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by Taylor Ireland
In this article, we examine the order flow and limit order book data in the WTI futures market that provides insight into the sell-side defense near key resistance and subsequent price discovery lower for Tuesday, September 3, 2019's auction. Market structure and order flow provide insight into the actual transactional activity of a dual-auction's price discovery process. Analysis of this data provides real-time insight into the actions of market participants, providing a dynamic dataset to inform one's risk management process.
Position: N/A
Taylor Ireland
Sharedata Futures, Inc. provides historical data mining and visualization for the benchmark NYMEX Energy Futures Markets.
Sharedata combines structural analysis of the market generated data, dynamic systems analysis, and Bayesian causal inference techniques favored by the scientific and intelligence communities to provide a robust framework for addressing the uncertainty and risk in energy pricing.
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DAY TRADING
WTI Weekly: Dual Probability Prevents Cognitive Bias Regarding Trend Direction
10/08/19 06:06:42 PMby Taylor Ireland
In this article, we examine the order flow and limit order book data in the WTI futures market that provides insight into the sell-side defense near key resistance and subsequent price discovery lower for Tuesday, September 3, 2019's auction. Market structure and order flow provide insight into the actual transactional activity of a dual-auction's price discovery process. Analysis of this data provides real-time insight into the actions of market participants, providing a dynamic dataset to inform one's risk management process.
Position: N/A
| September 3, 2019: The structural framework provided in Sharedata's Daily Dashboard for Tuesday's, September 3 auction assigned the highest probability for price discovery higher based on a buy excess development in the prior auction. Markets are probabilistic, not deterministic. This fact requires conceptualizing (at minimum) dual probability paths in order to avoid destructive, cognitive bias to a single trade premise. While our daily dual probability path assigned the primary expectation to price discovery higher, it also provided a secondary path should the key level (resistance at 55.34s in this case) hold. Statistical support targets were noted: 53.59s/52.82s/52.05s, respectively. Qualitatively, these levels had held as resistance 61%, 91%, and 91% of recent market auctions (respectively) based on the market generated data. This data provided a secondary sell-side framework (provided key resistance held) with a probable support "zone" between 53.59s-52.82s. This type of daily framework, while useful, is more effective when using live order flow and structural analysis to confirm or negate the most probable daily inference. |
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| Figure 1. WTI Daily Dashboard For Sep 3, 2019. |
| Graphic provided by: Sharedata Futures, Inc.. |
| |
| Following the US Labor Day holiday, price discovery lower developed in Tuesday's Globex auction near Friday's key resistance, 55.34s. Low volume selling interest emerged there, driving price lower to 56.63s at Tuesday's Average Daily Range (ADR) low target through the London auction. A structural Gap Lower open developed. Based on the market generated data, Gap Lower opens have asymmetric potential for price discovery lower. Congruent with that structural fact, the ADR low did not provide asymmetric statistical potential for holding as support. Selling interest emerged upon the NY open, driving price lower, achieving the stopping point low, 52.84s, at Tuesday's 1st Standard Deviation (1SD) low target. As noted earlier, the 1SD low had held in 91% of recent auctions, based on the market generated data, providing asymmetric potential to hold as support. Sellers were trapped there amidst buying interest, driving price higher in retracement to 54s into the NY close. |
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| Figure 2. WTI Daily Auction Sep 3, 2019. |
| Graphic provided by: NinjaTrader. |
| |
| The practice of analyzing market structure developments is best accomplished with the use of order flow and limit order book (LOB) analysis. In short, plotting the buy and sell transactions of significance (in this case volume equal to or greater than 150 contracts) as well as the resting limit bids and offers (equal to or greater than 75 contracts) allows us to see both the actions and intent of larger participants (who significantly affect price). |
| In this week's analysis, the key market structure development was the sell-side defense of key resistance during the Globex auction resulting in a Gap Lower open. This development was indication and warning that the primary daily inference (price discovery higher) had failed and focus should be directed to the secondary inference. While price discovery lower did develop, it was known from the market generated data that a potential support area could develop around 52.84s. This is in fact what occurred. The statistical framework provided support targets which the market auctioned to intraday. Following the Globex sell-side auction and Gap Lower open, the trend of the LOB shifted into an upward trend at Sharedata's 1SD Low target as sellers trapped amidst buying interest, indicating the buy programs were dominant in this auction amidst a retracement rally into the NY close. |
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| Figure 3. WTI Daily Limit Order Book Sep 3, 2019. |
| Graphic provided by: NinjaTrader. |
| |
| The market structure negated the primary buy-side inference early in Tuesday's auction while providing statistical support targets as potential destinations. The order flow and limit order book data confirmed the buy-side shift later in the NY auction at key statistical support. The daily framework identified statistical support targets and their qualitative potential to hold as resistance. This confluence of structural, statistical, and order flow data provided insight into the auction's outcome based not on lagging fundamental data or media hype but rather structural formation and transactional behavior of significant quantity that drives price discovery. |
Sharedata Futures, Inc. provides historical data mining and visualization for the benchmark NYMEX Energy Futures Markets.
Sharedata combines structural analysis of the market generated data, dynamic systems analysis, and Bayesian causal inference techniques favored by the scientific and intelligence communities to provide a robust framework for addressing the uncertainty and risk in energy pricing.
| Title: | Founder |
| Company: | Sharedata Futures, Inc. |
| Dallas, TX | |
| Website: | www.sdfanalytics.com |
| E-mail address: | support@sdfanalytics.com |
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| Sharedata Futures, Inc. has not added any product or service information to TRADERS' RESOURCE. | |
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