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\n Retail forex brokers do not publish execution quality data. There is no public benchmark for matching latency, slippage asymmetry, spread widening, or last-look hold time. We're changing that. BEQI<\/strong> — the Broker Execution Quality Index — is an open methodology, an open Python toolkit, and a public spreadsheet anyone can contribute to. Measure your own broker in 30 minutes; the data accumulates into a benchmark.\n <\/p>\n Retail forex execution is the most opaque layer of the trading stack.<\/strong> Spreads are advertised. Commission is published. Everything between order send and confirmed fill — matching latency, slippage symmetry, last-look hold, requote frequency, spread widening at signal moments — is invisible to the trader and unpublished by the broker. Strategy backtests assume zero-latency fills (a problem we documented in detail in Why Latency Arbitrage Backtests Don't Survive in Production<\/a>). Broker reviews score on UX and withdrawals. Nobody measures the variables that actually decide whether a strategy survives in production.<\/p>\n<\/div>\n BEQI fixes one specific problem: there's no public methodology, no shared toolkit, and no benchmark dataset for retail forex execution quality. We've been measuring this internally at BJF Trading Group for years to build broker dialect support into SharpTrader Pro<\/a> and SharpTrader Optimizer<\/a>. Now we're open-sourcing the methodology and the code.<\/p>\n The point is not to publish a leaderboard naming brokers (that's a legal minefield and would compromise the integrity of the data). The point is to give every trader, quant, and broker-tech researcher the same standardized way to measure, the same Python scripts to run, and a public place to deposit anonymized results. Over months, the spreadsheet becomes a benchmark. Anyone can replicate the methodology, dispute it, or extend it.<\/p>\n If 10,000 traders measure their broker's execution quality with the same open methodology<\/strong>, the result is a public benchmark that no individual broker can deny, no review site can fake, and every strategy developer can use to allocate capital with eyes open.<\/p>\n<\/div>\n Execution quality is not a single number. BEQI decomposes it into five orthogonal dimensions, each independently measurable from a trading statement (and, for two of them, from a tick stream paired with the statement):<\/p>\n Each dimension is independently meaningful. A broker can have low matching latency but high slippage asymmetry, or vice versa — the combinations tell different stories about how the broker is actually behaving. The toolkit reports each dimension separately and a composite BEQI score (weighted) at the end.<\/p>\n You need three things: (a)<\/strong> a trading statement with at least 200 fills covering at least one trading week, (b)<\/strong> Python 3.10+ on any machine, (c)<\/strong> 30 minutes. The toolkit handles the rest.<\/p>\n The toolkit accepts three input formats:<\/p>\n If your platform doesn't expose send-time (the moment your strategy decided to send the order), you can still measure dimensions 2 and 5 (slippage asymmetry and requote rate) from a basic statement. Dimensions 1, 3, and 4 require a richer log.<\/p>\n The toolkit auto-detects the input format. The report includes the five dimension scores, the composite BEQI, sample-size diagnostics (lower N = lower confidence), and a tier classification (1 through 4, see below). Run it on more data to tighten the confidence intervals.<\/p>\n The toolkit never sees your account number, account balance, P&L, or position sizes. It only consumes timestamps, prices, requested-vs-filled deltas, and event types. The output JSON contains the BEQI scores, sample size, currency pairs covered, and time period — nothing identifying. You can submit it to the public spreadsheet anonymously.<\/p>\n<\/div>\n Based on internal measurements across the brokers SharpTrader Pro supports, here are the empirical tiers. These are starting baselines — the public spreadsheet will refine them over time.<\/p>\n Tier 1 · Institutional ECN<\/p>\n Tier 2 · Fast retail \/ fast bridge<\/p>\n Tier 3 · Standard retail<\/p>\n Tier 4 · Problematic<\/p>\n Tier classification matters because the same trading strategy will produce wildly different live results across tiers. A latency arbitrage<\/a> strategy that's profitable on Tier 1 collapses on Tier 3. A trend-following strategy that holds positions for hours is roughly indifferent between Tier 1 and Tier 3 but suffers visibly on Tier 4. Knowing your broker's tier before you commit capital is the entire game.<\/p>\n The spreadsheet is the long-term asset. Anyone can contribute one row per audit. Each row contains the five dimension scores, the composite, the sample size, the currency pairs covered, the time period, and (optionally, voluntarily) the broker name. We expect — and welcome — anonymous submissions where the broker is left blank<\/strong>; over time, even those produce useful aggregate distributions of execution quality across the retail forex industry.<\/p>\n
\n Browse the public spreadsheet<\/a>\n <\/div>\n
\n 3<\/strong> input formats supported<\/span>
\n MIT<\/strong> license<\/span>
\n ~30 min<\/strong> to first audit<\/span>\n <\/div>\n<\/div>\n
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\n<\/p>\nWhy a public broker audit didn't exist before<\/h2>\n
The BEQI thesis in one sentence<\/h3>\n
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\n<\/p>\nThe five BEQI dimensions<\/h2>\n
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\n \nBEQI dimension<\/th>\n What it measures<\/th>\n Unit<\/th>\n Source<\/th>\n Better is…<\/th>\n<\/tr>\n<\/thead>\n \n 1. Matching latency<\/td>\n Median time from order send to confirmed fill<\/td>\n milliseconds<\/td>\n Statement timestamps + send-time log<\/td>\n Lower<\/td>\n<\/tr>\n \n 2. Slippage asymmetry<\/td>\n Ratio of negative-slippage fills to positive-slippage fills<\/td>\n ratio (1.0 = symmetric, >1.0 = against trader)<\/td>\n Statement: requested vs filled price<\/td>\n Closer to 1.0<\/td>\n<\/tr>\n \n 3. Spread widening factor<\/td>\n Spread expansion at signal moments vs baseline<\/td>\n multiplier (1.0 = no widening, 3.0 = 3x widening)<\/td>\n Tick stream around order timestamps<\/td>\n Closer to 1.0<\/td>\n<\/tr>\n \n 4. Last-look hold time<\/td>\n Median delay between matched timestamp and confirmation<\/td>\n milliseconds<\/td>\n FIX log or extended statement<\/td>\n Lower \/ zero<\/td>\n<\/tr>\n \n 5. Requote rate<\/td>\n Requotes per 100 orders<\/td>\n count per 100<\/td>\n Statement requote events<\/td>\n Lower \/ zero<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
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\n<\/p>\nHow to run the audit on your own broker (30 minutes)<\/h2>\n
Step 1 \u2014 Export your statement<\/h3>\n
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order_id, send_time, fill_time, requested_price, filled_price, side, volume<\/code> — for everyone else, including custom logs<\/li>\n<\/ul>\nStep 2 \u2014 Install and run<\/h3>\n
\n$ cd forex-broker-audit-toolkit
\n$ pip install -r requirements.txt
\n$ python beqi.py –input my_statement.html –tick-data eurusd_ticks.csv –output report.json<\/div>\nbeqi.py<\/code> dispatches to the per-dimension analyzers, prints a summary, and writes a JSON report you can submit to the public spreadsheet.<\/p>\nStep 3 \u2014 Review the report<\/h3>\n
Anonymity by default<\/h3>\n
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\n<\/p>\nReading your results: the four BEQI tiers<\/h2>\n
BEQI 85–100<\/h3>\n
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BEQI 65–85<\/h3>\n
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BEQI 40–65<\/h3>\n
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BEQI 0–40<\/h3>\n
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\n<\/p>\nThe public BEQI spreadsheet<\/h2>\n
\n Open in new tab →<\/a>\n <\/div>\n