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WTI Weekly: In Times Of Uncertainty A Dual Probability Path Is Important In Risk Management

06/03/20 04:39:22 PM
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 rotation between resistance and support in Monday, March 30, 2020'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.

Security:   WTI
Position:   N/A

March 30, 2020:

The structural framework provided in Sharedata's Daily Dashboard for Monday, March 30 was for price discovery higher. Our daily dual probability path assigned the primary expectation for price discovery higher, noting statistical resistance targets were: 23.85s/24.82s/25.79s, respectively. Qualitatively, these levels had held as resistance 65%, 75%, and 95% of recent market auctions (respectively) based on the market generated data. This data provided a buy-side framework with a probable resistance "zone" between 23.85s-24.82s. As discussed frequently, markets are probabilistic not deterministic. Within this framework, the secondary expectation was for price discovery lower with a probable support "zone" between 19.17s-18.20s. 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. The failure of the market at key resistance early in Monday's auction was structural indication that the primary expectation was invalidated.

Figure 1. WTI Daily Dashboard 30 Mar 2020.
Graphic provided by: Sharedata Futures, Inc..
Heading into Monday's auction, sell excess had developed at key resistance. An aggressive price discovery lower to 19.92s ensued in Sunday's Globex auction. Sellers trapped as buy excess halted the selloff. Balance then developed, 20.80s-19.95s, through London trade into the NY open. Balance continued in the NY auction before selling interest emerged, 20s, driving price lower to 19.27s near Monday's Average Daily Range Low Target. Buying interest emerged there amidst buy excess, halting the sell-side into Monday's NY close.

Figure 2. WTI Daily Dashboard 27 Mar 2020.
Graphic provided by: Sharedata Futures, Inc..
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).

Figure 3. WTI Daily Auction 30 Mar 2020.
Graphic provided by: NinjaTrader.
In this week's analysis, although the primary expectation was for price discovery higher, the market initially saw an aggressive sell-side rejection at the key resistance level, 21.90s. Subsequently, balance developed as price discovery up and down developed between areas of large bid and offer liquidity in the LOB. Price discovery lower ultimately continued as selling interest absorbed large bid liquidity at/near 20s, driving price lower to 19.27s near Monday's Average Daily Range Low Target. Buying interest emerged there amidst excess and large bid liquidity in the LOB into the NY close.

Figure 4. WTI Limit Order Book 30 Mar 2020.
Graphic provided by: NinjaTrader.
The initial sell-side rejection at key resistance resulted in a selloff toward key statistical support where market structure indicated (via the buy excess) the selloff had terminated. The daily framework identified statistical resistance targets and their qualitative potential to hold as resistance. The failure at key resistance, order flow, and limit order book data then indicated the primary expectation had been invalidated, thus favoring sell-side activity. 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.

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.

Title: Founder
Company: Sharedata Futures, Inc.
Dallas, TX
E-mail address:

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