Recovery and correction notice
The owner supplied two shared conversations containing the previously published article, titled Order Flow Imbalance (OFI) as a Short-Term Price Predictor on Nasdaq-100 Futures. The second includes a copied publication header dated 21 December 2025. That date is source-reported; an original WordPress backup or independently verified publication timestamp is unavailable. This corrected edition restores its existing URL on 27 October 2026. It preserves the subject and explains the method, while withdrawing claims that the recovered text could not substantiate. The source conversations are retained privately as provenance; this is not a new experiment or an exact WordPress restoration.
The underlying research and its limits
Cont, Kukanov and Stoikov, The Price Impact of Order Book Events (2014; earlier working paper linked below), study NYSE TAQ observations for 50 US stocks. They report a short-interval relationship between price changes and order flow imbalance at the best quotes, with impact related inversely to depth. That finding motivates investigation. It does not itself validate a Nasdaq-100 futures strategy, any particular data provider, or profitability after costs. The original article transferred the idea to NQ without supplying an independently reproducible NQ study.
What OFI measures
OFI accounts for changes in displayed supply and demand through time. A static quote-size imbalance compares bid and ask quantities at one instant. OFI instead accumulates signed quote events: additions, removals and changes in the best prices. A cancellation can change the visible book without a trade. Consequently, executed trade volume alone does not represent all the information used by this definition. These measures answer different questions; no universal superiority is established here.
Best-bid event accounting
Let b and q_b denote the current best bid price and displayed quantity, and prev their immediately preceding valid observation. If b rises, the bid contribution is +q_b. If b is unchanged, it is q_b minus q_b_prev. If b falls, it is -q_b_prev. At the ask, let a and q_a be the corresponding variables: if a falls use +q_a, if it is unchanged use q_a minus q_a_prev, and if a rises use -q_a_prev. The event OFI contribution is bid contribution minus ask contribution. Summing those contributions over a declared interval produces the interval OFI. A reproducible implementation must define how session starts, resets, invalid quotes and missing predecessor events are handled.
Depth and the price-change equation
A useful descriptive specification is price change = beta times OFI + residual, with beta estimated for a declared interval and depth regime. The earlier text wrote beta as 1/D. That shorthand is an intuition about depth, not an identified universal coefficient for NQ. Price units, quantity units, normalization and the observation window must be fixed before estimating a slope. Most importantly, explaining price changes in the same interval as the measured flow is not the same as predicting a future executable return.
Feed-provider claims withdrawn
The recovered article assigned predictive R-squared ranges of 0.45-0.55 to one provider and below 0.10 to another. No dataset, regression specification, reproducible benchmark or source supporting those comparisons was present. Those figures are withdrawn, and this edition makes no provider ranking or recommendation. The claim that a particular retail prop account necessarily supplies direct market-by-order data or unrestricted API access was also unsupported and is withdrawn. Feed entitlements and sequence completeness would have to be checked against the actual contracted product and captured messages.
Snapshots, event feeds and order-level data
The best-quote accounting above uses a sequence of best bid and ask updates; it does not logically require individual order identifiers. A study of queue position or order lifecycle asks additional questions. Aggregation, dropped events and timestamp ordering may change the measurement, so a future study must document what was actually observed. Do not infer feed suitability from a provider name. Record sequence gaps, exchange and receipt times, depth coverage, update semantics and the reconstruction rules used by the chosen experiment.
Implementation status and a testable next step
The source included a pandas/NumPy vectorization sketch using shifted prices and sizes and conditional event contributions. It supplied no unit-test evidence, representative replay benchmark or end-to-end latency measurement. The claimed throughput of 50,000 updates per second and the production-ready label are withdrawn. No runnable NQ engine is released by this recovery. Before a new experiment, preregister a data snapshot and its rights, chronological partitions, event ordering, forecast horizon, trial budget, cost model and fill rules. Unit-test bid and ask price changes, unchanged quotes, resets, gaps and session boundaries. Keep any validation period untouched until the protocol is fixed.
From a signal to an executable claim
A future forecast must use information available before the hypothetical order is sent. Queue position, latency, spread, fees and adverse selection must enter the execution model, with assumptions clearly separated from observed fills. Report comparison baselines and all attempted variants, including failed screens. Neither this restored note nor the academic relationship supplies evidence of an adopted live strategy. There is no new NQ result, provider endorsement, affiliate offer or immediate-deployment recommendation attached to this page.