Technical Indicators#
Technical indicators translate raw price and volume data into quantitative signals for entry, exit, and timing decisions. The research here spans the oldest tools in systematic trading — moving averages, RSI, Donchian channels — as well as rigorous empirical tests of whether these signals contain genuine predictive content once data-mining bias and transaction costs are accounted for. A recurring tension is between the simplicity that makes these indicators robust and the difficulty of distinguishing real edge from overfitting in historical tests.
This collection covers profitability tests of simple technical rules in Bitcoin markets, RSI as both an overbought-momentum signal and a counter-intuitive trend continuation signal, SMA and EMA crossover performance in crypto, volatility-normalized MACD, seasonality effects tied to calendar effects and time-of-day, and breakout strategies including opening-range and Donchian channel approaches. Several entries provide practical implementations with backtested equity curves rather than purely academic evaluations.
Related topics include Trend Following for moving-average-based trend systems, Momentum for return-based momentum signals distinct from indicator-driven approaches, and Backtesting & Research Methodology for frameworks for validating whether observed indicator edges are genuine.
Are Simple Technical Trading Rules Profitable in Bitcoin Markets?#
This paper empirically evaluates whether simple technical trading rules — moving average crossovers, filter rules, and momentum strategies — generate statistically significant profits in Bitcoin markets. The study applies these classic technical analysis rules to Bitcoin price data and tests their performance against appropriate benchmarks, addressing whether the well-documented technical trading profits in traditional markets extend to the newer cryptocurrency market.
Why Overbought RSI Readings Signal Continued Momentum in Crypto#
Research Article #67 from Trading Research Hub. Challenges the conventional interpretation of the Relative Strength Index (RSI) by testing a contrarian approach where traditional “overbought” readings are used as buy signals rather than sell signals. The study examines whether high RSI values in trending crypto markets actually predict continued momentum rather than reversals.
The backtesting results demonstrate that in strongly trending crypto markets, high RSI readings often signal persistent momentum rather than imminent reversals. This counter-intuitive finding aligns with the broader momentum literature showing that strong assets tend to continue strengthening, challenging the typical retail trader assumption that overbought conditions automatically warrant selling.
By Pedma.
SMA Crossovers in Crypto: Why Simpler Trend Signals Outperform Complex Models#
Research Article #66 from Trading Research Hub. Demonstrates the effectiveness of simple moving average (SMA) crossover strategies in cryptocurrency markets. The study makes the case that simplicity in signal design often leads to more robust out-of-sample performance than complex multi-parameter models.
The article tests various SMA crossover configurations on crypto market data, evaluating performance across different lookback periods and market conditions. The results reinforce the principle that simple, well-understood trading rules with few parameters are less likely to be overfit and more likely to maintain their edge in live trading.
By Pedma.
EMA Rate-of-Change as a Trend Signal: 3,906% Returns with Controlled Drawdowns#
Research Article #59 from Trading Research Hub. Presents a data-driven trend-following strategy using EMA derivatives that generated 3,906% total return with a maximum drawdown of just -27.25%, compared to the market’s -92.58% drawdown. The strategy achieved an annualized return of 56.96% with a Sharpe Ratio of 1.5002.
The EMA-derivative approach captures crypto trends through the rate of change of exponential moving averages rather than simple crossovers, providing earlier trend detection while filtering out noise. The article demonstrates through rigorous backtesting that consistent profitability in cryptocurrency markets is achievable through disciplined systematic trading that manages volatility as a core feature.
By Pedma.
A Counter-Intuitive RSI Strategy Delivering 602% Returns at Half the Drawdown#
Research Article #60 from Trading Research Hub. Presents a systematic RSI-based trading strategy that achieved 602.91% total return while limiting maximum drawdown to -33.22% compared to the market’s -92.58%. The strategy delivered a Sharpe Ratio of 0.9925, significantly outperforming benchmark risk-adjusted metrics.
The approach transforms traditional RSI interpretations by using a methodical, counter-intuitive framework that turns market volatility from a threat into an opportunity. Rather than relying on complex algorithms or high-frequency trading, the strategy demonstrates how systematic application of a well-tested RSI variant can capture meaningful crypto upside while maintaining remarkably lower risk metrics.
By Pedma.
Turn-of-the-Month Seasonality Effect in Cryptocurrency Returns#
Research Article #45 from Trading Research Hub. An in-depth exploration of the turn-of-the-month seasonality effect in cryptocurrency returns. Drawing inspiration from well-documented calendar anomalies in traditional equity markets, the study tests whether similar patterns exist in crypto.
The article examines historical crypto price data to determine if returns cluster around specific calendar dates, and whether this effect is statistically significant enough to be exploited by a systematic trading strategy. The practical implications for timing entries and exits based on calendar effects are tested against benchmark buy-and-hold performance.
By Pedma.
Testing the Sell-in-May Seasonal Pattern in Bitcoin#
Research Article #27 from Trading Research Hub. Tests the “Sell in May and Go Away” seasonal anomaly in Bitcoin. This well-known traditional finance pattern suggests that equity returns are significantly lower during the May-October period compared to November-April, and the article examines whether it holds in cryptocurrency markets.
The article provides a systematic backtesting of seasonal trading rules on Bitcoin data, evaluating whether adjusting exposure based on this calendar effect would improve risk-adjusted returns. The study adds to the body of research exploring which traditional market anomalies transfer to the structurally different cryptocurrency market.
By Pedma.
Evaluating RSI as a Momentum Signal for Bitcoin and Altcoins#
An article examining the validity and usefulness of the Relative Strength Index (RSI) as a momentum signal for Bitcoin and altcoin trading models. The article addresses common misconceptions about how RSI should be interpreted and used in systematic trading.
The article measures the actual signal content of RSI through systematic testing, evaluating its effectiveness as a momentum indicator rather than the traditional overbought/oversold interpretation favored by retail traders. By analyzing how RSI values correlate with subsequent returns across crypto assets, the study provides data-driven guidance on whether and how to incorporate RSI into a quantitative crypto trading model.
By Pedma.
A Short-Term Breakout Trading Strategy for Cryptocurrencies#
Research Article #22 from Trading Research Hub. Presents a short-term breakout trading strategy for cryptocurrencies with total returns of $121,950 in backtesting. The strategy focuses on identifying and trading price breakouts from consolidation ranges on shorter timeframes.
The article provides a systematic implementation of breakout detection rules, including entry triggers, position sizing, and exit criteria. The results demonstrate how short-term momentum captured through breakout signals can generate significant returns in crypto markets, though with the important caveat that short-term strategies face higher execution cost challenges.
By Pedma.
Seasonality in Bitcoin Intraday Trend Trading#
An investigation into whether Bitcoin exhibits exploitable intraday trend-following dynamics and, critically, whether those dynamics vary by time of week. Concretum Research constructs a high-frequency Bitcoin trend-following benchmark and uncovers a clear intraweek seasonality pattern they call the “Monday Asia Open Effect.”
The key finding is that intraday trend-following performance is strongly positive starting Sunday around 7:00 PM New York time, with elevated returns persisting for roughly 24 hours into Monday, aligning with the Monday open of Asian cash equity markets. In contrast, US Sunday morning is associated with negative benchmark performance, consistent with choppier price action and weaker trend persistence. The article demonstrates that incorporating this day-of-week filter can materially improve portfolio performance for intraday crypto trend-following strategies.
By Concretum Research.
Mentioned by Concretum Research in this discussion.
8 Reasons to Stop Using MACD (and Use This Instead) – with Alex Spiroglou#
MACD is one of the most widely used momentum indicators in technical analysis, but it carries a structural flaw that makes its readings incomparable across markets, securities, and time periods. The traditional MACD measures the gap between two exponential moving averages in absolute price terms, which means a reading of 10 on the S&P 500 tells you something entirely different from a reading of 10 on natural gas or gold. In this post, Alex Spiroglou — who won the 2022 Charles H. Dow Award for his research on MACD-V — walks through eight concrete reasons why the standard MACD misleads traders, and presents MACD-V as a volatility-normalized replacement that solves these problems systematically.
MACD-V divides the traditional MACD reading by the 26-period Average True Range (ATR) and scales by 100: MACD-V = [(12-EMA − 26-EMA) / ATR(26)] × 100. This normalization removes the effect of price level and absolute volatility, making readings genuinely comparable across assets and time. A reading of 1.5 on gold means the same thing as a reading of 1.5 on crude oil or the German Bund — overbought and oversold thresholds become universal across asset classes, timeframes, and decades. The indicator effectively transforms MACD from a price-denominated momentum gauge into a volatility-denominated one, eliminating the market-specific calibration problem that makes standard MACD unsuitable for systematic multi-market trading systems.
By Andrew Swanscott, Better System Trader.
Mentioned by @bettersystrader in this discussion.
Time of Day Same Bar Trading#
A straightforward intraday momentum strategy for ES (E-mini S&P 500) futures using 30-minute bars. The approach analyzes absolute price moves (|Close-Open|) for each 30-minute time slot over a 120-day lookback, building historical distributions per slot. A signal triggers when the current bar’s movement exceeds the 90th percentile of that slot’s history, trading directionally with the impulse: long if Close > Open, short if Close < Open. The position is held for exactly one 30-minute bar, then exited at close.
The strategy deliberately excludes stop losses, profit targets, regime filters, transaction fees, and risk management to focus purely on the “raw tendency.” The author emphasizes that this “extreme impulse by time-of-day effect” appears persistent across both long and short sides and multiple markets, positioning it as a foundational building block for systematic trading systems.
By Petr Podhajsky.
Project Kintoun: A Regime-Filtered ORB Strategy#
A regime-filtered opening range breakout (ORB) strategy for NQ (Nasdaq-100) futures on 1-minute timeframes during US equity morning sessions. The strategy builds on classic ORB mechanics — trading price movements beyond the initial 15-minute range — but adds three validation layers to filter false breakouts: microstructure retest confirmation (price must return to the breakout level), VWAP slope regime filtering (trades align with intraday trend direction), and volume confirmation (breakouts require above-average participation).
Backtested from June 2022 through October 2025 across 1,062 trades, the strategy achieved a 1.47 profit factor, 2.28 Sharpe ratio, 57.24% annualized returns, and a maximum drawdown of -8.46% (6.76 Calmar ratio) on a 33.9% win rate. The implementation handles timezone alignment across daylight savings transitions, incorporates dynamic position scaling based on account equity changes, and uses multiple exit mechanisms including profit targets, stop losses, and time-based exits.
By Jeremy Hsu.
Mentioned by Jeremy Hsu in this discussion: “Most trading strategies fail because they assume the wrong probability model.”
How Often Do Stocks Hit 7-12+ ATR Above SMA50? A 10-Year Study#
Denis Hamel measures how far US stocks extend above their 50-day simple moving average, expressed in ATR units — the standard (Close - SMA50) / ATR(14) distance — and turns the empirical distribution of those extensions into a profit-taking framework. The study spans roughly 10 years of daily bars (2016–2026) across 5,998 US common stocks and over 10 million stock-days, counting each continuous run above the SMA50 as a single “leg” (recorded at its peak ATR-distance) to avoid the double-counting that per-day tallies produce. A “CLEAN” filter removes artifacts that masquerade as trends: gap events (any single day with a >25% move, catching biotech FDA pops, reverse-merger spikes, penny pumps) and post-event “pinning” (acquisitions or post-bankruptcy names trading flat while the SMA50 catches up).
The headline rule is “trim hard at 7 ATR, hold a small runner above 11.” Three of every five tradable US stocks reached at least 7 ATR over the decade, two-fifths hit 8, a quarter hit 9, while 15+ ATR is a ~1.2% tail and 20+ is essentially never (4 names in 10 years). Roughly two-thirds of legs that touch 7 ATR die there, which is the math behind an aggressive first-touch trim. The pattern is remarkably stable across liquidity tiers measured by point-in-time dollar volume (heavy $50M+, medium $10–50M, light $1–10M) — the peak-distance distribution has the same steep drop after 7 and a very thin tail past 11 — except that lightly-traded names carry a genuinely fatter right tail and deserve a slightly larger runner. The scale-out logic is elegant: the histogram of leg peaks is the sell distribution, so selling proportionally at each level harvests the expected move without leaving anything undefined. Even the most-watched momentum names rarely break 10 ATR (TSLA 10.21 in the 2020 COVID rally, AAPL 10.11, NFLX 10.02, NVDA 8.94 in the 2023–24 AI leg), reinforcing the case for trimming when a position gets there.
By Denis Hamel.
100 Trading Indicators Tested Across 4,100+ Strategies#
Tomas Nesnidal tests 100 indicator conditions across 4,115 breakout strategies: 3,500 NASDAQ strategies and 615 Bitcoin strategies, both using 60-minute data and in-sample/out-of-sample splits. The study is designed to avoid the usual “one indicator, one hand-picked strategy” trap by applying each filter across many baseline breakout systems and scoring whether it improves net profit, return ratio, win percentage, average trade, and maximum drawdown.
The main result is that simple volatility and participation measures dominate. Bar range relative to ATR is the best overall individual filter, reducing NASDAQ maximum drawdown by 40%, improving net profit by 60%, and carrying the strongest robustness score into unseen data. Volume indicators work especially well on NASDAQ, where volume data is cleaner, while Bitcoin is led by volatility filters such as standard deviation; crypto volume filters are weaker out-of-sample, likely because exchange fragmentation and wash trading make the data noisier. The sharp negative result is on oscillators: RSI, stochastics, and related overbought/oversold tools consistently rank near the bottom for breakout trading because they are built for mean-reversion logic, not continuation after range expansion.
By Tomas Nesnidal.
Mentioned by Breakout Trading Academy (@onlybreakouts) in this X discussion, where the thread summarizes the article as evidence that bar range relative to ATR, volume, and other simple volatility/participation filters beat more complex indicator conditions across thousands of breakout strategies.
The Volume Momentum Deviation Factor#
A short, self-contained write-up of a volume-based factor the author calls the Volume Momentum Deviation Factor. The construction is a MACD histogram computed on volume rather than price: take a fast smoothed moving average of volume, subtract a slow one, then subtract that difference’s own smoothing, leaving the second-order deviation. The published formula is sma(volume,13,2) - sma(volume,27,2) - sma(sma(volume,13,2) - sma(volume,27,2),10,2) — a 13/27 volume MACD with a 10-period signal line, expressed in the two-argument SMA convention used by Chinese quant platforms (period, weight).
The economic story is a lead-lag one. When short-horizon volume strengthens sharply against its longer-horizon baseline, participation and trading heat are rising; when that pickup in volume structure is not yet matched by a corresponding price move, the author reads the gap between fund behaviour and price performance as evidence of an incipient trend or a mispricing. The factor is meant to capture that “volume leading, price not yet reflecting” state and the continuation or correction that follows it. The post shows a backtest equity curve and notes that the whole thing is two lines of source code, inviting readers to test it themselves.
By 套利豪仔 (@pritipatelfgoo).
Replicating and Improving the Saty Phase Oscillator Opening-Window Strategy#
An X quote-chain in which a published intraday index-futures strategy is independently rebuilt, validated against beta and randomised entries, improved, adopted back by its author, and then stress-tested on 19 years of history it had never seen. It is a worked example of what a serious replication of a social-media strategy post looks like — and of how much the published version can flatter itself.
The strategy. The signal is the Saty Phase Oscillator, a free TradingView indicator plotting price displacement from the 21-period EMA normalised by ATR(14), read on a 10-minute NQ chart with extended-hours data. A close above +100 goes long, below −100 goes short; per the replicator, ±100 is roughly 3 ATR of displacement, so this buys extension rather than fading it. Entries are allowed only from 9:30–11:00 ET, exits come on the opposite cross or flat at the close, and there is no stop or target. On NQ since 2019, one contract with friction, the author reported 392 trades, a 59% win rate, 1.86 profit factor, +$259,883 and −$29,707 maximum drawdown.
The replication. The Dutch Warren (@etbent1) rebuilt it from the Pine source as both an MQL5 expert advisor and a Python twin, neither tuned toward the published numbers, and ran it on USTEC from 2020: 13,038 points against the original’s 12,994 — a different index over a different window, about 2% apart. Two controls support it. Buying every open in the same window returns +6.5 points per trade over 1,716 trades against the signal’s +35.0 over 373, so this is not simply long exposure to a rising index; and it beat all 1,000 of 1,000 random-entry variants.
The improvements. Two changes held on both the 2020–23 train and 2024–26 test splits. Ending the entry window at 10:00 rather than 11:00 lifts profit factor from 1.97 to 2.34 and roughly doubles return over drawdown, monotonically — every widening makes it worse. The replicator ties this to the indicator’s construction: ATR(14) on 10-minute bars spans 140 minutes, so at 9:30 the normaliser is almost all premarket while by 11:00 it is full of regular-hours bars, meaning the 3-ATR threshold stops measuring the same quantity as the session progresses. Exiting at the 61.8 level instead of the full traverse to −100 lifts profit factor to 2.16. A third refinement skips entries more than 2.5 ATR from session VWAP, ending in the same place on 258 trades instead of 373 while cutting drawdown from 1,797 points to 759. The two negative results matter as much: sweeping the entry threshold from 80 to 120 shows 100 is a clean peak on both halves rather than a fitted artefact, and measured adverse excursion — winners median 35 points, losers median 117 — overlaps so heavily that any stop tighter than 150 points destroys returns, costing 53% of total profit at 60 points and 21% at 100. There is room for a catastrophe stop, not a working one.
The round trip. The original author adopted all three changes on NQ and re-ran his own backtest, so improvements found on another instrument in another codebase held on the instrument they came from. The current configuration is “+100/−100 cross, 9:30–10:00 ET, exit at opposite 61.8 — skip if price is > 2.5 ATR from VWAP”, and adding the VWAP filter to the already-narrowed version moved profit factor 2.73 → 3.07, drawdown −$14.0K → −$10.4K, return over drawdown 16.1x → 21.8x, t-statistic 4.93 → 5.18, on unchanged total profit of +$227K. Against the original rules that is roughly 87% of the profit on fewer than half the trades — 182 since 2019 — with about a third of the drawdown. Pressed on whether this is levered tech beta, he supplied the long/short split the earlier posts lacked: shorts made $140K against longs’ $87K, which is hard to square with pure long-beta over a period when the index rose throughout.
Our reading of why it works. Inference from the mechanics, not a claim in the posts. The construction quirk the replicator treats as a nuisance is more plausibly the edge. Because ATR(14) looks back 140 minutes, a 9:30–10:00 signal normalises the opening move against thin overnight bars, so a displacement reading as 3 ATR is a far smaller bar than 3 ATR of regular-hours volatility. The trigger therefore fires on days when the opening auction repriced the index decisively beyond its overnight range — information events and large order imbalances rather than noise. Parent orders discovered at the open are worked through the morning on scheduled algorithms, supplying the continuation the trade holds for; this is the standard microstructural account of opening-range effects. It predicts each improvement. The 10:00 cutoff works because the imbalance is largest before it has been worked off, and because a later +100 print requires a bigger move that more likely marks the end of the repricing than its start. The 61.8 exit works because the edge decays as the imbalance is absorbed. The VWAP filter works because an entry far from VWAP means chasing a move that is largely complete — the same exit from a worse price.
Why it should travel across these instruments. Also inference. NQ and USTEC agreement is the weakest evidence of the three, since USTEC is a contract-for-difference wrapper on the same index: it tests data and implementation, not the mechanism. US500 is the only independent instrument, and it is the one that agrees weakly. Four conditions look necessary, and they rank the instruments as observed: a discrete cash open, an overnight session thin enough that premarket-built ATR is materially smaller than regular-hours volatility, enough underlying flow that parent orders are worked over hours, and a participant mix that extends rather than fades the initial move. The Nasdaq-100 scores highest on the last two — it is the most concentrated major index, its constituents carry the heaviest single-stock options and leveraged-ETF flow, and it attracts the most momentum-oriented participation. The S&P 500 is broader and more heavily hedged, so imbalances are smaller relative to volatility and absorbed faster by cross-sector and index-arbitrage flow. Its 1.14 profit factor in 2020–23 against USTEC’s 1.60 is therefore the expected ordering, not a robustness failure.
The same logic says where it should fail, which is the falsifiable half. Continuously traded assets with no opening auction — spot FX, crypto perpetuals — have no premarket-to-regular-hours volatility ratio for the normaliser to exploit and no daily moment when accumulated information discharges at once. Single stocks keep the auction but replace institutional rebalancing flow with idiosyncratic news. Part of that prediction has since been tested.
The multi-instrument test. The replicator ran the same signal on four instruments, each keyed to its own session open rather than the US equity open: USTEC at 09:30 ET, BTC at 10:00, gold at 01:30 (London metals) and WTI at 03:30 (London oil), on M10 bars over 2020–2026, each leg scaled to one unit of its own maximum drawdown. All three satellites earn their place on recovery factor (return ÷ max drawdown): USTEC alone 22.1, plus gold 24.0, plus BTC 29.5, plus WTI 31.1, all four 31.8. The portfolio result is the striking one — every pairwise daily correlation is within ±0.04, so four legs at one unit of risk each still draw down only 1.10 combined while return goes from 22 to 35. Near-zero correlation is itself evidence for the mechanism: if these legs were riding a common macro factor or shared beta they would move together, whereas each trading its own local session’s information discharge is exactly what produces independence. The negative results carry as much information. Forex produced nothing at any hour of the day; silver, platinum and most mega-caps were actively worse than buying blind. His summary is the right one — the window matters more than the instrument.
Scoring our prediction against it. Half right. Forex failing at every hour is a clean confirmation, and so is the mega-cap result, where a signal that is worse than blind buying fits the known tendency of single-stock opening moves to reverse rather than extend. But BTC working refutes the crypto half outright, and the correction is worth making precisely. What the mechanism requires is not a formal opening auction but a recurring hour at which activity steps up sharply from a thin baseline — and post-ETF Bitcoin has one, since institutional and US-hours flow concentrates the day even though the venue never closes. An auction is one way to manufacture that step; a habitual session is another. The result also narrows our earlier account of why NQ ranks first. Gold, oil and BTC have no 0DTE hedging, no leveraged-ETF rebalancing and no mega-cap retail concentration, yet the signal works on all three, so those features cannot be what the edge depends on. The common factor is thinner: a thin period followed by a concentrated one, in an instrument whose flow at that hour is large enough to be worked over hours rather than absorbed at once. That is a better and more falsifiable claim than the equity-microstructure story it replaces. Two cautions on the test itself: it is 2020–2026 only, so the regime concerns raised by the 26-year backtest apply here with less history rather than more, and the author flags that these are CFDs rather than futures and a backtest rather than live results. There is also a selection question — establishing that forex fails “at any hour of the day” means many instrument-by-hour combinations were searched, and the surviving three were reported alongside the failures, which is the honest way to do it but still a wide search.
Extending the sample to 2000. Cédric Després (@semoi69) re-ran the original rules — 388 trades indicates the unrefined version — and changed how the results are presented, twice. First, a like-for-like sizing comparison on the published window: one fixed contract against risking 1% of capital per 0.4% of price movement, a constant notional of about 2.5x capital. Same signals and 59.3% win rate, but profit factor 1.85 against 1.71, CAGR +18.0% against +15.1%, Sharpe 1.38 against 1.21, at average leverage 3.1x and 2.5x. The reframing number is the drawdown: −$30,989, quoted as −24.0% of capital, implying an account near $130,000. Stated that way the strategy is an 18% CAGR at a 24% peak-to-trough loss with Calmar below 1 — respectable, but a different object from “+$227K on one contract, 21.8x return over drawdown”, which omits the capital required to carry it.
Second, he extended the backtest to 2000 and shaded the region the published version never saw — “les 19 années que la stratégie publiée n’a jamais vues”. Under percentage sizing the strategy does make money there, but the shape is nothing like the recent curve: a strong run into a 2008–09 peak, then roughly seven years sideways-to-underwater through 2016, mostly −5% to −25% below prior peak, before climbing again into 2019. His summary is the right one — you have to be willing to be flat for five or more years and keep taking the trades.
Why the sizing matters. Our analysis. Under fixed-contract sizing, dollar P&L is proportional to point moves, and a Nasdaq point has been worth $20 throughout while the index went from roughly 1,500 in 2003 to 25,000 today. An unchanged percentage edge therefore yields something like fifteen times more dollars at the end of the sample than at the start. That is what the chart shows: the fixed-contract line is pinned near zero across 2000–2018 and only takes off after 2019, while the percentage-sized line shows the strategy was demonstrably active and profitable in the early 2000s. The flat stretch is an artefact of quoting a two-decade backtest in undeflated points, and the same artefact inflates the recent record. This is distinct from the beta question already addressed — the random-entry and buy-every-open controls are also in points, so they establish the signal beats naive entry but do not correct for dollar magnitude scaling with index level. It also explains the post-2025 divergence: a contract at 25,000 is simply a larger bet than a contract at 7,000, which is what the 3.1x versus 2.5x average leverage measures.
Why the regime matters. The long flat stretch runs from roughly 2009 to 2016, which on the reading above is exactly where the mechanism should have been weakest: post-crisis quantitative easing, suppressed intraday volatility, low dispersion, dominant index arbitrage, and execution algorithms whose purpose is to absorb the opening imbalance this strategy rides. An imbalance worked off in twenty minutes yields nothing to a system holding for continuation. The strong early 2000s fit from the other direction — the dot-com unwind supplied enormous overnight repricings into a market with far less algorithmic absorption — and the post-2019 resurgence coincides with 0DTE hedging, leveraged-ETF rebalancing in mega-cap technology, and a step-change in retail participation. The 26-year curve is less an indictment than a confirmation of what the strategy is: a bet on a microstructural regime that paid in 2000–08, did not in 2009–16, and has paid again since 2019.
What this does to the statistics. Profit factor 3.07, return over drawdown 21.8x and a 5.18 t-statistic are all computed inside the favourable regime on 182 trades. The t-statistic tests whether the mean trade differs from zero within the sample, not whether the sample is representative, and a strategy refined on a regime will produce a strong one there almost by construction. Every improvement — the 10:00 cutoff, the 61.8 exit, the VWAP filter — was developed on 2020–26 data and validated on a split entirely inside that window, and a train/test partition of a single regime cannot detect regime-dependence. Després’s real contribution is pointing out that a genuine 19-year holdout already exists, unused. Re-running the refined ruleset in percentage terms on 2000–2019 is the highest-value test remaining. In fairness, he tested the original rules rather than the refined ones, and 10-minute Nasdaq futures data with premarket sessions back to 2000 is hard enough to source that the pre-2010 portion deserves scepticism about the data before the strategy.
Remaining caveats. Slippage assumptions beyond “friction included” are never stated, nor is it shown how the results survive the 2020 volatility regime specifically. The system runs without a working stop, which the adverse-excursion study justifies statistically but which stays a live tail-risk decision — easier to see once drawdown is quoted as −24% of capital rather than a dollar figure detached from any account size.
By The Milk Man (@MrMilkTrading), with the replication and extensions by the Dutch Warren (@etbent1) and the long-history test by Cédric Després (@semoi69).
The chain runs original rules and backtest → independent replication and two improvements → the author’s acknowledgement → the VWAP-distance filter → the filter confirmed on NQ, with updated metrics. A follow-up applies the signal to four instruments on their own session opens. Separately, Cédric Després extends the backtest to 2000 and compares position-sizing schemes.