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The research

“Smart money” concepts: what the research actually says

About half of the popular “smart money” trading concepts are renamed versions of real, documented finance — and about half are folklore. The line runs cleanly between passive, observable patterns (real) and intentional-manipulation or precise-rule claims (folklore). One thing is consistent across all of it: every supporting study documents a phenomenon or a weak, decaying signal — none of them endorses any of these setups as a profitable rule.

Concept by concept

Where each idea really comes from

For each popular concept: the real phenomenon it points at, an honest rating of how well peer-reviewed research supports it, what the research does and doesn’t show, and the actual papers. Every citation here was verified against its source.

PARTLY SUPPORTED

Liquidity sweeps & resting stop orders

also called: liquidity pools · stop hunts · buy-side / sell-side liquidity · equal highs & lows

Where it comes from. Market-microstructure research on round-number price clustering and the stop-loss / take-profit orders that rest at obvious levels.

What the research supports. Round-number price clustering is one of the most replicated microstructure findings. Stop and take-profit orders cluster at and just beyond those levels, and reaching a stop cluster can trigger positive-feedback “price cascades” that make moves unusually fast. So “liquidity rests at obvious levels, and a move into it can fuel a fast continuation” is genuinely real and measured.

What it does not support. The idea that institutions deliberately drive price to a level to trigger retail stops and then reverse. The measured cascades are emergent — many independent orders firing in waves — not a coordinated raid; predatory-trading theory concerns forced liquidation of large distressed positions, not retail stops. And take-profit clusters reverse price, so a “sweep” is not a one-way trade. Most direct evidence is FX from ~1996–2005.

Sources: Osler (2000) · Osler (2003) · Osler (2005) · Harris (1991) · Bhattacharya, Holden & Jacobsen (2012) · Webb & Mitchell (2001) · Brunnermeier & Pedersen (2005)

PARTLY SUPPORTED

Session timing & “kill zones”

also called: London open · New York open · London–NY overlap · session windows

Where it comes from. Documented intraday volatility and volume seasonality.

What the research supports. Time of day genuinely matters: volatility and volume cluster at session opens and especially the London–New York overlap. This is robust across decades of peer-reviewed equity and FX research, plus a 14-year central-bank study.

What it does not support. Any privileged single “window”; uniform behavior across sessions (the open-volatility pattern doesn’t even hold for New York in the data); and profitability — these studies show WHEN volatility occurs, not that trading those windows beats costs. Higher volatility is not itself an edge. Samples run 1985–2007 and should be re-checked on today’s markets.

Sources: Wood, McInish & Ord (1985) · Harris (1986) · Andersen & Bollerslev (1997) · Andersen & Bollerslev (1998) · Ito & Hashimoto (2006) · Clifton & Plumb / RBA (2007)

PARTLY SUPPORTED

Fair value gaps & imbalances

also called: 3-candle imbalance · liquidity void · “gaps always fill”

Where it comes from. Order-imbalance microstructure plus the academic literature on price gaps.

What the research supports. Signed order imbalance genuinely moves price, and a gap-related anomaly does exist.

What it does not support. The visual “3-candle fair value gap” is a charting heuristic with no peer-reviewed test, and it should not be conflated with the order-flow “imbalance” literature — that’s signed order flow, not a candle gap. The “gaps always fill” belief is the part most at odds with the evidence: rigorous studies find gaps more often CONTINUE in their direction, and one explicitly calls universal gap-fill a “myth.” Gap-fill is conditional on size, horizon and market, not universal.

Sources: Chordia, Roll & Subrahmanyam (2002) · Plastun, Sibande, Gupta & Wohar (2020) · Caporale & Plastun (2017) · Stuebinger & Schneider (2019) · Aiche, Cohen & Griskin (2023) · Janse van Rensburg & van Zyl (2025)

FOLKLORE

Accumulation → manipulation → distribution

also called: market-maker model · smart-money cycle · three-phase model

Where it comes from. The Wyckoff Method (1910s–1930s) — accumulation / markup / distribution / markdown phases and the “composite operator.”

What the research supports. The lineage is genuine and well-documented: this three-phase model is a session-level simplification of Wyckoff’s century-old framework, with an added “manipulation” (liquidity-sweep) phase.

What it does not support. Wyckoff’s own works are descriptive practitioner texts with no statistical testing, and no rigorous peer-reviewed study validates the three-phase intraday model as profitable. (A well-known 1992 paper tested moving-average and breakout rules — not this model — in-sample only, and was later tempered by data-snooping critiques.)

Sources: Wyckoff (1910) · Wyckoff (1931 / 1937) · Brock, Lakonishok & LeBaron (1992) · Sharma & Raj (2024)

PARTLY SUPPORTED

Market structure & order blocks

also called: break of structure · change of character · order blocks · supply & demand zones · trend

Where it comes from. The broad trend/structure idea descends from Dow Theory and the technical-analysis literature; the support/resistance sub-piece descends from the real order-clustering mechanism.

What the research supports. Trend and breakout structure carry a weak, decaying signal — real but mixed. Support/resistance levels are the best-supported sub-piece, with genuine (level-dependent) predictive power plus a real order-clustering mechanism behind them.

What it does not support. Much of the breakout edge turns out to be a data-snooping artifact that fades out-of-sample and after costs, and profitability has decayed over time. The distinctive “order block” candle and the break-of-structure / change-of-character constructs have no peer-reviewed validation located at all.

Sources: Brock, Lakonishok & LeBaron (1992) · Sullivan, Timmermann & White (1999) · Lo, Mamaysky & Wang (2000) · Park & Irwin (2007) · Brown, Goetzmann & Kumar (1998) · Osler (2000 / 2003)

MIXED

Fibonacci “optimal” entries & smart-money order flow

also called: Fibonacci retracement levels · “optimal” entry · smart money vs retail

Where it comes from. Informed-trading theory and microstructure (the “smart money” framing) plus Fibonacci charting folklore (the precise ratios).

What the research supports. That informed “smart money” exists, has measurable price impact, and systematically disadvantages aggregate retail is established science — from the foundational theory to measurable informed-flow metrics to evidence that heavily retail-bought stocks subsequently underperform.

What it does not support. None of that validates reading precise intrabar levels or stops being hunted to specific chart prices — it is about spreads, price impact and aggregate multi-week predictability. And the Fibonacci “optimal entry” ratios fail their one direct controlled test: Fibonacci zones performed no better than randomly chosen non-Fibonacci zones. (The order-flow “toxicity” metric is real but disputed.)

Sources: Kyle (1985) · Glosten & Milgrom (1985) · Easley, Kiefer, O’Hara & Paperman (1996) · Easley, López de Prado & O’Hara (2012) · Andersen & Bondarenko (2014) · Barber, Odean & Zhu (2009) · Tsinaslanidis, Guijarro & Voukelatos (2022) · Gurrib, Nourani & Bhaskaran (2022)

We also tested it ourselves

One popular mechanical version, on 7 years of real data

One of the most popular mechanical setups built from these concepts — enter on a fair-value gap after price sweeps a session high or low and shifts structure, inside a fixed session window — run with no lookahead and realistic costs on 2,553,973 one-minute EURUSD bars (2019-01-01–2025-12-31). Across 54 rule variations (three session windows, three reward-to-risk ratios, a minimal and a fuller encoding), the best win rate any variation reached was 56.3%, and 0 of 54 were profitable after a 0.5-pip cost. That is consistent with the research above: the underlying patterns are real, but a precise mechanical rule built on them did not produce an edge in our test.

EncodingWindow (ET)R:RBiasTradesWin rateExpectancy (R)Profit factor
Sweep → structure shift → gapLondon 03-041:1daily29152.2%-0.1310.77
Sweep → structure shift → gapLondon 03-041:1none57753.4%-0.1060.81
Sweep → structure shift → gapLondon 03-041:1open15548.4%-0.1880.68
Sweep → structure shift → gapLondon 03-041:2daily29136.4%-0.1270.83
Sweep → structure shift → gapLondon 03-041:2none57738.0%-0.0920.87
Sweep → structure shift → gapLondon 03-041:2open15534.8%-0.1620.78
Sweep → structure shift → gapLondon 03-041:3daily29128.5%-0.1240.85
Sweep → structure shift → gapLondon 03-041:3none57730.0%-0.0920.89
Sweep → structure shift → gapLondon 03-041:3open15527.7%-0.1490.82
Sweep → structure shift → gapAM 10-111:1daily18750.3%-0.1290.77
Sweep → structure shift → gapAM 10-111:1none39948.6%-0.1590.73
Sweep → structure shift → gapAM 10-111:1open15253.3%-0.0610.88
Sweep → structure shift → gapAM 10-111:2daily18734.8%-0.1460.8
Sweep → structure shift → gapAM 10-111:2none39933.3%-0.2040.73
Sweep → structure shift → gapAM 10-111:2open15238.2%-0.0930.86
Sweep → structure shift → gapAM 10-111:3daily18725.7%-0.2390.71
Sweep → structure shift → gapAM 10-111:3none39926.3%-0.2480.7
Sweep → structure shift → gapAM 10-111:3open15232.2%-0.0920.88
Sweep → structure shift → gapPM 14-151:1daily15348.4%-0.2190.63
Sweep → structure shift → gapPM 14-151:1none31649.1%-0.1990.66
Sweep → structure shift → gapPM 14-151:1open12656.3%-0.0540.89
Sweep → structure shift → gapPM 14-151:2daily15338.6%-0.1400.8
Sweep → structure shift → gapPM 14-151:2none31636.4%-0.1870.74
Sweep → structure shift → gapPM 14-151:2open12639.7%-0.0880.87
Sweep → structure shift → gapPM 14-151:3daily15332.7%-0.1510.81
Sweep → structure shift → gapPM 14-151:3none31632.0%-0.1680.78
Sweep → structure shift → gapPM 14-151:3open12632.5%-0.1620.79
First gap (minimal)London 03-041:1daily60149.9%-0.2650.58
First gap (minimal)London 03-041:1none115348.3%-0.2980.55
First gap (minimal)London 03-041:1open62845.7%-0.3510.49
First gap (minimal)London 03-041:2daily60133.4%-0.2600.69
First gap (minimal)London 03-041:2none115334.4%-0.2310.72
First gap (minimal)London 03-041:2open62832.3%-0.2950.65
First gap (minimal)London 03-041:3daily60125.6%-0.2380.75
First gap (minimal)London 03-041:3none115325.8%-0.2300.75
First gap (minimal)London 03-041:3open62823.9%-0.3090.68
First gap (minimal)AM 10-111:1daily65147.3%-0.2840.56
First gap (minimal)AM 10-111:1none128548.3%-0.2620.59
First gap (minimal)AM 10-111:1open61247.1%-0.2900.56
First gap (minimal)AM 10-111:2daily65133.5%-0.2260.72
First gap (minimal)AM 10-111:2none128534.2%-0.2050.75
First gap (minimal)AM 10-111:2open61233.8%-0.2190.73
First gap (minimal)AM 10-111:3daily65126.3%-0.1790.8
First gap (minimal)AM 10-111:3none128526.2%-0.1820.8
First gap (minimal)AM 10-111:3open61226.0%-0.1980.78
First gap (minimal)PM 14-151:1daily33945.1%-0.3920.45
First gap (minimal)PM 14-151:1none76045.5%-0.3900.45
First gap (minimal)PM 14-151:1open37543.5%-0.4290.42
First gap (minimal)PM 14-151:2daily33931.3%-0.3640.59
First gap (minimal)PM 14-151:2none76031.8%-0.3510.6
First gap (minimal)PM 14-151:2open37529.3%-0.4270.54
First gap (minimal)PM 14-151:3daily33924.2%-0.3580.64
First gap (minimal)PM 14-151:3none76023.7%-0.3770.62
First gap (minimal)PM 14-151:3open37521.6%-0.4670.54

Expectancy = average R per trade (the metric that actually matters; a high win rate at a small target can still lose). Profit factor < 1 = a net loser. Net of cost. See methodology below.

Method. Free 1-minute FX data from HistData.com (used under their terms; we publish only derived statistics). No lookahead; fills require price to trade to the level; 0.5-pip round-trip cost; stop assumed first on an ambiguous bar; times in America/New_York. Educational only, not advice. Educational information only — not investment advice. Hypothetical backtested results; past performance does not guarantee future results. Trading involves risk of loss.

References

34 sources — all verified

  1. Osler, Carol L. (2000). “Support for Resistance: Technical Analysis and Intraday Exchange Rates.” FRBNY Economic Policy Review, 6(2), 53–68.
  2. Osler, Carol L. (2003). “Currency Orders and Exchange Rate Dynamics: An Explanation for the Predictive Success of Technical Analysis.” Journal of Finance, 58(5), 1791–1820. DOI: 10.1111/1540-6261.00588.
  3. Osler, Carol L. (2005). “Stop-loss orders and price cascades in currency markets.” Journal of International Money and Finance, 24(2), 219–241.
  4. Harris, Lawrence (1991). “Stock Price Clustering and Discreteness.” Review of Financial Studies, 4(3), 389–415. DOI: 10.1093/rfs/4.3.389.
  5. Bhattacharya, U., Holden, C. W., & Jacobsen, S. (2012). “Penny Wise, Dollar Foolish: Buy-Sell Imbalances On and Around Round Numbers.” Management Science, 58(2), 413–431. DOI: 10.1287/mnsc.1110.1364.
  6. Webb, R. I., & Mitchell, J. (2001). “Clustering and psychological barriers: The importance of numbers.” Journal of Futures Markets, 21(5), 395–428.
  7. Brunnermeier, M. K., & Pedersen, L. H. (2005). “Predatory Trading.” Journal of Finance, 60(4), 1825–1863. DOI: 10.1111/j.1540-6261.2005.00781.x.
  8. Wood, R. A., McInish, T. H., & Ord, J. K. (1985). “An Investigation of Transactions Data for NYSE Stocks.” Journal of Finance, 40(3), 723–739.
  9. Harris, Lawrence (1986). “A Transaction Data Study of Weekly and Intradaily Patterns in Stock Returns.” Journal of Financial Economics, 16(1), 99–117.
  10. Andersen, T. G., & Bollerslev, T. (1997). “Intraday periodicity and volatility persistence in financial markets.” Journal of Empirical Finance, 4(2–3), 115–158.
  11. Andersen, T. G., & Bollerslev, T. (1998). “Deutsche Mark–Dollar Volatility: Intraday Activity Patterns, Macroeconomic Announcements, and Longer Run Dependencies.” Journal of Finance, 53(1), 219–265.
  12. Ito, T., & Hashimoto, Y. (2006). “Intra-day Seasonality in Activities of the Foreign Exchange Markets.” Journal of the Japanese and International Economies, 20(4), 637–664.
  13. Clifton, K., & Plumb, M. (2007). “Intraday Currency Market Volatility and Turnover.” Reserve Bank of Australia Bulletin, Dec 2007.
  14. Chordia, T., Roll, R., & Subrahmanyam, A. (2002). “Order imbalance, liquidity, and market returns.” Journal of Financial Economics, 65(1), 111–130.
  15. Plastun, A., Sibande, X., Gupta, R., & Wohar, M. E. (2020). “Price gap anomaly in the US stock market: The whole story.” North American Journal of Economics and Finance, 52, 101177.
  16. Caporale, G. M., & Plastun, A. (2017). “Price gaps: Another market anomaly?” Investment Analysts Journal, 46(4), 279–293.
  17. Stuebinger, J., & Schneider, L. (2019). “Statistical Arbitrage with Mean-Reverting Overnight Price Gaps… S&P 500.” Journal of Risk and Financial Management, 12(2), 51.
  18. Aiche, A., Cohen, G., & Griskin, V. (2023). “Stocks Opening Price Gaps and Adjustments to New Information.” Computational Economics, 63(2), 759–775.
  19. Janse van Rensburg, M., & van Zyl, T. (2025). “Price Gaps and Volatility: Do Weekend Gaps Tend to Close?” Journal of Risk and Financial Management, 18(3), 132.
  20. Wyckoff, R. D. [as “Rollo Tape”] (1910). Studies in Tape Reading. The Ticker Publishing Co.
  21. Wyckoff, R. D. (1931 / 1937). The Richard D. Wyckoff Method of Trading and Investing in Stocks.
  22. Brock, W., Lakonishok, J., & LeBaron, B. (1992). “Simple Technical Trading Rules and the Stochastic Properties of Stock Returns.” Journal of Finance, 47(5), 1731–1764.
  23. Sullivan, R., Timmermann, A., & White, H. (1999). “Data-Snooping, Technical Trading Rule Performance, and the Bootstrap.” Journal of Finance, 54(5), 1647–1691.
  24. Lo, A. W., Mamaysky, H., & Wang, J. (2000). “Foundations of Technical Analysis…” Journal of Finance, 55(4), 1705–1765.
  25. Park, C.-H., & Irwin, S. H. (2007). “What Do We Know About the Profitability of Technical Analysis?” Journal of Economic Surveys, 21(4), 786–826.
  26. Brown, S. J., Goetzmann, W. N., & Kumar, A. (1998). “The Dow Theory: William Peter Hamilton’s Track Record Reconsidered.” Journal of Finance, 53(4), 1311–1333.
  27. Kyle, A. S. (1985). “Continuous Auctions and Insider Trading.” Econometrica, 53(6), 1315–1335.
  28. Glosten, L. R., & Milgrom, P. R. (1985). “Bid, ask and transaction prices in a specialist market…” Journal of Financial Economics, 14(1), 71–100.
  29. Easley, D., Kiefer, N. M., O’Hara, M., & Paperman, J. B. (1996). “Liquidity, Information, and Infrequently Traded Stocks.” Journal of Finance, 51(4), 1405–1436.
  30. Easley, D., López de Prado, M., & O’Hara, M. (2012). “Flow Toxicity and Liquidity in a High-Frequency World.” Review of Financial Studies, 25(5), 1457–1493.
  31. Andersen, T. G., & Bondarenko, O. (2014). “VPIN and the flash crash.” Journal of Financial Markets, 17, 1–46.
  32. Barber, B. M., Odean, T., & Zhu, N. (2009). “Do Retail Trades Move Markets?” Review of Financial Studies, 22(1), 151–186.
  33. Tsinaslanidis, P., Guijarro, F., & Voukelatos, N. (2022). “Automatic identification and evaluation of Fibonacci retracements…” Expert Systems with Applications, 187, 115893.
  34. Gurrib, I., Nourani, M., & Bhaskaran, R. K. (2022). “Energy crypto currencies… are Fibonacci retracements profitable?” Financial Innovation, 8(1), Article 8.

We cite what each paper actually found and avoid overstating it; where a concept has no rigorous support, we say so plainly. Educational information only — not investment advice. Educational information only — not investment advice. Hypothetical backtested results; past performance does not guarantee future results. Trading involves risk of loss.

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