Simulation · synthetic data

Round numbers: the order clustering behind “levels the market respects”

Take-profit orders cluster ON round numbers and stop-losses just BEYOND them — a measured order-flow asymmetry that produces both reversals at the level and faster moves after a break. Build the book, run price into it, and compare round levels against non-round controls.

Self-contained simulation on randomly generated synthetic data — not market data, not a live signal, and not a record of trading. It runs entirely in your browser; nothing is sent anywhere. Educational information only — not investment advice. Hypothetical backtested results; past performance does not guarantee future results. Trading involves risk of loss. Full-page version, with the findings and citations

The evidence behind it

What the research actually found

The support rating below is the same one this concept carries in our research corpus, and the quoted findings are the corpus text — not a summary written to flatter the simulation. Every citation was verified against its source.

PARTLY SUPPORTED

Support & resistance levels

also called: round numbers, prior highs/lows, 'levels the market respects'

Where it comes from. As old as charting itself. The credible mechanism arrived when researchers looked at actual order books: customer stop-loss and take-profit orders cluster heavily at round numbers, which mechanically creates bounce points and breakout accelerations.

What the research supports. Support/resistance levels published by major FX dealers had genuine intraday predictive content — prices bounced off them more often than chance (Osler 2000). The mechanism is documented: take-profit orders cluster ON round numbers and stop-losses just BEYOND them, so trend reversals at round levels and faster moves after breaks are real order-flow effects, not chart mysticism (Osler 2003).

What it does not support. Predictive content is not a trading system: the documented effects are intraday, small, and strongest in FX where the order data lives. Nothing supports precise 'retest' entry rituals or the idea that drawn lines on any chart carry power beyond where orders actually cluster.

Sources: Osler (2000) · Osler (2003) · full concept-by-concept evidence

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) · full concept-by-concept evidence

Also embedded on

SMC: Liquidity Sweep

References

7 sources — all verified

Osler, Carol L. (2000). “Support for Resistance: Technical Analysis and Intraday Exchange Rates.” FRBNY Economic Policy Review, 6(2), 53–68.
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.
Osler, Carol L. (2005). “Stop-loss orders and price cascades in currency markets.” Journal of International Money and Finance, 24(2), 219–241.
Harris, Lawrence (1991). “Stock Price Clustering and Discreteness.” Review of Financial Studies, 4(3), 389–415. DOI: 10.1093/rfs/4.3.389.
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.
Webb, R. I., & Mitchell, J. (2001). “Clustering and psychological barriers: The importance of numbers.” Journal of Futures Markets, 21(5), 395–428.
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.

This simulation demonstrates a mechanism on synthetic data; it does not reproduce any paper's dataset or reported magnitudes. 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. See the methodology and the full disclaimer.