Please Note: You will need NinjaTrader Desktop to use Price Action Pivoter. Although Price Action Pivoter is designed primarily for $ES (E-mini S&P 500 Futures) and $NQ (E-mini Nasdaq 100), there is no restriction to use it on any other futures instrument. However, price action when combined with our unique pivot point strategy tends to work relatively better with index futures such as ES and NQ. Price Action Pivoter also provides money management, trade management, and risk management automation. You can set your money management, trade management, and risk and reward parameters and let our unique algorithmic trading system manage the entire trading process for you! Moreover, Price Action Pivoter includes advanced trailing peak threshold logic, which is ideal for Prop Traders. This feature is designed to potentially secure unrealized profits near peak prices since most prop trading firms calculate DD (drawdown) from the peak price of unrealized profits for each trade. You can learn more about Raffi’s background from his Opalesque TV interview with Matthias Knab, which is available here. Pinnacle Quant, LLC is a registered vendor and programmer with NinjaTraderEcosystem.
Here’s a list of what you need to use the Price Action Pivoter™:
1) Windows 10 or higher
2) Internet connection
3) NinjaTrader 8 Desktop (free download here)
4) Market data – such as NinjaTrader Brokerage, Interactive Brokers, TD Ameritrade, Kinetick, Rithmic, Coinbase, FXCM, CQG etc. If you don’t have market data, on a temporary basis, you can obtain free live market data from NinjaTrader.
♦ Pinnacle Quant, LLC is a registered NinjaTrader vendor, and is listed in the NinjaTrader Ecosystem.
♦ Download Price Range Grabber™ for NinjaTrader Desktop for FREE!
♦ Per CNBC, since 2019 80% of the stock market is on autopilot. Don’t trade manually against algo trading bots; doing so means your long-term chances of success are very low. Instead, use our unique NinjaTrader automated trading system and give yourself an edge.
If you’re looking for the best automated trading system for NinjaTrader, one that incorporates every element a trader needs to give themselves an edge in the market, then this is the one you’ve been searching for! This isn’t just another automated system or “algo” bot; it’s a comprehensive solution crafted for traders who demand precision, control, and a definitive trading edge. The original PriceActionPivoter™, beloved by clients trading $ES and $NQ, has evolved. This new version introduces a second strategy, allowing trading of any instrument with the quantitative edge usually reserved for hedge funds and CPOs.
By integrating a sophisticated LSTM neural network, coded in Python and delivered through standalone software that I’ve named MLPriceMapper™, this tool doesn’t just follow the market—it anticipates it! MLPriceMapper™ provides data-driven predictive insights into market direction and price ranges for any trading day, and for any futures instrument, offering an unparalleled advantage for in your trading endeavors.
This article will highlight the new benefits, discuss how to take advantage of MLPriceMapper™ to trade any futures instrument of your choice, including $ES, $NQ, $CL, $GC, $ZN, $6B, $FDAX, $NG, $6E, and others, and show you some of the fruits produced from this labor of love where I spent countless nights coding through until morning to produce this software that incorporates tidbits of input from many client’s that I’ve had the pleasure of working with over the years, both as a licensed and registered Commodity Trading Advisor, as well as an algorithmic developer software engineer.
In this article, I’ll also delve into the enhanced features of PriceActionPivoter™ V2, MidPivotPHL indicator that is included with this advanced trading suite of tool. I will show you how to utilize this comprehensive system to trade a variety of futures instruments, including $ES, $NQ, $CL, $GC, $ZN, $6B, $6E, $NG, and $FDAX. This software represents more than a technological achievement; it’s the embodiment of passion and dedication. Countless sleepless nights have been dedicated to programming, refining, and perfecting every aspect, informed by valuable insights from numerous traders I’ve had the privilege to work with. The feedback from traders has been instrumental in shaping the software, ensuring it includes the features and capabilities traders truly desire to make their trading journey worthwhile. Wearing two hats, one as a licensed Commodity Trading Advisor and the other as a software engineer, I have a unique understanding of what traders need to be more successful and how to deliver it to them. Get ready to discover the real-world advantages and potential returns that an investment in a trading system like this could bring to your trading career for many years to come!
Since this strategy is built on the NinjaTrader platform, which offers a variety of advanced tools such as backtesting, walk forward testing, and multi-objective optimization, you can hone in on the days and times you wish to trade by selecting your preferred hours. However, you’re likely going to want to backtest the Main System Strategy for nearly all RTH trading hours and for every RTH trading day (don’t worry I’m going to show you some hypothetical backtested results below). As such, set the trading hours from most of the RTH (regular trading hours) session (I do 6:30A to 12:30P), but please be sure to input your time zone’s start and stop times (e.g. for EST – 930 to 1530). Your trade will need some time to breathe, as most clients set their “Flatten Everything” time at or near the RTH close – I set mine 1 minute before RTH close at 12:59P PST.
The Main System Strategy is based on pivot points (both traditional and Fibonacci), as well as various other highly watched price levels (e.g. Globex Open, Globex Close, Prior Day Low, Prior Day High, Midpoint etc). Unlike most NinjaTrader built automated trading strategies, PriceActionPivoter™ is relatively unique in that it relies heavily on historical price action and how those prices reacted at well-knowns price levels to determine if it should enter a trade or not. Although the system requires a minimum of 1,500 bars to trade (1 minute bar time-frame), it is strongly recommended 30 days of data are loaded on the 1-minute chart for the system to make the optimal analysis prior to entering the trade.
No one can tell the future with any degree of certainty, but we do have past data, and we can systematically analyze how price acted near known price levels, and based on that data we can build models, based on how prices have repeatedly moved in the past. That is the cornerstone strategy that is running the Main System Strategy.
As you can see below, the UI (user interface) is straightforward and easy to use. After selecting PriceActionPivoterV2 as your strategy, you can select days, times, and desired trade direction (long and/or short) for the system to enter trades. On the chart the UI will look very similar to the below UI, which is taken from NinjaTrader’s Strategy Analyzer tool. There are two entry strategies: the first is the main system strategy, which I described above (see several years of backtests below), and the other is the MLPriceMapper™ based strategy, which I will discuss in Part II below.
Special Operations, which should be used in conjunction with the MLPriceMapper™ LSTM neural network forecasting software that I provide, is considered tactical trading, where you can select particular price levels for which you would like the system to enter trades. Once your trade is live, the system will fully manage the trade for you based on various money and trade management parameters you set in this powerful self-directed automated trading system.
As shown below, you can choose to have the system monitor up to 5 prices levels in each direction. In order for the system to enter a trade, the price levels you input must be crossed. In terms of a long trade, the cross above must touch the price and cross above it, along with two green bars (the final bar’s body must be bigger than its upper wick), and in terms of a short trade the cross must touch the price and cross below it, along with two red bars (the final bar’s body must be bigger than its lower wick). More on how you can easily choose price levels with assistance from MLPriceMapper™ below, but for now, here is how the UI looks:
Please note the directional prices 1 through 5 shown above; a common strategy used by price level traders is to select both directions allowing some room between each price level (in the case above, 5 points of room), and whichever price level gets crossed first the system will follow and enter that trade. Meaning, if the market touches a particular price that gets crossed, the system will go long; if the market touches a particular price and crosses below it, then the system will go short; it all depends on which price level gets crossed first.
However, that type of price level trading strategy is not very sophisticated. What makes a price level trading strategy truly sophisticated is using machine learning to determine, based on 6 to 8 months of data (remember, we’re predicting for the next X number of hours for a day trade, we’re not investing long term, so no need to have years and years of data, even though my system allows you to grab unlimited data from your data feed), how price reacted at well-known price levels, upon training a well-engineered LSTM model on that kind of valuable past data (more on how to easily grab that data within seconds by using BulkDataGrabber™ below), then the machine learning software can determine if price is potentially headed higher or lower from the time you fed it your data set. Moreover, the same LSTM model can be programmed to predict price ranges that one can expect for the very next RTH trading session – and that is exactly what I’ve programmed MLPriceMapper™ to do for you!
I’m going to discuss MLPriceMapper™ in more detail below. If you’ve made it this far, keep reading, because there’s a lot more exciting stuff that no trader should be without. Most traders go into battle armed with a slingshot (RSI, MACD, MAs Market Profile, gut feelings etc), when professional traders, hedge funds, and CPOs are armed with stealth war drones (past data, machine learning, systematic rules-based trade and money management). You can have everything they have at a tiny fraction of what they spend on their engineering and quant data teams (most of which is done to impress investors – you’ve probably heard them make claims like ‘we have 100 PhDs working on our machine learning system’).
Finally, PriceActionPivoter™ V2 has several trade management mechanisms in place to ensure every aspect of your trade is under full control based on your settings. Everything in parts I and II above were “offense” – now I’m going to discuss “defense.” Here are the ways you can have the PriceActionPivoter™ V2 system fully manage the trades for you:
There are other elements, such as the number of crosses price should make over well-respected pivot levels for the main strategy before a trade is executed. As you know, price does not typically move linearly; it typically likes to zig and zag through pivot levels, and each time it does, more momentum gets added at the price level as traders add long and short positions to see what happens next.
There is unique way that I have programmed the system to calculate these crosses, which I’d prefer to discuss in a live demonstration setting on a Zoom call, but suffice it to note, it works really nicely at identifying proper tradable price levels.
There’s also thresholds of entry above and below these pivot levels. In the PriceActionPivoter™ V2 system, I track the highs and lows of each cross price every time it crosses a price level. If the threshold is set at one (1), that means that the system will only enter the trade if the current price is higher than the highest price that crossed that pivot level for long trades, and vice versa for short trades; this is quite helpful in confirming price direction as much as systematically possible – here is what the UI looks like:
As mentioned above, MLPriceMapper™ is a standalone Python coded software that makes predictions based on the LSTM model. Incidentally, LSTM, which stands for Long Short-Term Memory is a type of recurrent neural network (RNN) perfectly suited for time-series data analysis.
In the MLPriceMapper™ model, the LSTM model is trained on a dataset containing various features of 1-minute bars, such as open, high, low, close prices, volume, RSI, and various pivot points. The goal of MLPriceMapper™’s LSTM model is to predict whether the closing price during the regular trading hours (RTH) will be higher or lower compared to the last known provided data the model was trained on. Now, the data is derived and automatically organized from another NinjaTrader based software that I provide in the suite of trading tools that comes with PriceActionPivoter™ V2 called BulkDataGrabber™.
As the name implies, BulkDataGrabber™ is a handy software that will grab all sorts of data from NinjaTrader’s Strategy Analyzer backtest, including open, high, low, close prices, volume, RSI, Bollinger bands, various SMAs, and all traditional pivot points, and it can do so concurrently usually in under 60 seconds for multiple futures instruments (you pick which instruments like ES, NQ, FDAX etc that you would like MLPriceMapper™ to be trained on), and creates neatly organized CSVs and automatically places them in the PriceActionPivoter_by_Pinnacle_Quant directory that it creates on your computer. You can then simply input the CSV filename in MLPriceMapper™ and it will get automatically get trained on that data to make a prediction of price for you! I will discuss BulkDataGrabber™ and provide a sample CSV for your review later on below. For now, here is where you would copy and paste the filename.csv into MLPriceMapper™:
MLPriceMapper™ then processes your CSV data file, then trains and tests itself with it by doing a total of 12 epochs. In each epoch, the entire dataset of the CSV is passed through the model in smaller subsets that are called batches. In essence, an epoch refers to the number of times the entire set of data (included in the csv file that you would generate via BulkDataGrabber™) is passed forward and backward through the LSTM network during the training process.
However, the LSTM model in MLPriceMapper™ is doing two things for you:
Here is what the MLPriceMapper™ output will look like:
Armed with this valuable information, you can now look at a chart, which will automatically have the MidPivotPointsPHL indicator loaded on the chart, and map MLPriceMapper™’s predicted price direction, the low price range and the high price range on top of the known pivot points as well as high and low prices from yesterday and two days ago, and you’ll be able to easily pick your entry prices, as well as place your price target, stop, and trailing stop to coincide with those levels. Of course, I’m referencing the second strategy available in PriceActionPivoter™V2, which is called “Special Operations.” If you’d rather let the system find the price levels for you, simply use the “Main System Strategy” instead. I can show you in detail how you can easily use “Special Operations” tactical trading strategy during a Zoom call. To set-up a Zoom call, please complete the form below. I offer those based on availability on a first confirm, first serve basis.
Here is what the chart, noting the low and high prices, looked like on 1/18/2024, the very same date of the analysis on MLPriceMapper™ as captured in the above image. Also, you will note that I ran the MLPriceMapper™ analysis on the morning of 01/18/2024 at 6:56A PST:
To train MLPriceMapper™ you need relevant data. Included in the suite of tools in PriceActionPivoter™ V2 is BulkDataGrabber™, which will automatically generate all the data you need to train the LSTM machine learning model in MLPriceMapper™. Once you’ve downloaded BulkDataGrabber™ from your PinnacleQuant.com control panel login area (along with all the other software like PriceActionPivoter™V2, and MLPriceMapper™), all you need to do is pick “BulkDataGrabber” from Strategy Analyzer’s Backtest area, click on “Grab All Data” and choose your instrument(s). If you have a list of instruments, you can pick them all concurrently; if you want to run it one instrument at a time, that works too. Under “Set Times” please ensure your premarket start and end times, and your RTH start and end times reflect your actual time zone’s Premarket and RTH start and end times. I’m in Los Angeles, so I’m in Pacific Standard Time. For example, if you are in Eastern Standard Time, you need to change the start and end times for both Premarket and RTH start and end times by 3 hours. Please don’t forget this step, as it is crucial for obtaining accurate data. You then pick a start date that is at least 6 months ahead of the end date, and finally, you simply click “Run” on the bottom right corner.
All of relevant data will be automatically organized in a CSV (see below for the actual csv file generated and download it so you can review the data it collected) and placed in your PriceActionPivoter_by_Pinnacle_Quant directory that BulkDataGrabber™ will instantly make for you. Here is what the UI for BulkDataGrabber™ looks like:
Here is the actual CSV the file above generated. Feel free to grab it and review the data for yourself:
Before glancing at the hypothetical backtests below, please bear in mind that in NinjaTrader any backtests that do not use minute bars, and any backtests whose strategy is not calculated on the close of the bar is a moot point. Meaning, if you see a backtest where Renko bars, Heiken Ashi, or tick bars were used, that entire backtest is erroneous and not reflective of the strategy at all. That is because all strategies that are backtested on NinjaTrader (and other platforms for that matter) require calculations where the strategy is calculated everything at the close of the bar, so that the OHLC (open, high , low, close) prices that every data broker stores is accurately measured. All of my strategies, not just PriceActionPivoter™ always use minute bars only, and all calculations are always done at the close of the bar to ensure that the hypothetical backtests are accurate and reflect real-world conditions. Moreover, please note that the below backtests are for:
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