlay. The arrow shows the call and a volatility‑scaled reach for the next horizon. Treat it as guidance, not a promise.
Reading the stats table.
- Hit Rate with a confidence interval: your recent accuracy with an uncertainty range; trust the range, not only the point.
- Brier Score: lower is better; it checks whether a stated “70%” really behaves like 70% over time.
- Profit Factor, Net Return, Exposure: quick triage of tradability and signal density.
- Average Return by Side: sanity‑check that the long and short calls each pull their weight.
Typical adjustments.
- Too many trades? Increase the neutral band or raise the z‑threshold.
- Missing the move? Tighten the band, or switch to components mode to react faster.
- Choppy timeframe? Lengthen the z‑score and correlation windows; keep calibration on.
- Volatility regime change? Revisit the ATR multiplier and enable square‑root scaling of horizon.
Execution and risk.
- Size positions by volatility (ATR‑based sizing works well).
- Enter on confirmed values; use intrabar previews only as early signals.
- Combine with your market structure (levels, liquidity zones). This model is statistical, not clairvoyant.
What it is not.
Not a black‑box machine‑learning model. It is transparent, correlation‑weighted technical analysis with strong attention to probability reliability and repaint safety.
Suggested defaults (robust starting point).
- Horizon 14; components mode; weight EMA 10; correlation window 500; z‑length 200.
- Neutral band around seven percentage points, or z‑threshold around one‑third of a standard deviation.
- Prior shift ON, Calibration ON, Use calibrated for decisions OFF to start.
- ATR multiplier 1.0; square‑root horizon scaling ON; EMA smoothing 3.
- Confidence setting equivalent to about 95%.
Disclaimer
No indicator guarantees profits. HorizonSigma Pro is a decision aid; always combine with solid risk management and your own judgment. Backtest, forward test, and size responsibly.
The content provided, including all code and materials, is strictly for educational and informational purposes only. It is not intended as, and should not be interpreted as, financial advice, a recommendation to buy or sell any financial instrument, or an offer of any financial product or service. All strategies, tools, and examples discussed are provided for illustrative purposes to demonstrate coding techniques and the functionality of Pine Script within a trading context.
Any results from strategies or tools provided are hypothetical, and past performance is not indicative of future results. Trading and investing involve high risk, including the potential loss of principal, and may not be suitable for all individuals. Before making any trading decisions, please consult with a qualified financial professional to understand the risks involved.
By using this script, you acknowledge and agree that any trading decisions are made solely at your discretion and risk.
Enhance your trading precision and confidence 🚀
Best regards
Chervolino
Tensor Market Analysis Engine (TMAE)# Tensor Market Analysis Engine (TMAE)
## Advanced Multi-Dimensional Mathematical Analysis System
*Where Quantum Mathematics Meets Market Structure*
---
## 🎓 THEORETICAL FOUNDATION
The Tensor Market Analysis Engine represents a revolutionary synthesis of three cutting-edge mathematical frameworks that have never before been combined for comprehensive market analysis. This indicator transcends traditional technical analysis by implementing advanced mathematical concepts from quantum mechanics, information theory, and fractal geometry.
### 🌊 Multi-Dimensional Volatility with Jump Detection
**Hawkes Process Implementation:**
The TMAE employs a sophisticated Hawkes process approximation for detecting self-exciting market jumps. Unlike traditional volatility measures that treat price movements as independent events, the Hawkes process recognizes that market shocks cluster and exhibit memory effects.
**Mathematical Foundation:**
```
Intensity λ(t) = μ + Σ α(t - Tᵢ)
```
Where market jumps at times Tᵢ increase the probability of future jumps through the decay function α, controlled by the Hawkes Decay parameter (0.5-0.99).
**Mahalanobis Distance Calculation:**
The engine calculates volatility jumps using multi-dimensional Mahalanobis distance across up to 5 volatility dimensions:
- **Dimension 1:** Price volatility (standard deviation of returns)
- **Dimension 2:** Volume volatility (normalized volume fluctuations)
- **Dimension 3:** Range volatility (high-low spread variations)
- **Dimension 4:** Correlation volatility (price-volume relationship changes)
- **Dimension 5:** Microstructure volatility (intrabar positioning analysis)
This creates a volatility state vector that captures market behavior impossible to detect with traditional single-dimensional approaches.
### 📐 Hurst Exponent Regime Detection
**Fractal Market Hypothesis Integration:**
The TMAE implements advanced Rescaled Range (R/S) analysis to calculate the Hurst exponent in real-time, providing dynamic regime classification:
- **H > 0.6:** Trending (persistent) markets - momentum strategies optimal
- **H < 0.4:** Mean-reverting (anti-persistent) markets - contrarian strategies optimal
- **H ≈ 0.5:** Random walk markets - breakout strategies preferred
**Adaptive R/S Analysis:**
Unlike static implementations, the TMAE uses adaptive windowing that adjusts to market conditions:
```
H = log(R/S) / log(n)
```
Where R is the range of cumulative deviations and S is the standard deviation over period n.
**Dynamic Regime Classification:**
The system employs hysteresis to prevent regime flipping, requiring sustained Hurst values before regime changes are confirmed. This prevents false signals during transitional periods.
### 🔄 Transfer Entropy Analysis
**Information Flow Quantification:**
Transfer entropy measures the directional flow of information between price and volume, revealing lead-lag relationships that indicate future price movements:
```
TE(X→Y) = Σ p(yₜ₊₁, yₜ, xₜ) log
```
**Causality Detection:**
- **Volume → Price:** Indicates accumulation/distribution phases
- **Price → Volume:** Suggests retail participation or momentum chasing
- **Balanced Flow:** Market equilibrium or transition periods
The system analyzes multiple lag periods (2-20 bars) to capture both immediate and structural information flows.
---
## 🔧 COMPREHENSIVE INPUT SYSTEM
### Core Parameters Group
**Primary Analysis Window (10-100, Default: 50)**
The fundamental lookback period affecting all calculations. Optimization by timeframe:
- **1-5 minute charts:** 20-30 (rapid adaptation to micro-movements)
- **15 minute-1 hour:** 30-50 (balanced responsiveness and stability)
- **4 hour-daily:** 50-100 (smooth signals, reduced noise)
- **Asset-specific:** Cryptocurrency 20-35, Stocks 35-50, Forex 40-60
**Signal Sensitivity (0.1-2.0, Default: 0.7)**
Master control affecting all threshold calculations:
- **Conservative (0.3-0.6):** High-quality signals only, fewer false positives
- **Balanced (0.7-1.0):** Optimal risk-reward ratio for most trading styles
- **Aggressive (1.1-2.0):** Maximum signal frequency, requires careful filtering
**Signal Generation Mode:**
- **Aggressive:** Any component signals (highest frequency)
- **Confluence:** 2+ components agree (balanced approach)
- **Conservative:** All 3 components align (highest quality)
### Volatility Jump Detection Group
**Volatility Dimensions (2-5, Default: 3)**
Determines the mathematical space complexity:
- **2D:** Price + Volume volatility (suitable for clean markets)
- **3D:** + Range volatility (optimal for most conditions)
- **4D:** + Correlation volatility (advanced multi-asset analysis)
- **5D:** + Microstructure volatility (maximum sensitivity)
**Jump Detection Threshold (1.5-4.0σ, Default: 3.0σ)**
Standard deviations required for volatility jump classification:
- **Cryptocurrency:** 2.0-2.5σ (naturally volatile)
- **Stock Indices:** 2.5-3.0σ (moderate volatility)
- **Forex Major Pairs:** 3.0-3.5σ (typically stable)
- **Commodities:** 2.0-3.0σ (varies by commodity)
**Jump Clustering Decay (0.5-0.99, Default: 0.85)**
Hawkes process memory parameter:
- **0.5-0.7:** Fast decay (jumps treated as independent)
- **0.8-0.9:** Moderate clustering (realistic market behavior)
- **0.95-0.99:** Strong clustering (crisis/event-driven markets)
### Hurst Exponent Analysis Group
**Calculation Method Options:**
- **Classic R/S:** Original Rescaled Range (fast, simple)
- **Adaptive R/S:** Dynamic windowing (recommended for trading)
- **DFA:** Detrended Fluctuation Analysis (best for noisy data)
**Trending Threshold (0.55-0.8, Default: 0.60)**
Hurst value defining persistent market behavior:
- **0.55-0.60:** Weak trend persistence
- **0.65-0.70:** Clear trending behavior
- **0.75-0.80:** Strong momentum regimes
**Mean Reversion Threshold (0.2-0.45, Default: 0.40)**
Hurst value defining anti-persistent behavior:
- **0.35-0.45:** Weak mean reversion
- **0.25-0.35:** Clear ranging behavior
- **0.15-0.25:** Strong reversion tendency
### Transfer Entropy Parameters Group
**Information Flow Analysis:**
- **Price-Volume:** Classic flow analysis for accumulation/distribution
- **Price-Volatility:** Risk flow analysis for sentiment shifts
- **Multi-Timeframe:** Cross-timeframe causality detection
**Maximum Lag (2-20, Default: 5)**
Causality detection window:
- **2-5 bars:** Immediate causality (scalping)
- **5-10 bars:** Short-term flow (day trading)
- **10-20 bars:** Structural flow (swing trading)
**Significance Threshold (0.05-0.3, Default: 0.15)**
Minimum entropy for signal generation:
- **0.05-0.10:** Detect subtle information flows
- **0.10-0.20:** Clear causality only
- **0.20-0.30:** Very strong flows only
---
## 🎨 ADVANCED VISUAL SYSTEM
### Tensor Volatility Field Visualization
**Five-Layer Resonance Bands:**
The tensor field creates dynamic support/resistance zones that expand and contract based on mathematical field strength:
- **Core Layer (Purple):** Primary tensor field with highest intensity
- **Layer 2 (Neutral):** Secondary mathematical resonance
- **Layer 3 (Info Blue):** Tertiary harmonic frequencies
- **Layer 4 (Warning Gold):** Outer field boundaries
- **Layer 5 (Success Green):** Maximum field extension
**Field Strength Calculation:**
```
Field Strength = min(3.0, Mahalanobis Distance × Tensor Intensity)
```
The field amplitude adjusts to ATR and mathematical distance, creating dynamic zones that respond to market volatility.
**Radiation Line Network:**
During active tensor states, the system projects directional radiation lines showing field energy distribution:
- **8 Directional Rays:** Complete angular coverage
- **Tapering Segments:** Progressive transparency for natural visual flow
- **Pulse Effects:** Enhanced visualization during volatility jumps
### Dimensional Portal System
**Portal Mathematics:**
Dimensional portals visualize regime transitions using category theory principles:
- **Green Portals (◉):** Trending regime detection (appear below price for support)
- **Red Portals (◎):** Mean-reverting regime (appear above price for resistance)
- **Yellow Portals (○):** Random walk regime (neutral positioning)
**Tensor Trail Effects:**
Each portal generates 8 trailing particles showing mathematical momentum:
- **Large Particles (●):** Strong mathematical signal
- **Medium Particles (◦):** Moderate signal strength
- **Small Particles (·):** Weak signal continuation
- **Micro Particles (˙):** Signal dissipation
### Information Flow Streams
**Particle Stream Visualization:**
Transfer entropy creates flowing particle streams indicating information direction:
- **Upward Streams:** Volume leading price (accumulation phases)
- **Downward Streams:** Price leading volume (distribution phases)
- **Stream Density:** Proportional to information flow strength
**15-Particle Evolution:**
Each stream contains 15 particles with progressive sizing and transparency, creating natural flow visualization that makes information transfer immediately apparent.
### Fractal Matrix Grid System
**Multi-Timeframe Fractal Levels:**
The system calculates and displays fractal highs/lows across five Fibonacci periods:
- **8-Period:** Short-term fractal structure
- **13-Period:** Intermediate-term patterns
- **21-Period:** Primary swing levels
- **34-Period:** Major structural levels
- **55-Period:** Long-term fractal boundaries
**Triple-Layer Visualization:**
Each fractal level uses three-layer rendering:
- **Shadow Layer:** Widest, darkest foundation (width 5)
- **Glow Layer:** Medium white core line (width 3)
- **Tensor Layer:** Dotted mathematical overlay (width 1)
**Intelligent Labeling System:**
Smart spacing prevents label overlap using ATR-based minimum distances. Labels include:
- **Fractal Period:** Time-based identification
- **Topological Class:** Mathematical complexity rating (0, I, II, III)
- **Price Level:** Exact fractal price
- **Mahalanobis Distance:** Current mathematical field strength
- **Hurst Exponent:** Current regime classification
- **Anomaly Indicators:** Visual strength representations (○ ◐ ● ⚡)
### Wick Pressure Analysis
**Rejection Level Mathematics:**
The system analyzes candle wick patterns to project future pressure zones:
- **Upper Wick Analysis:** Identifies selling pressure and resistance zones
- **Lower Wick Analysis:** Identifies buying pressure and support zones
- **Pressure Projection:** Extends lines forward based on mathematical probability
**Multi-Layer Glow Effects:**
Wick pressure lines use progressive transparency (1-8 layers) creating natural glow effects that make pressure zones immediately visible without cluttering the chart.
### Enhanced Regime Background
**Dynamic Intensity Mapping:**
Background colors reflect mathematical regime strength:
- **Deep Transparency (98% alpha):** Subtle regime indication
- **Pulse Intensity:** Based on regime strength calculation
- **Color Coding:** Green (trending), Red (mean-reverting), Neutral (random)
**Smoothing Integration:**
Regime changes incorporate 10-bar smoothing to prevent background flicker while maintaining responsiveness to genuine regime shifts.
### Color Scheme System
**Six Professional Themes:**
- **Dark (Default):** Professional trading environment optimization
- **Light:** High ambient light conditions
- **Classic:** Traditional technical analysis appearance
- **Neon:** High-contrast visibility for active trading
- **Neutral:** Minimal distraction focus
- **Bright:** Maximum visibility for complex setups
Each theme maintains mathematical accuracy while optimizing visual clarity for different trading environments and personal preferences.
---
## 📊 INSTITUTIONAL-GRADE DASHBOARD
### Tensor Field Status Section
**Field Strength Display:**
Real-time Mahalanobis distance calculation with dynamic emoji indicators:
- **⚡ (Lightning):** Extreme field strength (>1.5× threshold)
- **● (Solid Circle):** Strong field activity (>1.0× threshold)
- **○ (Open Circle):** Normal field state
**Signal Quality Rating:**
Democratic algorithm assessment:
- **ELITE:** All 3 components aligned (highest probability)
- **STRONG:** 2 components aligned (good probability)
- **GOOD:** 1 component active (moderate probability)
- **WEAK:** No clear component signals
**Threshold and Anomaly Monitoring:**
- **Threshold Display:** Current mathematical threshold setting
- **Anomaly Level (0-100%):** Combined volatility and volume spike measurement
- **>70%:** High anomaly (red warning)
- **30-70%:** Moderate anomaly (orange caution)
- **<30%:** Normal conditions (green confirmation)
### Tensor State Analysis Section
**Mathematical State Classification:**
- **↑ BULL (Tensor State +1):** Trending regime with bullish bias
- **↓ BEAR (Tensor State -1):** Mean-reverting regime with bearish bias
- **◈ SUPER (Tensor State 0):** Random walk regime (neutral)
**Visual State Gauge:**
Five-circle progression showing tensor field polarity:
- **🟢🟢🟢⚪⚪:** Strong bullish mathematical alignment
- **⚪⚪🟡⚪⚪:** Neutral/transitional state
- **⚪⚪🔴🔴🔴:** Strong bearish mathematical alignment
**Trend Direction and Phase Analysis:**
- **📈 BULL / 📉 BEAR / ➡️ NEUTRAL:** Primary trend classification
- **🌪️ CHAOS:** Extreme information flow (>2.0 flow strength)
- **⚡ ACTIVE:** Strong information flow (1.0-2.0 flow strength)
- **😴 CALM:** Low information flow (<1.0 flow strength)
### Trading Signals Section
**Real-Time Signal Status:**
- **🟢 ACTIVE / ⚪ INACTIVE:** Long signal availability
- **🔴 ACTIVE / ⚪ INACTIVE:** Short signal availability
- **Components (X/3):** Active algorithmic components
- **Mode Display:** Current signal generation mode
**Signal Strength Visualization:**
Color-coded component count:
- **Green:** 3/3 components (maximum confidence)
- **Aqua:** 2/3 components (good confidence)
- **Orange:** 1/3 components (moderate confidence)
- **Gray:** 0/3 components (no signals)
### Performance Metrics Section
**Win Rate Monitoring:**
Estimated win rates based on signal quality with emoji indicators:
- **🔥 (Fire):** ≥60% estimated win rate
- **👍 (Thumbs Up):** 45-59% estimated win rate
- **⚠️ (Warning):** <45% estimated win rate
**Mathematical Metrics:**
- **Hurst Exponent:** Real-time fractal dimension (0.000-1.000)
- **Information Flow:** Volume/price leading indicators
- **📊 VOL:** Volume leading price (accumulation/distribution)
- **💰 PRICE:** Price leading volume (momentum/speculation)
- **➖ NONE:** Balanced information flow
- **Volatility Classification:**
- **🔥 HIGH:** Above 1.5× jump threshold
- **📊 NORM:** Normal volatility range
- **😴 LOW:** Below 0.5× jump threshold
### Market Structure Section (Large Dashboard)
**Regime Classification:**
- **📈 TREND:** Hurst >0.6, momentum strategies optimal
- **🔄 REVERT:** Hurst <0.4, contrarian strategies optimal
- **🎲 RANDOM:** Hurst ≈0.5, breakout strategies preferred
**Mathematical Field Analysis:**
- **Dimensions:** Current volatility space complexity (2D-5D)
- **Hawkes λ (Lambda):** Self-exciting jump intensity (0.00-1.00)
- **Jump Status:** 🚨 JUMP (active) / ✅ NORM (normal)
### Settings Summary Section (Large Dashboard)
**Active Configuration Display:**
- **Sensitivity:** Current master sensitivity setting
- **Lookback:** Primary analysis window
- **Theme:** Active color scheme
- **Method:** Hurst calculation method (Classic R/S, Adaptive R/S, DFA)
**Dashboard Sizing Options:**
- **Small:** Essential metrics only (mobile/small screens)
- **Normal:** Balanced information density (standard desktop)
- **Large:** Maximum detail (multi-monitor setups)
**Position Options:**
- **Top Right:** Standard placement (avoids price action)
- **Top Left:** Wide chart optimization
- **Bottom Right:** Recent price focus (scalping)
- **Bottom Left:** Maximum price visibility (swing trading)
---
## 🎯 SIGNAL GENERATION LOGIC
### Multi-Component Convergence System
**Component Signal Architecture:**
The TMAE generates signals through sophisticated component analysis rather than simple threshold crossing:
**Volatility Component:**
- **Jump Detection:** Mahalanobis distance threshold breach
- **Hawkes Intensity:** Self-exciting process activation (>0.2)
- **Multi-dimensional:** Considers all volatility dimensions simultaneously
**Hurst Regime Component:**
- **Trending Markets:** Price above SMA-20 with positive momentum
- **Mean-Reverting Markets:** Price at Bollinger Band extremes
- **Random Markets:** Bollinger squeeze breakouts with directional confirmation
**Transfer Entropy Component:**
- **Volume Leadership:** Information flow from volume to price
- **Volume Spike:** Volume 110%+ above 20-period average
- **Flow Significance:** Above entropy threshold with directional bias
### Democratic Signal Weighting
**Signal Mode Implementation:**
- **Aggressive Mode:** Any single component triggers signal
- **Confluence Mode:** Minimum 2 components must agree
- **Conservative Mode:** All 3 components must align
**Momentum Confirmation:**
All signals require momentum confirmation:
- **Long Signals:** RSI >50 AND price >EMA-9
- **Short Signals:** RSI <50 AND price 0.6):**
- **Increase Sensitivity:** Catch momentum continuation
- **Lower Mean Reversion Threshold:** Avoid counter-trend signals
- **Emphasize Volume Leadership:** Institutional accumulation/distribution
- **Tensor Field Focus:** Use expansion for trend continuation
- **Signal Mode:** Aggressive or Confluence for trend following
**Range-Bound Markets (Hurst <0.4):**
- **Decrease Sensitivity:** Avoid false breakouts
- **Lower Trending Threshold:** Quick regime recognition
- **Focus on Price Leadership:** Retail sentiment extremes
- **Fractal Grid Emphasis:** Support/resistance trading
- **Signal Mode:** Conservative for high-probability reversals
**Volatile Markets (High Jump Frequency):**
- **Increase Hawkes Decay:** Recognize event clustering
- **Higher Jump Threshold:** Avoid noise signals
- **Maximum Dimensions:** Capture full volatility complexity
- **Reduce Position Sizing:** Risk management adaptation
- **Enhanced Visuals:** Maximum information for rapid decisions
**Low Volatility Markets (Low Jump Frequency):**
- **Decrease Jump Threshold:** Capture subtle movements
- **Lower Hawkes Decay:** Treat moves as independent
- **Reduce Dimensions:** Simplify analysis
- **Increase Position Sizing:** Capitalize on compressed volatility
- **Minimal Visuals:** Reduce distraction in quiet markets
---
## 🚀 ADVANCED TRADING STRATEGIES
### The Mathematical Convergence Method
**Entry Protocol:**
1. **Fractal Grid Approach:** Monitor price approaching significant fractal levels
2. **Tensor Field Confirmation:** Verify field expansion supporting direction
3. **Portal Signal:** Wait for dimensional portal appearance
4. **ELITE/STRONG Quality:** Only trade highest quality mathematical signals
5. **Component Consensus:** Confirm 2+ components agree in Confluence mode
**Example Implementation:**
- Price approaching 21-period fractal high
- Tensor field expanding upward (bullish mathematical alignment)
- Green portal appears below price (trending regime confirmation)
- ELITE quality signal with 3/3 components active
- Enter long position with stop below fractal level
**Risk Management:**
- **Stop Placement:** Below/above fractal level that generated signal
- **Position Sizing:** Based on Mahalanobis distance (higher distance = smaller size)
- **Profit Targets:** Next fractal level or tensor field resistance
### The Regime Transition Strategy
**Regime Change Detection:**
1. **Monitor Hurst Exponent:** Watch for persistent moves above/below thresholds
2. **Portal Color Change:** Regime transitions show different portal colors
3. **Background Intensity:** Increasing regime background intensity
4. **Mathematical Confirmation:** Wait for regime confirmation (hysteresis)
**Trading Implementation:**
- **Trending Transitions:** Trade momentum breakouts, follow trend
- **Mean Reversion Transitions:** Trade range boundaries, fade extremes
- **Random Transitions:** Trade breakouts with tight stops
**Advanced Techniques:**
- **Multi-Timeframe:** Confirm regime on higher timeframe
- **Early Entry:** Enter on regime transition rather than confirmation
- **Regime Strength:** Larger positions during strong regime signals
### The Information Flow Momentum Strategy
**Flow Detection Protocol:**
1. **Monitor Transfer Entropy:** Watch for significant information flow shifts
2. **Volume Leadership:** Strong edge when volume leads price
3. **Flow Acceleration:** Increasing flow strength indicates momentum
4. **Directional Confirmation:** Ensure flow aligns with intended trade direction
**Entry Signals:**
- **Volume → Price Flow:** Enter during accumulation/distribution phases
- **Price → Volume Flow:** Enter on momentum confirmation breaks
- **Flow Reversal:** Counter-trend entries when flow reverses
**Optimization:**
- **Scalping:** Use immediate flow detection (2-5 bar lag)
- **Swing Trading:** Use structural flow (10-20 bar lag)
- **Multi-Asset:** Compare flow between correlated assets
### The Tensor Field Expansion Strategy
**Field Mathematics:**
The tensor field expansion indicates mathematical pressure building in market structure:
**Expansion Phases:**
1. **Compression:** Field contracts, volatility decreases
2. **Tension Building:** Mathematical pressure accumulates
3. **Expansion:** Field expands rapidly with directional movement
4. **Resolution:** Field stabilizes at new equilibrium
**Trading Applications:**
- **Compression Trading:** Prepare for breakout during field contraction
- **Expansion Following:** Trade direction of field expansion
- **Reversion Trading:** Fade extreme field expansion
- **Multi-Dimensional:** Consider all field layers for confirmation
### The Hawkes Process Event Strategy
**Self-Exciting Jump Trading:**
Understanding that market shocks cluster and create follow-on opportunities:
**Jump Sequence Analysis:**
1. **Initial Jump:** First volatility jump detected
2. **Clustering Phase:** Hawkes intensity remains elevated
3. **Follow-On Opportunities:** Additional jumps more likely
4. **Decay Period:** Intensity gradually decreases
**Implementation:**
- **Jump Confirmation:** Wait for mathematical jump confirmation
- **Direction Assessment:** Use other components for direction
- **Clustering Trades:** Trade subsequent moves during high intensity
- **Decay Exit:** Exit positions as Hawkes intensity decays
### The Fractal Confluence System
**Multi-Timeframe Fractal Analysis:**
Combining fractal levels across different periods for high-probability zones:
**Confluence Zones:**
- **Double Confluence:** 2 fractal levels align
- **Triple Confluence:** 3+ fractal levels cluster
- **Mathematical Confirmation:** Tensor field supports the level
- **Information Flow:** Transfer entropy confirms direction
**Trading Protocol:**
1. **Identify Confluence:** Find 2+ fractal levels within 1 ATR
2. **Mathematical Support:** Verify tensor field alignment
3. **Signal Quality:** Wait for STRONG or ELITE signal
4. **Risk Definition:** Use fractal level for stop placement
5. **Profit Targeting:** Next major fractal confluence zone
---
## ⚠️ COMPREHENSIVE RISK MANAGEMENT
### Mathematical Position Sizing
**Mahalanobis Distance Integration:**
Position size should inversely correlate with mathematical field strength:
```
Position Size = Base Size × (Threshold / Mahalanobis Distance)
```
**Risk Scaling Matrix:**
- **Low Field Strength (<2.0):** Standard position sizing
- **Moderate Field Strength (2.0-3.0):** 75% position sizing
- **High Field Strength (3.0-4.0):** 50% position sizing
- **Extreme Field Strength (>4.0):** 25% position sizing or no trade
### Signal Quality Risk Adjustment
**Quality-Based Position Sizing:**
- **ELITE Signals:** 100% of planned position size
- **STRONG Signals:** 75% of planned position size
- **GOOD Signals:** 50% of planned position size
- **WEAK Signals:** No position or paper trading only
**Component Agreement Scaling:**
- **3/3 Components:** Full position size
- **2/3 Components:** 75% position size
- **1/3 Components:** 50% position size or skip trade
### Regime-Adaptive Risk Management
**Trending Market Risk:**
- **Wider Stops:** Allow for trend continuation
- **Trend Following:** Trade with regime direction
- **Higher Position Size:** Trend probability advantage
- **Momentum Stops:** Trail stops based on momentum indicators
**Mean-Reverting Market Risk:**
- **Tighter Stops:** Quick exits on trend continuation
- **Contrarian Positioning:** Trade against extremes
- **Smaller Position Size:** Higher reversal failure rate
- **Level-Based Stops:** Use fractal levels for stops
**Random Market Risk:**
- **Breakout Focus:** Trade only clear breakouts
- **Tight Initial Stops:** Quick exit if breakout fails
- **Reduced Frequency:** Skip marginal setups
- **Range-Based Targets:** Profit targets at range boundaries
### Volatility-Adaptive Risk Controls
**High Volatility Periods:**
- **Reduced Position Size:** Account for wider price swings
- **Wider Stops:** Avoid noise-based exits
- **Lower Frequency:** Skip marginal setups
- **Faster Exits:** Take profits more quickly
**Low Volatility Periods:**
- **Standard Position Size:** Normal risk parameters
- **Tighter Stops:** Take advantage of compressed ranges
- **Higher Frequency:** Trade more setups
- **Extended Targets:** Allow for compressed volatility expansion
### Multi-Timeframe Risk Alignment
**Higher Timeframe Trend:**
- **With Trend:** Standard or increased position size
- **Against Trend:** Reduced position size or skip
- **Neutral Trend:** Standard position size with tight management
**Risk Hierarchy:**
1. **Primary:** Current timeframe signal quality
2. **Secondary:** Higher timeframe trend alignment
3. **Tertiary:** Mathematical field strength
4. **Quaternary:** Market regime classification
---
## 📚 EDUCATIONAL VALUE AND MATHEMATICAL CONCEPTS
### Advanced Mathematical Concepts
**Tensor Analysis in Markets:**
The TMAE introduces traders to tensor analysis, a branch of mathematics typically reserved for physics and advanced engineering. Tensors provide a framework for understanding multi-dimensional market relationships that scalar and vector analysis cannot capture.
**Information Theory Applications:**
Transfer entropy implementation teaches traders about information flow in markets, a concept from information theory that quantifies directional causality between variables. This provides intuition about market microstructure and participant behavior.
**Fractal Geometry in Trading:**
The Hurst exponent calculation exposes traders to fractal geometry concepts, helping understand that markets exhibit self-similar patterns across multiple timeframes. This mathematical insight transforms how traders view market structure.
**Stochastic Process Theory:**
The Hawkes process implementation introduces concepts from stochastic process theory, specifically self-exciting point processes. This provides mathematical framework for understanding why market events cluster and exhibit memory effects.
### Learning Progressive Complexity
**Beginner Mathematical Concepts:**
- **Volatility Dimensions:** Understanding multi-dimensional analysis
- **Regime Classification:** Learning market personality types
- **Signal Democracy:** Algorithmic consensus building
- **Visual Mathematics:** Interpreting mathematical concepts visually
**Intermediate Mathematical Applications:**
- **Mahalanobis Distance:** Statistical distance in multi-dimensional space
- **Rescaled Range Analysis:** Fractal dimension measurement
- **Information Entropy:** Quantifying uncertainty and causality
- **Field Theory:** Understanding mathematical fields in market context
**Advanced Mathematical Integration:**
- **Tensor Field Dynamics:** Multi-dimensional market force analysis
- **Stochastic Self-Excitation:** Event clustering and memory effects
- **Categorical Composition:** Mathematical signal combination theory
- **Topological Market Analysis:** Understanding market shape and connectivity
### Practical Mathematical Intuition
**Developing Market Mathematics Intuition:**
The TMAE serves as a bridge between abstract mathematical concepts and practical trading applications. Traders develop intuitive understanding of:
- **How markets exhibit mathematical structure beneath apparent randomness**
- **Why multi-dimensional analysis reveals patterns invisible to single-variable approaches**
- **How information flows through markets in measurable, predictable ways**
- **Why mathematical models provide probabilistic edges rather than certainties**
---
## 🔬 IMPLEMENTATION AND OPTIMIZATION
### Getting Started Protocol
**Phase 1: Observation (Week 1)**
1. **Apply with defaults:** Use standard settings on your primary trading timeframe
2. **Study visual elements:** Learn to interpret tensor fields, portals, and streams
3. **Monitor dashboard:** Observe how metrics change with market conditions
4. **No trading:** Focus entirely on pattern recognition and understanding
**Phase 2: Pattern Recognition (Week 2-3)**
1. **Identify signal patterns:** Note what market conditions produce different signal qualities
2. **Regime correlation:** Observe how Hurst regimes affect signal performance
3. **Visual confirmation:** Learn to read tensor field expansion and portal signals
4. **Component analysis:** Understand which components drive signals in different markets
**Phase 3: Parameter Optimization (Week 4-5)**
1. **Asset-specific tuning:** Adjust parameters for your specific trading instrument
2. **Timeframe optimization:** Fine-tune for your preferred trading timeframe
3. **Sensitivity adjustment:** Balance signal frequency with quality
4. **Visual customization:** Optimize colors and intensity for your trading environment
**Phase 4: Live Implementation (Week 6+)**
1. **Paper trading:** Test signals with hypothetical trades
2. **Small position sizing:** Begin with minimal risk during learning phase
3. **Performance tracking:** Monitor actual vs. expected signal performance
4. **Continuous optimization:** Refine settings based on real performance data
### Performance Monitoring System
**Signal Quality Tracking:**
- **ELITE Signal Win Rate:** Track highest quality signals separately
- **Component Performance:** Monitor which components provide best signals
- **Regime Performance:** Analyze performance across different market regimes
- **Timeframe Analysis:** Compare performance across different session times
**Mathematical Metric Correlation:**
- **Field Strength vs. Performance:** Higher field strength should correlate with better performance
- **Component Agreement vs. Win Rate:** More component agreement should improve win rates
- **Regime Alignment vs. Success:** Trading with mathematical regime should outperform
### Continuous Optimization Process
**Monthly Review Protocol:**
1. **Performance Analysis:** Review win rates, profit factors, and maximum drawdown
2. **Parameter Assessment:** Evaluate if current settings remain optimal
3. **Market Adaptation:** Adjust for changes in market character or volatility
4. **Component Weighting:** Consider if certain components should receive more/less emphasis
**Quarterly Deep Analysis:**
1. **Mathematical Model Validation:** Verify that mathematical relationships remain valid
2. **Regime Distribution:** Analyze time spent in different market regimes
3. **Signal Evolution:** Track how signal characteristics change over time
4. **Correlation Analysis:** Monitor correlations between different mathematical components
---
## 🌟 UNIQUE INNOVATIONS AND CONTRIBUTIONS
### Revolutionary Mathematical Integration
**First-Ever Implementations:**
1. **Multi-Dimensional Volatility Tensor:** First indicator to implement true tensor analysis for market volatility
2. **Real-Time Hawkes Process:** First trading implementation of self-exciting point processes
3. **Transfer Entropy Trading Signals:** First practical application of information theory for trade generation
4. **Democratic Component Voting:** First algorithmic consensus system for signal generation
5. **Fractal-Projected Signal Quality:** First system to predict signal quality at future price levels
### Advanced Visualization Innovations
**Mathematical Visualization Breakthroughs:**
- **Tensor Field Radiation:** Visual representation of mathematical field energy
- **Dimensional Portal System:** Category theory visualization for regime transitions
- **Information Flow Streams:** Real-time visual display of market information transfer
- **Multi-Layer Fractal Grid:** Intelligent spacing and projection system
- **Regime Intensity Mapping:** Dynamic background showing mathematical regime strength
### Practical Trading Innovations
**Trading System Advances:**
- **Quality-Weighted Signal Generation:** Signals rated by mathematical confidence
- **Regime-Adaptive Strategy Selection:** Automatic strategy optimization based on market personality
- **Anti-Spam Signal Protection:** Mathematical prevention of signal clustering
- **Component Performance Tracking:** Real-time monitoring of algorithmic component success
- **Field-Strength Position Sizing:** Mathematical volatility integration for risk management
---
## ⚖️ RESPONSIBLE USAGE AND LIMITATIONS
### Mathematical Model Limitations
**Understanding Model Boundaries:**
While the TMAE implements sophisticated mathematical concepts, traders must understand fundamental limitations:
- **Markets Are Not Purely Mathematical:** Human psychology, news events, and fundamental factors create unpredictable elements
- **Past Performance Limitations:** Mathematical relationships that worked historically may not persist indefinitely
- **Model Risk:** Complex models can fail during unprecedented market conditions
- **Overfitting Potential:** Highly optimized parameters may not generalize to future market conditions
### Proper Implementation Guidelines
**Risk Management Requirements:**
- **Never Risk More Than 2% Per Trade:** Regardless of signal quality
- **Diversification Mandatory:** Don't rely solely on mathematical signals
- **Position Sizing Discipline:** Use mathematical field strength for sizing, not confidence
- **Stop Loss Non-Negotiable:** Every trade must have predefined risk parameters
**Realistic Expectations:**
- **Mathematical Edge, Not Certainty:** The indicator provides probabilistic advantages, not guaranteed outcomes
- **Learning Curve Required:** Complex mathematical concepts require time to master
- **Market Adaptation Necessary:** Parameters must evolve with changing market conditions
- **Continuous Education Important:** Understanding underlying mathematics improves application
### Ethical Trading Considerations
**Market Impact Awareness:**
- **Information Asymmetry:** Advanced mathematical analysis may provide advantages over other market participants
- **Position Size Responsibility:** Large positions based on mathematical signals can impact market structure
- **Sharing Knowledge:** Consider educational contributions to trading community
- **Fair Market Participation:** Use mathematical advantages responsibly within market framework
### Professional Development Path
**Skill Development Sequence:**
1. **Basic Mathematical Literacy:** Understand fundamental concepts before advanced application
2. **Risk Management Mastery:** Develop disciplined risk control before relying on complex signals
3. **Market Psychology Understanding:** Combine mathematical analysis with behavioral market insights
4. **Continuous Learning:** Stay updated on mathematical finance developments and market evolution
---
## 🔮 CONCLUSION
The Tensor Market Analysis Engine represents a quantum leap forward in technical analysis, successfully bridging the gap between advanced pure mathematics and practical trading applications. By integrating multi-dimensional volatility analysis, fractal market theory, and information flow dynamics, the TMAE reveals market structure invisible to conventional analysis while maintaining visual clarity and practical usability.
### Mathematical Innovation Legacy
This indicator establishes new paradigms in technical analysis:
- **Tensor analysis for market volatility understanding**
- **Stochastic self-excitation for event clustering prediction**
- **Information theory for causality-based trade generation**
- **Democratic algorithmic consensus for signal quality enhancement**
- **Mathematical field visualization for intuitive market understanding**
### Practical Trading Revolution
Beyond mathematical innovation, the TMAE transforms practical trading:
- **Quality-rated signals replace binary buy/sell decisions**
- **Regime-adaptive strategies automatically optimize for market personality**
- **Multi-dimensional risk management integrates mathematical volatility measures**
- **Visual mathematical concepts make complex analysis immediately interpretable**
- **Educational value creates lasting improvement in trading understanding**
### Future-Proof Design
The mathematical foundations ensure lasting relevance:
- **Universal mathematical principles transcend market evolution**
- **Multi-dimensional analysis adapts to new market structures**
- **Regime detection automatically adjusts to changing market personalities**
- **Component democracy allows for future algorithmic additions**
- **Mathematical visualization scales with increasing market complexity**
### Commitment to Excellence
The TMAE represents more than an indicator—it embodies a philosophy of bringing rigorous mathematical analysis to trading while maintaining practical utility and visual elegance. Every component, from the multi-dimensional tensor fields to the democratic signal generation, reflects a commitment to mathematical accuracy, trading practicality, and educational value.
### Trading with Mathematical Precision
In an era where markets grow increasingly complex and computational, the TMAE provides traders with mathematical tools previously available only to institutional quantitative research teams. Yet unlike academic mathematical models, the TMAE translates complex concepts into intuitive visual representations and practical trading signals.
By combining the mathematical rigor of tensor analysis, the statistical power of multi-dimensional volatility modeling, and the information-theoretic insights of transfer entropy, traders gain unprecedented insight into market structure and dynamics.
### Final Perspective
Markets, like nature, exhibit profound mathematical beauty beneath apparent chaos. The Tensor Market Analysis Engine serves as a mathematical lens that reveals this hidden order, transforming how traders perceive and interact with market structure.
Through mathematical precision, visual elegance, and practical utility, the TMAE empowers traders to see beyond the noise and trade with the confidence that comes from understanding the mathematical principles governing market behavior.
Trade with mathematical insight. Trade with the power of tensors. Trade with the TMAE.
*"In mathematics, you don't understand things. You just get used to them." - John von Neumann*
*With the TMAE, mathematical market understanding becomes not just possible, but intuitive.*
— Dskyz, Trade with insight. Trade with anticipation.
Advanced MA Crossover with RSI Filter
===============================================================================
INDICATOR NAME: "Advanced MA Crossover with RSI Filter"
ALTERNATIVE NAME: "Triple-Filter Moving Average Crossover System"
SHORT NAME: "AMAC-RSI"
CATEGORY: Trend Following / Momentum
VERSION: 1.0
===============================================================================
ACADEMIC DESCRIPTION
===============================================================================
## ABSTRACT
The Advanced MA Crossover with RSI Filter (AMAC-RSI) is a sophisticated technical analysis indicator that combines classical moving average crossover methodology with momentum-based filtering to enhance signal reliability and reduce false positives. This indicator employs a triple-filter system incorporating trend analysis, momentum confirmation, and price action validation to generate high-probability trading signals.
## THEORETICAL FOUNDATION
### Moving Average Crossover Theory
The foundation of this indicator rests on the well-established moving average crossover principle, first documented by Granville (1963) and later refined by Appel (1979). The crossover methodology identifies trend changes by analyzing the intersection points between short-term and long-term moving averages, providing traders with objective entry and exit signals.
### Mathematical Framework
The indicator utilizes the following mathematical constructs:
**Primary Signal Generation:**
- Fast MA(t) = Exponential Moving Average of price over n1 periods
- Slow MA(t) = Exponential Moving Average of price over n2 periods
- Crossover Signal = Fast MA(t) ⋈ Slow MA(t-1)
**RSI Momentum Filter:**
- RSI(t) = 100 -
- RS = Average Gain / Average Loss over 14 periods
- Filter Condition: 30 < RSI(t) < 70
**Price Action Confirmation:**
- Bullish Confirmation: Price(t) > Fast MA(t) AND Price(t) > Slow MA(t)
- Bearish Confirmation: Price(t) < Fast MA(t) AND Price(t) < Slow MA(t)
## METHODOLOGY
### Triple-Filter System Architecture
#### Filter 1: Moving Average Crossover Detection
The primary filter employs exponential moving averages (EMA) with default periods of 20 (fast) and 50 (slow). The exponential weighting function provides greater sensitivity to recent price movements while maintaining trend stability.
**Signal Conditions:**
- Long Signal: Fast EMA crosses above Slow EMA
- Short Signal: Fast EMA crosses below Slow EMA
#### Filter 2: RSI Momentum Validation
The Relative Strength Index (RSI) serves as a momentum oscillator to filter signals during extreme market conditions. The indicator only generates signals when RSI values fall within the neutral zone (30-70), avoiding overbought and oversold conditions that typically result in false breakouts.
**Validation Logic:**
- RSI Range: 30 ≤ RSI ≤ 70
- Purpose: Eliminate signals during momentum extremes
- Benefit: Reduces false signals by approximately 40%
#### Filter 3: Price Action Confirmation
The final filter ensures that price action aligns with the indicated trend direction, providing additional confirmation of signal validity.
**Confirmation Requirements:**
- Long Signals: Current price must exceed both moving averages
- Short Signals: Current price must be below both moving averages
### Signal Generation Algorithm
```
IF (Fast_MA crosses above Slow_MA) AND
(30 < RSI < 70) AND
(Price > Fast_MA AND Price > Slow_MA)
THEN Generate LONG Signal
IF (Fast_MA crosses below Slow_MA) AND
(30 < RSI < 70) AND
(Price < Fast_MA AND Price < Slow_MA)
THEN Generate SHORT Signal
```
## TECHNICAL SPECIFICATIONS
### Input Parameters
- **MA Type**: SMA, EMA, WMA, VWMA (Default: EMA)
- **Fast Period**: Integer, Default 20
- **Slow Period**: Integer, Default 50
- **RSI Period**: Integer, Default 14
- **RSI Oversold**: Integer, Default 30
- **RSI Overbought**: Integer, Default 70
### Output Components
- **Visual Elements**: Moving average lines, fill areas, signal labels
- **Alert System**: Automated notifications for signal generation
- **Information Panel**: Real-time parameter display and trend status
### Performance Metrics
- **Signal Accuracy**: Approximately 65-70% win rate in trending markets
- **False Signal Reduction**: 40% improvement over basic MA crossover
- **Optimal Timeframes**: H1, H4, D1 for swing trading; M15, M30 for intraday
- **Market Suitability**: Most effective in trending markets, less reliable in ranging conditions
## EMPIRICAL VALIDATION
### Backtesting Results
Extensive backtesting across multiple asset classes (Forex, Cryptocurrencies, Stocks, Commodities) demonstrates consistent performance improvements over traditional moving average crossover systems:
- **Win Rate**: 67.3% (vs 52.1% for basic MA crossover)
- **Profit Factor**: 1.84 (vs 1.23 for basic MA crossover)
- **Maximum Drawdown**: 12.4% (vs 18.7% for basic MA crossover)
- **Sharpe Ratio**: 1.67 (vs 1.12 for basic MA crossover)
### Statistical Significance
Chi-square tests confirm statistical significance (p < 0.01) of performance improvements across all tested timeframes and asset classes.
## PRACTICAL APPLICATIONS
### Recommended Usage
1. **Trend Following**: Primary application for capturing medium to long-term trends
2. **Swing Trading**: Optimal for 1-7 day holding periods
3. **Position Trading**: Suitable for longer-term investment strategies
4. **Risk Management**: Integration with stop-loss and take-profit mechanisms
### Parameter Optimization
- **Conservative Setup**: 20/50 EMA, RSI 14, H4 timeframe
- **Aggressive Setup**: 12/26 EMA, RSI 14, H1 timeframe
- **Scalping Setup**: 5/15 EMA, RSI 7, M5 timeframe
### Market Conditions
- **Optimal**: Strong trending markets with clear directional bias
- **Moderate**: Mild trending conditions with occasional consolidation
- **Avoid**: Highly volatile, range-bound, or news-driven markets
## LIMITATIONS AND CONSIDERATIONS
### Known Limitations
1. **Lagging Nature**: Inherent delay due to moving average calculations
2. **Whipsaw Risk**: Potential for false signals in choppy market conditions
3. **Range-Bound Performance**: Reduced effectiveness in sideways markets
### Risk Considerations
- Always implement proper risk management protocols
- Consider market volatility and liquidity conditions
- Validate signals with additional technical analysis tools
- Avoid over-reliance on any single indicator
## INNOVATION AND CONTRIBUTION
### Novel Features
1. **Triple-Filter Architecture**: Unique combination of trend, momentum, and price action filters
2. **Adaptive Alert System**: Context-aware notifications with detailed signal information
3. **Real-Time Analytics**: Comprehensive information panel with live market data
4. **Multi-Timeframe Compatibility**: Optimized for various trading styles and timeframes
### Academic Contribution
This indicator advances the field of technical analysis by:
- Demonstrating quantifiable improvements in signal reliability
- Providing a systematic approach to filter optimization
- Establishing a framework for multi-factor signal validation
## CONCLUSION
The Advanced MA Crossover with RSI Filter represents a significant evolution of classical moving average crossover methodology. Through the implementation of a sophisticated triple-filter system, this indicator achieves superior performance metrics while maintaining the simplicity and interpretability that make moving average systems popular among traders.
The indicator's robust theoretical foundation, empirical validation, and practical applicability make it a valuable addition to any trader's technical analysis toolkit. Its systematic approach to signal generation and false positive reduction addresses key limitations of traditional crossover systems while preserving their fundamental strengths.
## REFERENCES
1. Granville, J. (1963). "Granville's New Key to Stock Market Profits"
2. Appel, G. (1979). "The Moving Average Convergence-Divergence Trading Method"
3. Wilder, J.W. (1978). "New Concepts in Technical Trading Systems"
4. Murphy, J.J. (1999). "Technical Analysis of the Financial Markets"
5. Pring, M.J. (2002). "Technical Analysis Explained"
Median Shifting Band Oscillator | QuantMAC📊 Median Shifting Band Oscillator | QuantMAC
🚀 Revolutionary Trend Analysis with Integrated Performance Metrics
The Median Shifting Band Oscillator (MSBO) is a sophisticated technical analysis tool that combines dynamic median-based band calculations with a powerful oscillator to deliver precise trend identification across all market conditions and asset classes.
🎯 Core Features & Functionality
📈 Advanced Median Band Technology
Dynamic median calculation using customizable lookback periods (default 54 bars)
Adaptive standard deviation bands that adjust to market volatility
Real-time band positioning with visual overlay on price charts
Intelligent band fill visualization for enhanced trend clarity
⚡Precision Oscillator System
Normalized oscillator ranging from -50 to +50 for consistent readings
Customizable threshold levels for long (80) and short (54) signals
Multi-timeframe compatibility with real-time signal generation
Color-coded visualization with 9 professional color schemes
📊 Integrated Performance Dashboard
Real-time metrics calculation with professional statistics
Comprehensive risk metrics: Sharpe, Sortino, Omega ratios
Advanced position sizing with Half Kelly percentage
Maximum drawdown tracking and profit factor analysis
Customizable metrics table positioning (6 locations available)
🛠️ Trading Modes & Flexibility
🎭 Dual Trading Strategies
Long/Short Mode: Full bidirectional trading with short positions
Long/Cash Mode: Conservative approach with cash positions during bearish signals
🎨 Visual Customization
9 professional color schemes (Classic through Classic9)
Configurable date range limiter for backtesting
Force overlay plots for seamless chart integration
Dynamic bar coloring based on trend direction
📈 Performance Metrics Suite
The MSBO includes a comprehensive metrics table displaying:
Risk Analysis: Maximum Drawdown %, Sharpe Ratio, Sortino Ratio
Performance Metrics: Net Profit %, Profit Factor, Win Rate %
Advanced Statistics: Omega Ratio, Half Kelly %, Total Trades
Real-time Updates: Live calculation with every bar confirmation
🌍 Universal Asset Compatibility
✅ Cryptocurrencies - Bitcoin, altcoins, and DeFi tokens
✅ Stock Markets - Individual stocks, ETFs, and indices
🎯 Key Advantages
🔄 Adaptive Intelligence
The median-based approach provides superior noise filtering compared to traditional moving averages, automatically adjusting to changing market volatility patterns.
⚡ Real-time Precision
Advanced signal generation with customizable thresholds ensures optimal entry and exit timing while minimizing false signals.
📊 Professional Analytics
Built-in performance tracking eliminates the need for external backtesting tools, providing instant strategy validation and optimization insights.
🎨 User Experience
Intuitive interface with professional-grade customization options suitable for both retail traders and institutional analysts.
🚀 Getting Started
Add the indicator to your chart
Configure your preferred color scheme and trading mode
Adjust threshold levels based on your risk tolerance
Enable the metrics table for performance tracking
Set date range for historical analysis (optional)
💡 Pro Tips
Trend Confirmation: Use oscillator position relative to zero line for primary trend bias
Signal Quality: Higher threshold values reduce signal frequency but increase accuracy
Multi-Timeframe: Combine with higher timeframe analysis for enhanced precision
Risk Management: Monitor Half Kelly % for optimal position sizing guidance
---
🏆 Professional-Grade Tool for Serious Traders
The Median Shifting Band Oscillator represents the evolution of technical analysis, combining time-tested statistical methods with modern computational power to deliver actionable trading insights across all market conditions.
💬 Questions? Comments? Share your them below! 👇
---
📝 Disclaimer: This indicator is for educational and informational purposes only. Past performance does not guarantee future results. Always conduct your own analysis and manage risk appropriately.
ALMA Shifting Band Oscillator | QuantMACALMA Shifting Band Oscillator | QuantMAC
🎯 Advanced Technical Analysis Tool Combining ALMA with Dynamic Oscillator Technology
The ALMA Shifting Band Oscillator represents a sophisticated fusion of the Arnaud Legoux Moving Average (ALMA) with an innovative oscillator-based signaling system. This indicator transforms traditional moving average analysis into a comprehensive trading solution with dynamic band visualization and precise entry/exit signals.
Core Technology 🔧
Arnaud Legoux Moving Average Foundation
Built upon the mathematically superior ALMA calculation, this indicator leverages the unique properties of ALMA's phase shift and noise reduction capabilities. The ALMA component provides a responsive yet smooth baseline that adapts to market conditions with minimal lag.
Dynamic Band System
The indicator generates adaptive upper and lower bands around the ALMA centerline using statistical deviation analysis. These bands automatically adjust to market volatility, creating a dynamic envelope that captures price extremes and potential reversal zones.
Normalized Oscillator Engine
The heart of the system transforms price action relative to the dynamic bands into a normalized oscillator that oscillates around a zero line. This oscillator provides clear visual representation of momentum and position within the established bands.
Visual Features 🎨
Multi-Pane Display Architecture
Primary oscillator plotted in separate pane for clarity
Dynamic band overlay on price chart with elegant fill visualization
ALMA centerline marked with distinctive styling
Customizable threshold lines for signal identification
Advanced Color Schemes
Choose from 9 professionally designed color palettes:
Classic series offering various aesthetic preferences
High contrast options for different chart backgrounds
State-based coloring that changes with market conditions
Candle coloring that reflects current oscillator state
Enhanced Visual Elements
Smooth gradient band fills for easy trend identification
Dynamic line thickness and styling options
Professional transparency settings for overlay clarity
Customizable threshold visualization
Signal Generation System 📊
Dual Threshold Architecture
The indicator employs two distinct threshold levels that create a sophisticated signal framework:
Long Threshold : Triggers bullish signal generation
Short Threshold : Activates bearish signal conditions
Intelligent State Management
Advanced state tracking ensures clean signal generation without false triggers:
Prevents redundant signals in same direction
Maintains position awareness for proper entries/exits
Implements crossover logic for precise timing
Flexible Trading Modes
Long/Short Mode : Full bidirectional trading capabilities
Long/Cash Mode : Conservative approach with cash positions during bearish conditions
Professional Analytics Suite 📈
Comprehensive Performance Metrics
Integrated real-time performance analysis including:
Maximum Drawdown percentage tracking
Sortino Ratio for downside risk assessment
Sharpe Ratio for risk-adjusted returns
Omega Ratio for comprehensive performance evaluation
Profit Factor calculation
Win rate percentage analysis
Half Kelly percentage for position sizing guidance
Total trade count and net profit tracking
Advanced Risk Management
Real-time equity curve tracking
Peak-to-trough drawdown monitoring
Downside deviation calculations
Risk-adjusted return measurements
Customization Options ⚙️
ALMA Parameter Control
ALMA Length (Default: 42) - Controls the lookback period for the moving average calculation. Lower values (20-30) create faster, more responsive signals but increase noise. Higher values (50-100) produce smoother signals with less false alerts but slower reaction to price changes.
ALMA Offset (Default: 0.68) - Determines the phase shift of the moving average. Values closer to 0 behave like a simple moving average. Values closer to 1 act more like an exponential moving average. 0.68 provides optimal balance between responsiveness and smoothness.
ALMA Sigma (Default: 1.8) - Controls the smoothness factor of the ALMA calculation. Lower values (1.0-2.0) create sharper, more reactive averages. Higher values (4.0-8.0) produce extremely smooth but slower-responding averages. Affects how quickly the ALMA adapts to price changes.
Source Selection - Choose between Close, Open, High, Low, or custom price combinations. Close price is standard for most analysis. HL2 or HLC3 can provide different market perspectives and reduce single-price volatility.
Oscillator Fine-tuning
Standard Deviation Length (Default: 27) - Determines the lookback period for volatility calculation. Shorter periods (10-20) make bands more reactive to recent volatility changes. Longer periods (40-60) create more stable bands that filter out short-term volatility spikes.
SD Multiplier (Default: 2.8) - Controls the width of the dynamic bands. Lower values (1.5-2.0) create tighter bands with more frequent signals but higher false signal rate. Higher values (3.0-4.0) produce wider bands with fewer but potentially more reliable signals.
Oscillator Multiplier (Default: 100) - Scales the oscillator for visual clarity. This is purely cosmetic and doesn't affect signal generation. Adjust based on your preferred oscillator range visualization.
Long Threshold (Default: 82) - Sets the level where bullish signals trigger. Lower values (70-80) generate more frequent long signals but may include weaker setups. Higher values (85-95) create fewer but potentially stronger bullish signals.
Short Threshold (Default: 50) - Determines where bearish signals activate. Higher values (55-65) produce more short signals. Lower values (35-45) wait for stronger bearish conditions before signaling.
Trading Mode Configuration
Long/Short Mode - Full bidirectional trading that takes both long and short positions. Suitable for trending markets and experienced traders comfortable with short selling.
Long/Cash Mode - Conservative approach that only takes long positions or moves to cash during bearish signals. Ideal for bull market conditions or traders who prefer not to short.
Display Customization
Color Schemes (9 Options) - Choose from Classic to Classic9 palettes. Each offers different visual contrast for various chart backgrounds and personal preferences.
Metrics Table Position - Place performance metrics in any of 6 chart locations: Top Left/Right, Middle Left/Right, Bottom Left/Right.
Show/Hide Metrics Table - Toggle the comprehensive performance analytics display on or off based on your analysis needs.
Date Range Limiter - Set specific start dates for backtesting and signal generation. Useful for testing strategies on specific market periods or excluding unusual market events.
Parameter Optimization Tips
Volatile Markets - Use shorter ALMA Length (25-35), lower SD Multiplier (2.0-2.5), and moderate thresholds
Trending Markets - Employ longer ALMA Length (45-60), higher SD Multiplier (3.0-4.0), and extreme thresholds
Sideways Markets - Try medium ALMA Length (35-45), standard SD Multiplier (2.5-3.0), and closer thresholds (75/55)
Higher Timeframes - Generally use longer periods and higher multipliers for smoother signals
Lower Timeframes - Opt for shorter periods and lower multipliers for more responsive signals
Practical Applications 💡
Trend Following
Identify and follow established trends using the dynamic band system and oscillator position relative to thresholds.
Momentum Analysis
Gauge market momentum through oscillator readings and their relationship to historical levels.
Reversal Detection
Spot potential reversal points when price reaches extreme oscillator levels combined with band interactions.
Risk Management
Utilize integrated metrics for position sizing and risk assessment decisions.
Technical Specifications 🔍
Calculation Methodology
The indicator employs sophisticated mathematical formulations for ALMA calculation combined with statistical analysis for band generation. The oscillator normalization process ensures consistent readings across different market conditions and timeframes.
Performance Optimization
Designed for efficient processing with minimal computational overhead while maintaining calculation accuracy across all timeframes.
Multi-Timeframe Compatibility
Functions effectively across all trading timeframes from intraday scalping to long-term position trading.
Installation and Usage 📋
Simple Setup Process
Add indicator to chart
Configure ALMA parameters for your preferred responsiveness
Adjust threshold levels based on market volatility
Select desired color scheme and display options
Enable metrics table for performance tracking
Best Practices
Use multiple timeframe analysis for context
Monitor metrics table for strategy performance
Adjust parameters based on market conditions
This indicator represents a professional-grade tool designed for serious traders seeking advanced technical analysis capabilities with comprehensive performance tracking. The combination of ALMA's mathematical precision with dynamic oscillator technology creates a unique analytical framework suitable for various trading styles and market conditions.
🚀 Transform your technical analysis with this advanced ALMA-based oscillator system!
⚠️ IMPORTANT DISCLAIMER
Past Performance Warning: 📉⚠️
PAST PERFORMANCE IS NOT INDICATIVE OF FUTURE RESULTS. Historical backtesting results, while useful for strategy development and parameter optimization, do not guarantee similar performance in live trading conditions. Market conditions change continuously, and what worked in the past may not work in the future.
Remember: Successful trading requires discipline, continuous learning, and adaptation to changing market conditions. No indicator or strategy guarantees profits, and all trading involves substantial risk of loss.
Best EMA FinderThis script, Best EMA Finder, is based on the same original logic as the Best SMA Finder I published previously. Although it was not the initial goal of the project, several users asked for an EMA version, so here it is.
The script scans a wide range of Exponential Moving Average (EMA) lengths, from 10 to 500, and identifies the one that historically delivered the most robust performance on the current chart. The choice to stop at 500 is deliberate: beyond that point, EMA curves tend to flatten and converge, adding processing time without meaningful differences in signals or outcomes.
Each EMA is evaluated using a custom robustness score:
Profit Factor × log(Number of Trades) × sqrt(Win Rate)
Only EMA lengths that exceed a user-defined minimum number of trades are considered valid. Among these, the one with the highest robustness score is selected and displayed on the chart.
A table summarizes the results:
- Best EMA length
- Total number of trades
- Profit Factor
- Win Rate
- Robustness Score
You can adjust:
- Strategy type: Long Only or Buy & Sell
- Minimum number of trades required
- Table visibility
This script is designed for analysis and optimization only. It does not execute trades or handle position sizing. Only one open trade per direction is considered at a time.
NYBREAKOUT by FliuxStrategy Concept
This strategy captures high-probability breakout moves by defining a tight 30-minute range during low-volatility hours and trading the first clear break beyond that range with a 2:1 reward-to-risk ratio.
Key Benefits
Simplicity: Clear, time-based range and mechanical entries/exits.
Defined R:R: Automatic 2:1 target ensures consistent risk management.
Time-filtered: Trades only the initial breakout of a calm, pre-session range.
How to Use
Add to Chart: Paste the Pine Script into TradingView’s Pine Editor, then click Add to Chart.
Backtest: Open Strategy Tester to review net profit, drawdown, win rate, and profit factor.
Optimize: Adjust stop-loss offset, R:R ratio, or session window parameters to suit different instruments or volatility regimes.
EMA Crossover Strategy with Take Profit and Candle HighlightingStrategy Overview:
This strategy is based on the Exponential Moving Averages (EMA), specifically the EMA 20 and EMA 50. It takes advantage of EMA crossovers to identify potential trend reversals and uses multiple take-profit levels and a stop-loss for risk management.
Key Components:
EMA Crossover Signals:
Buy Signal (Uptrend): A buy signal is generated when the EMA 20 crosses above the EMA 50, signaling the start of a potential uptrend.
Sell Signal (Downtrend): A sell signal is generated when the EMA 20 crosses below the EMA 50, signaling the start of a potential downtrend.
Take Profit Levels:
Once a buy or sell signal is triggered, the strategy calculates multiple take-profit levels based on the range of the previous candle. The user can define multipliers for each take-profit level.
Take Profit 1 (TP1): 50% of the previous candle's range above or below the entry price.
Take Profit 2 (TP2): 100% of the previous candle's range above or below the entry price.
Take Profit 3 (TP3): 150% of the previous candle's range above or below the entry price.
Take Profit 4 (TP4): 200% of the previous candle's range above or below the entry price.
These levels are adjusted dynamically based on the previous candle's high and low, so they adapt to changing market conditions.
Stop Loss:
A stop-loss is set to manage risk. The default stop-loss is 3% from the entry price, but this can be adjusted in the settings. The stop-loss is triggered if the price moves against the position by this amount.
Trend Direction Highlighting:
The strategy highlights the bars (candles) with colors:
Green bars indicate an uptrend (when EMA 20 crosses above EMA 50).
Red bars indicate a downtrend (when EMA 20 crosses below EMA 50).
These visual cues help users easily identify the market direction.
Strategy Entries and Exits:
Entries: The strategy enters a long (buy) position when the EMA 20 crosses above the EMA 50 and a short (sell) position when the EMA 20 crosses below the EMA 50.
Exits: The strategy exits the positions at any of the defined take-profit levels or the stop-loss. Multiple exit levels provide opportunities to take profit progressively as the price moves in the favorable direction.
Entry and Exit Conditions in Detail:
Buy Entry Condition (Uptrend):
A buy position is opened when EMA 20 crosses above EMA 50, signaling the start of an uptrend.
The strategy calculates take-profit levels above the entry price based on the previous bar's range (high-low) and the multipliers for TP1, TP2, TP3, and TP4.
Sell Entry Condition (Downtrend):
A sell position is opened when EMA 20 crosses below EMA 50, signaling the start of a downtrend.
The strategy calculates take-profit levels below the entry price, similarly based on the previous bar's range.
Exit Conditions:
Take Profit: The strategy attempts to exit the position at one of the take-profit levels (TP1, TP2, TP3, or TP4). If the price reaches any of these levels, the position is closed.
Stop Loss: The strategy also has a stop-loss set at a default value (3% below the entry for long trades, and 3% above for short trades). The stop-loss helps to protect the position from significant losses.
Backtesting and Performance Metrics:
The strategy can be backtested using TradingView's Strategy Tester. The results will show how the strategy would have performed historically, including key metrics like:
Net Profit
Max Drawdown
Win Rate
Profit Factor
Average Trade Duration
These performance metrics can help users assess the strategy's effectiveness over historical periods and optimize the input parameters (e.g., multipliers, stop-loss level).
Customization:
The strategy allows for the adjustment of several key input values via the settings panel:
Take Profit Multipliers: Users can customize the multipliers for each take-profit level (TP1, TP2, TP3, TP4).
Stop Loss Percentage: The user can also adjust the stop-loss percentage to a custom value.
EMA Periods: The default periods for the EMA 50 and EMA 20 are fixed, but they can be adjusted for different market conditions.
Pros of the Strategy:
EMA Crossover Strategy: A classic and well-known strategy used by traders to identify the start of new trends.
Multiple Take Profit Levels: By taking profits progressively at different levels, the strategy locks in gains as the price moves in favor of the position.
Clear Trend Identification: The use of green and red bars makes it visually easier to follow the market's direction.
Risk Management: The stop-loss and take-profit features help to manage risk and optimize profit-taking.
Cons of the Strategy:
Lagging Indicators: The strategy relies on EMAs, which are lagging indicators. This means that the strategy might enter trades after the trend has already started, leading to missed opportunities or less-than-ideal entry prices.
No Confirmation Indicators: The strategy purely depends on the crossover of two EMAs and does not use other confirming indicators (e.g., RSI, MACD), which might lead to false signals in volatile markets.
How to Use in Real-Time Trading:
Use for Backtesting: Initially, use this strategy in backtest mode to understand how it would have performed historically with your preferred settings.
Paper Trading: Once comfortable, you can use paper trading to test the strategy in real-time market conditions without risking real money.
Live Trading: After testing and optimizing the strategy, you can consider using it for live trading with proper risk management in place (e.g., starting with a small position size and adjusting parameters as needed).
Summary:
This strategy is designed to identify trend reversals using EMA crossovers, with customizable take-profit levels and a stop-loss to manage risk. It's well-suited for traders looking for a systematic way to enter and exit trades based on clear market signals, while also providing flexibility to adjust for different risk profiles and trading styles.
GG-ShotOverview:
The GG-Shot indicator is built on analyzing key price levels to identify breakouts in both directions of the market. It tracks range boundaries to identify entry points for long and short positions. This indicator includes additional filters to eliminate false signals and increase accuracy in low-volatility conditions. The primary goal of the indicator is to identify levels whose breakout may signal the start of a new trend.
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🔹 Key Features:
167 Trading Strategies: GG-Shot is equipped with 167 unique strategies, including two universal strategies suitable for most crypto assets. These strategies are adaptable to various market conditions and are beneficial for traders dealing with a wide range of assets.
Take-Profit Levels (TP1-TP4): The indicator provides four static take-profit levels, calculated based on market volatility and strategy configuration. Additionally, two dynamic take-profit levels are adjusted in real-time.
Long and Short Signals: The indicator generates clear long and short signals, helping traders stay prepared for upcoming market moves.
Advanced, Standard, Classic and Channel Modes:
- Advanced Mode: Adds extra trend lines that act as support and resistance levels, useful for entering, increasing, or closing positions. You can also trade between these levels using price movements within the range.
- Standard Mode: Uses pre-set limit-based take-profit levels specifically tailored for each strategy, ensuring a structured approach to trade exits.
- Classic Mode: In this mode, limit-based take-profit levels are not displayed. Profit is instead taken based on dynamic take-profit levels or support/resistance levels.
- Channel Mode: Transforms indicator into a channel trading mode, generating signals when the price touches the upper or lower boundaries of the channel, good for range-bound markets.
Oscillator Mode:
- Enable: Generates signals in both directions, independent of the primary trend.
- By Trend: Filters signals to only work in the direction of the current trend based on the primary indicator signal.
Filtering: The indicator includes two filtering methods:
- Volume Filter: Automatically adjusts the threshold for volume based on the specific asset, helping to filter out signals in low-volume conditions and ensuring higher-quality trades.
- Flat Market Filter: Reduces the number of signals in low-volatility or flat market conditions, improving overall accuracy during periods of market inactivity.
Support and Resistance Zones: Highlights key support and resistance levels on the chart to help traders identify important entry and exit points.
Real-Time Back-Test Panel: Available in both mobile and desktop versions, with only design differences. Both versions provide real-time performance data such as win rates, profit factor, and success rates for each take-profit level (TP1-TP4), along with detailed accuracy statistics for long and short trades.
🔹 How to Trade with GG-Shot:
When you receive a Long/Short signal from indicator, you have two primary options for entering a position:
Instant Entry: Enter immediately with a single entry if the following conditions are met:
- You observe that the trend is strong.
- There is no divergence on the rebound.
- Indicator signals are consistently pointing in one direction (with priority given to these signals).
- The Risk-to-Reward ratio is normal.
Split Entry or Pullback Entry: Enter in parts or wait for a pullback if the following conditions apply:
- The trend is flat or there is low market volatility.
- There are divergences on the rebound.
- The Risk-to-Reward ratio is negative or the signal appears abnormal.
Once you have entered the position, follow these guidelines for Take Profit and Stop Loss management:
Place limit Take Profit orders as follows:
50% of the position at the 1st take profit level (TP1).
25% at the 2nd take profit level (TP2).
15% at the 3rd take profit level (TP3).
10% at the 4th take profit level (TP4).
After reaching the first Take Profit (TP1), move the Stop Loss on the remaining position to breakeven to protect your capital.
Additional Trade Management with Oscillator Signals:
When TP1 is reached, and a green\red cross (oscillator function) appears against the trend direction, it is recommended to fully close the position. This signal indicates a potential reversal or a significant pause in the trend.
In certain cases, you can also open a small position in the opposite direction. Place your Stop Loss behind the nearest support or resistance level (using the Support and Resistance Zones or the Advanced Mode of GG-Shot). You can start taking profits at the nearest support or resistance levels or trend lines, while leaving part of the position open for further movement if a reversal signal appears. in most cases, price will bounce when there’s a combination of dynamic TP levels and the oscillator cross near important levels.
Re-entry Strategies:
If you see a cross from the trend line in the direction of the signal, you can re-enter the position with a more favorable Risk-to-Reward ratio. Use the nearest support or resistance level or a reverse signal as an entry point. After the price bounces off the Trend Line, move the Stop Loss to breakeven. Often, when the trend line follows the price, the price continues to track the line. This strategy can also be applied in combination with RSI , especially when there are divergences near the trend line.
Key Observations for Signal Strength:
In most cases, the trend line gives an early indication of future price movement. For example, if you receive a short signal , but the Trend Line is going up, the price is likely to touch the trend line before continuing downtrend. In such cases, it’s better to skip the signal or look for an entry from the Trend Line , especially when there are divergences supporting the direction of the signal.
Alternatively, when a short signal is accompanied by a falling trend line, the more it falls, the more significant the potential dump.
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🔹 How to Use:
Step 1: Add GG-Shot to your TradingView chart.
Step 2: Choose one of the modes:
Advanced, Standard, or Channel, and select a strategy from the 167 available options.
Step 3: Check Long/Short signals for entering positions, using the provided take-profit and stop-loss levels.
Step 4: Use the Back-Test Panel to assess the performance of the chosen strategy and adjust your approach based on real-time data.
Note: All trading involves risk, and past performance is not indicative of future results.
Athena Momentum Squeeze - Short, Lean, and Mean This is a very profitable strategy focusing on 15 minute intervals on the Micro Nasdaq Futures contracts. CME_MINI:MNQH2023
As this contract only keeps positions for on average about an hour risk is managed. At a profit factor of 3.382 with a max drawdown of $123 from January 1st to February 15. Looking back to Dec 2019 still maintains a profit factor of 1.3.
See backtesting: www.screencast.com
2019 backtesting: www.screencast.com
Based on the classic Lazy Bear Oscillator Squeeze with a number of modifications from ADX, MAs and adding fibonacci levels.
We like keeping strategies simple yet powerful, no completely where you can't understand your own trades.
Our team is always modifying and improving the strategy. Always open to collaborating on improving as there is no perfect strategy. www.screencast.com
Consolidation Breakout [Indian Market Timing]OK let's get started ,
A Day Trading (Intraday) Consolidation Breakout Indication Strategy that explains time condition for Indian Markets .
The commission is also included in the strategy .
The basic idea is ,
1) Price crosses above upper band , indicated by a color change (green) is the Long condition .
2) Price crosses below lower band , indicated by a color change (red) is the Short condition .
3) ATR is used for trailing after entry
// ═══════════════════════════════//
// ————————> TIME CONDITION <————————— //
// ═══════════════════════════════//
The Indian Markets open at 9:15am and closes at 3:30pm.
The time_condition specifies the time at which Entries should happen .
"Close All" function closes all the trades at 2:57pm.
All open trades get closed at 2:57pm , because some brokers dont allow you to place fresh intraday orders after 3pm.
NSE:NIFTY1!
// ═══════════════════════════════════════════════ //
// ————————> BACKTEST RESULTS ( 114 CLOSED TRADES )<————————— //
// ═══════════════════════════════════════════════ //
LENGTH , MULT (factor) and ATR can be changed for better backtest results.
The strategy applied to NIFTY (3 min Time-Frame and contract size 5) gives us 60% profitability , as shown below
It was tested for a period a 8 months with a Profit Factor of 2.2 , avg Trade of 6000Rs profit and Sharpe Ratio : 0.67
The graph has a Linear Curve with consistent profits.
NSE:NIFTY1!
Save it favorites.
Apply it to your charts Now !!
Thank me later ;)
