Path Of Least Resistance
I am still busy structuring my multi-pivots tutorials. It is a very difficult task because the methodology is mostly practice-based. One could write full books about how to make a golf swing or how to play a Chopin sonata, only hours, days and years of practice could actually make you a better player. Another difficulty for teaching is that most of what could be quantified and optimized, thanks to the initial help of Dr. K and Pascal, will be kept proprietary. The unfortunate experience in the VIT group that led to abusive external commercial use of my work without my consent forces me to do so. The quantified elements of the methodology are now an essential foundation for the risk management system of the robots setups. I will therefore keep the exclusivity of most tutorials for the early birds who will subscribe to the robots in mid-June.
In the meantime, I want to give you some introduction on how the multi-pivots can help you anticipate the path of least resistance day after day.
The formulas for calculating pivots, supports and resistances are as follows:
Resistance 3 = High + 2*(Pivot - Low)
Resistance 2 = Pivot + (R1 - S1)
Resistance 1 = 2 * Pivot - Low
Pivot Point = ( High + Close + Low )/3
Support 1 = 2 * Pivot - High
Support 2 = Pivot - (R1 - S1)
Support 3 = Low - 2*(High - Pivot)
We use six timeframes for the pivots and support/resistance levels:
Daily (D), Weekly (W), Monthly (M), Quarterly (Q), Semester (S) and Yearly (Y).
Additionally, we use two moving averages: the 50-day and the 200-day moving averages.
Each level is given a weight (or strength), proportional to the timeframe it covers, as follows:
Daily: 1
Weekly: 2
Monthly: 3
50 dma: 4
Quarterly: 5
Semester: 6
200 dma: 7
Yearly: 8
The next step is to locate clusters of support and resistance for the next trading day, within a realistic volatility multiple of the last close based on the last ATR% readings. (This is proprietary; please don’t ask for the details!). Once the clusters have been identified, we simply add up the weights of all levels within each cluster.
Below are today’s clusters results for IWM.
Cluster Strength
First Resistance Cluster: 82.606:83.213 11
Second Resistance Cluster: 84.086:85.313 8
First Support Cluster: 81.803:81.076 9
Second Support Cluster: 80.173:78.946 14
Our interest goes primarily to the first resistance and support clusters which are drawn as a white rectangle in the attached chart with their respective cluster resistance strength of 11 and cluster support strength of 9. The path of least resistance for the nearest clusters is slightly to the downside with a resistance-to-support strength of 11-to-9. This is not a big edge in front of a strong intraday money flow, but a neutral to negative intraday money flow will alert us of a potential reversal within the first resistance cluster.
At today’s open, IWM can move freely between the two clusters limits of 82.61 and 81.80 without testing the first significant resistance or support cluster areas. It will be day-neutral as long as it stays there.
Another part of the methodology will use these clusters to determine the optimal 3:1 reward-risk ratio buy and sell entries for the day, independently of market direction or money flow. These are the same as used by the robots. Today’s optimal buy point is 82.14 and the optimal short point is 82.82.
Based on the robot conclusions, I have included the “most probable” 3-day targets for long and short trades. The evidence that should jump at you all at once is that today’s recommended short entry limit at 82.82 is just below the “most probable” 3-day long target at 82.94, near the 50-day moving average (83.16) -so important to institutional investors- and most importantly, still well below the trailing stop (83.44) of the current robot position. In the absence of a very strong intraday 20 DMF, if IWM reaches that area, it will provide an optimal short entry as longs get exhausted within the strong resistance cluster. Only a sustained incoming buy order flow from institutions could prevent market makers from taking advantage of the weak buyers.
Billy
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