Highest Probability Trading System
Beating the market is a bold claim, but can it be done and on what scale?
In order to outperform a market, one must first understand how markets work. Asset prices can be qualified as trending or ranging. There can be small ranges within a trend and small trends within ranges. But the key factor that suggests where a chart is heading before it’s next destination is “liquidity”.
Let’s go ahead and establish our in-house definition of liquidity before proceeding:
Liquidity: The next swing low or swing high pivot on a chart, where price action is heading in order to trigger the most stop losses and liquidate most leveraged traders, before reaching its next destination.
The following figure depicts our definition of liquidity on a chart.
Since we’ve defined how markets move, then surely capitalizing on such movement can be profitable. Yet most participants think that assets move in random and that markets are unpredictable, that’s why most end up on the losing side.
In order to outperform markets, traders need a new set of eyes, they need eyes that can understand where liquidity is and do so without bias, then they need to countertrade the impulse of liquidity when it happens.
To further demonstrate how liquidity works, we’ve re-labeled that same chart just highlighting points of maximum liquidity.
As previously stated, markets move from one point of liquidity to the next, that’s why the majority fail at outperforming the market. They tend to buy high and sell low, then short-sell low to only end up covering at a higher price.
To further our thesis we categorized liquidity into two classes, major liquidity grabs, and minor liquidity grabs.
Based on our observation and those classifications we were able to extrapolate enough data from several asset classes to configure the optimal liquidity zones, both minor and major. Our hypothesis was if asset pricing moves from the point of maximum liquidity to the next, and we’re able to predict these liquidity zones, then we could hypothetically outperform each and every asset.
Once we applied our liquidity formula to the above chart, trading the asset became much more intuitive. Counter trading major liquidity zones is clearly the most profitable and highest probability route, then utilizing price action at minor liquidity zones to get an indication of the trend or impulse move’s strength can provide further insight into whether the impulse move was going to undergo continuation or failure.
Now that we established supporting data for one asset, our next obvious move was to test other asset classes. Do all assets move from one point of maximal liquidity to the next, and if they do on which scale?
Gold, Silver, and Copper all comply with our theory. The conclusion here is that most market participants are fooled by price action. They price-chase and get caught in the trap. The maximum liquidity theory has been validated on precious metals and crypto. Now let’s examine indices and equities.
ES1! S&P E-mini Futures trade based on liquidity on the 15minute chart,
And the SPY’s Daily, and 12hour charts do.
Amazon’s 3-minute chart and Google’s hourly chart comply with our maximum liquidity theory.
Our Liquidity case has been made, our hypothesis has been validated by several unrelated asset classes. But does this strategy work 100% of the time?
According to our findings, the strategy doesn’t work 100% of the time, which most market wizards will tell you, sometimes there simply isn’t a trade.
For backtesting, we’ll start with another unrelated asset, FedEx ticker “FDX”:
The FedEx 90 Minute chart, over a period of 1,317 days, when we apply our Maximum Liquidity formula we get 40 unique opportunity signals. Signals to either buy, sell, short-sell, hedge, or trade options.
With so many ways for market participants to approach these unique opportunities, we won’t highlight the profitability of each strategy, just the nature of the signals.
It appears that our Max Liquidity theory is profitable in trading ranges and during corrective phases. Maximum Liquidity is at the top and the bottom of trading ranges, at extremes below the previous low, and above previous range high. A long entry at the maximal liquidity swing low will allow participants a (buy low sell high) range trade. If the range was to break out, then one would have secured the absolute low, prior to a breakout into a trending market. Or conversely the extreme swing point high above the range prior to a bearish breakout.
If the participant was to long at the maximum liquidity low buy signal, then hedge the next sell signal and set proper stops. They would secure the range’s profits and allow the market to do the rest of the work.
If the asset was to breakout to the long-side or short-side, the participant would have captured excellent entries for either direction. This process removes all bias from the trade.
As a market starts trending, the theory of maximum liquidity becomes less and less profitable, participants might consider waiting for ranges to establish, then either range trade, hedge short at swing highs, add to their long position at swing lows, or just buy max liquidity lows, and sell max liquidity highs.
What started as an observation, we evolved into a chart liquidity pivot formula and then our team was able to convert all the data into a TradingView Script, that we named “Phantom”.
The name Phantom is due to how the indicator is able to project Phantom-like pivot levels. Where maximum liquidity is expected for a change in direction.
These specific reference points on the chart are represented by the top and bottom bands.
The outer bands act as boundaries and magnets, price action is likely to pivot from one end of the band to the next. From one liquidity zone to the polar opposite zone. Hence fooling bulls and bears equally, before heading to the next destination.
The formula isn’t perfect, at times price will breach the bands, and that’s either a breakout or a move to further liquidity before a quick turn in direction. Once price action clears extreme bands, the chart needs to be viewed at a larger or smaller time frame for a clearer understanding.
Inner midlines represent minor liquidity zones. Price action tends to chop at midlines on larger time frames and present great intra-day opportunities on smaller time frames.
Closes above the inner midlines during a bullish impulse indicates momentum is truly bullish, and that the next likely destination is the upper band. Closes below the inner midlines indicate that bears are in control, and a move towards the lower band is expected.
Strong rejection at midlines into whichever direction indicates a change in direction towards the previous liquidity zone.
Traders can choose to profit take either long or short positions at the next midline or wait for continuity until the next outer band. Profit-taking at midlines is the least risky approach.
The above chart demonstrates Phantom applied to Gold’s 2-hour Chart, and the charts below are for a comparative view of the larger and smaller time frames. Gold’s 4-hour and 30-minute charts.
Larger time frames will demonstrate a macro-scale view, and present fewer opportunities, but more profitable larger sings.
If one was to simply accumulate on Phantom’s buy signals on gold’s 4-hour chart, and do nothing, they’d currently be at a 20% profit in 6 months.
Smaller time frames will present many more opportunities, from day-trading to swinging and hedging choppy territory and small ranges.
Maximum liquidity zones happen at the extremes of trading ranges and mimic a false breakout. So what could seemingly be the ultimate drawback of Phantom is that the strategy would call for continued selling in an uptrend or continued buying in a downtrend. Which could lead to substantial losses if the fund manager isn’t aware of the shift in trend, nor is utilizing proper risk management practices.
Proper money and trade management practices call for hedging, and what was seemingly our strategy’s weakness has actually turned into a differentiating strength. Utilizing this strategy, a trader or fund manager can take advantage of maximum liquidity zones for hedging opportunities, at the extreme pivots of a trend.
Above are two examples on various scales 3–4 very profitable entries on $GOLD over a 35 day period, and 4 very profitable entries on the SPY over the course of 11 months.
Phantom would allow a fund manager or trader to take advantage of hedging a range, no matter the size of a range. Long to extreme swing low maximum liquidity zone, and hedge the swing high maximum liquidity zone. Once the trader-manager has hedged both ways, they can assume they’ve captured the range, and step away from the market. Unless they wanted to capitalize on intra-day opportunities and shorter time frame trades.
As the authors and creators of this strategy, we recommend utilizing Phantom for hedging, and never just trading one side of the market. Because as soon as an asset appears to be bearish and at maximum liquidity, that’s when it’s likely to turn back around. Same for a bullish asset. History is two-sided and so are markets, one can’t always win if they’re only trading one side.
“Buy low, sell high,” they say, and Phantom provides that and much more. Phantom is forward-thinking the indicator strategy that calculates maximum liquidity pivots on any scale and time frame.
Utilizing such a strategy allows all market participants to capitalize on major unique opportunities on any time frame for a higher probability outcome and larger returns.
Options traders can buy discounted out of the money call options on assets capitulating at maximum liquidity zones, and capitalize on the pivot in volatility.
The chart on the left demonstrates a unique Phantom buy opportunity on SLV, silver ETF.
During the sell-off our firm spotted the buy signal and opted for purchasing $16.50 SLV call options, that were 4 days away from expiry for $0.07
Over the course of the next 2 trading days, $SLV rallied to $16.50. We sold the calls while in the money and locked in a 98% profit in 2 days.
Trading the short side during an extended downtrend can be done utilizing Phantom’s midlines. Rigorous backtesting has demonstrated that rejections at midlines during a downtrend provide optimal short entries at a relative swing high.
Whether day trading swings or trading larger timeframe swings, Phantom provides the most optimal buying and selling opportunities.
Phantom has two variable settings that correlate the asset’s movement to its next maximum liquidity directional pivot.
Those variables can be modified by the user to properly backtest and trade the given asset on a given scale.
Scalp traders can seek a leveraged entry at a given signal and profit-take the next midline. Which according to our observations has been the highest probability outcome in all non-trending markets. But by doing so, they will not capitalize on the larger swing moves. But that’s what scalpers do.
Phantom is by far a superior hedging strategy. Highlighting extreme liquidity points of a range for traders to hedge both sides of a chart for a non-directionally biased position, in order to capitalize on the larger breakout.
Phantom was built with automation as the end goal. And our development team is applying finishing touches to the strategy’s automation, to any of the above parameters.
Automation backtests can be provided for any given asset for any period upon request.
Can the code be cracked?
Partially we believe that our Phantom strategy and the logic of taking advantage of maximal liquidity pivots is a step closer to beating all markets.
Phantom provides high probability entries for sizeable returns, at critical areas, where mainstream traders capitulate, Phantom indicates buy the dip.
Afterall Phantom counter-traded Peter Brandt’s gold sell signal for a fairly good return. After having signaled $GOLD bottom on our macro setting.
Author: George Saber