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Scan for outdated or missing drivers - takes under a minuteDriver Scan →Repair Windows errors before they cause bigger problemsFix Now →There is no verified universal finding that 90% of candlestick patterns fail. A candle pattern is a rule for describing past prices, not proof of what prices will do next. To test one responsibly, define what “fail” means, combine the pattern with explicit market context, and evaluate the resulting signals on future data after realistic trading costs. The Python example below builds a transparent research scanner; it is not a validated or profitable trading system.
What does “fail” mean for a candlestick pattern?
The number in the title is not established as a general statistic by the cited evidence. It would need a defined set of patterns, markets, timeframes, success criterion, sample period, and study date before “90% fail” could be tested. Change any of those choices and the result may change too.
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A candlestick encodes open, high, low, and close prices over a chosen interval. A named formation—such as a hammer—is a rule applied to those prices. It does not, by itself, establish a reliable prediction. Before measuring a pattern, specify:
- Identification: Which exact OHLC conditions count as the pattern?
- Outcome: Does success mean the next bar closes higher, price reaches a target before a stop, or a strategy earns a positive net return?
- Horizon and timing: When can a signal be acted on, and how long is it held?
- Comparison: Does it outperform a simple baseline on the same instruments and dates?
These are different questions, not interchangeable meanings of “works.”
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Why accuracy is not the same as trading success
| Measure | What it answers | What it does not establish |
|---|---|---|
| Pattern identification | Whether a rule or model labels a bar as a particular formation. | Whether the next move will be favorable. |
| Directional classification | How often a model correctly labels a defined future direction in a chosen dataset. | Whether trades can be executed profitably after costs. |
| Strategy performance | How a specified entry, exit, sizing, and cost model performed over a test period. | That the same result will persist in another market or regime. |
A 2019 preprint, Using Deep Learning Neural Networks and Candlestick Chart Representation to Predict Stock Market, reports 92.2% accuracy for its Taiwan dataset and 92.1% for its Indonesian dataset. Those are the paper authors’ results for deep-learning classification experiments using chart-image representations and selected datasets and prediction labels. They are not a universal candlestick win rate, a test of every named candle rule, or evidence of net profitability after execution costs.
Separately, the 2024 Journal of Financial Economics article Charting by Machines reports that machine-learning forecasts built from historical performance predicted the cross-section of future stock returns in the authors’ study. That is evidence about learned chart/history signals in that study, not direct confirmation of a particular candle formation or the scanner below.
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What a confluence scanner should—and should not—do
A confluence scanner makes several defined observations visible together: for example, a candle formation, a trend condition, and unusual volume. It can help rank or flag bars for review. Unless its score has been fitted to a specified outcome and calibrated on suitable data, that score is a heuristic—not a probability of success.
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“Institutional” in a scanner’s label does not make it institutionally validated. A serious research workflow needs reproducible inputs, inspectable rules, time-ordered testing, cost assumptions, and controls for data and model failures. The score should explain why a bar was flagged rather than imply certainty.
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Build a transparent Python scanner
The example uses a deterministic hammer-like rule plus two context checks: an upward trend and volume above its recent average. These definitions are teaching choices, not universal definitions of a hammer or evidence that these features improve returns. It expects a pandas DataFrame with lowercase open, high, low, close, and volume columns, ordered by time. Use consistently adjusted OHLC data where appropriate and record the instrument, interval, timezone, source, and retrieval date.
import pandas as pd
def add_confluence_features(df: pd.DataFrame) -> pd.DataFrame:
required = {"open", "high", "low", "close", "volume"}
missing = required.difference(df.columns)
if missing:
raise ValueError(f"Missing columns: {sorted(missing)}")
x = df.copy().sort_index()
if x.index.has_duplicates:
raise ValueError("Duplicate timestamps must be resolved first")
if x[list(required)].isna().any().any():
raise ValueError("Missing OHLCV values must be handled first")
if (x["high"] < x[["open", "close", "low"]].max(axis=1)).any():
raise ValueError("High is inconsistent with OHLC values")
if (x["low"] > x[["open", "close", "high"]].min(axis=1)).any():
raise ValueError("Low is inconsistent with OHLC values")
if (x["volume"] < 0).any():
raise ValueError("Volume cannot be negative")
bar_range = x["high"] - x["low"]
body = (x["close"] - x["open"]).abs()
lower_wick = x[["open", "close"]].min(axis=1) - x["low"]
upper_wick = x["high"] - x[["open", "close"]].max(axis=1)
# Example rule only: a long lower wick, small upper wick, nonzero body,
# and a close in the upper 40% of the bar's range.
x["hammer_like"] = (
(bar_range > 0)
& (body > 0)
& (lower_wick >= 2 * body)
& (upper_wick <= body)
& (x["close"] >= x["low"] + 0.6 * bar_range)
)
sma20 = x["close"].rolling(20, min_periods=20).mean()
x["trend_up"] = (x["close"] > sma20) & (sma20 > sma20.shift(5))
volume_mean20 = x["volume"].rolling(20, min_periods=20).mean()
x["volume_above_average"] = x["volume"] > volume_mean20
# A ranking heuristic, not a calibrated probability.
x["score"] = (
x["hammer_like"].astype(int)
+ x["trend_up"].astype(int)
+ x["volume_above_average"].astype(int)
)
x["candidate"] = x["hammer_like"] & (x["score"] >= 2)
return xThe sample rejects missing values and duplicate timestamps, checks basic OHLC consistency, and avoids evaluating rolling features before their lookback is available. Those checks cannot establish that the feed is complete, correctly adjusted, or free of bad timestamps; validate those properties against the chosen data source as well. The rule’s thresholds and 20-bar context windows are explicit so they can be changed and tested rather than treated as market facts.
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Run the function only on completed bars. A signal computed from a bar’s closing price is not available before that close; a backtest that enters at that same close without a defensible execution assumption may introduce look-ahead. For an alert, display the timestamp, raw pattern condition, trend and volume checks, and score components. Do not describe score as a success probability.
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- Freeze the question first. Define instruments, bar interval, timezone, pattern rule, prediction target, horizon, and signal timing before looking for a favorable result. Decide whether you are evaluating labels or an executable strategy.
- Split by time. Use earlier observations for development and later observations for validation; reserve a final, untouched chronological test period. Do not randomly mix future and past bars. Fit thresholds, weights, or other learned choices on training data only.
- Compare simple baselines. Test the candle alone, each context feature alone, and a basic reference such as the unconditional outcome rate or a simple trend rule. Keep the same dates, target, and assumptions across comparisons.
- Report the right measures. For classification, report the target definition, class balance, and suitable metrics rather than accuracy alone. For a strategy, report returns and risk measures under stated sizing and execution rules. Keep the two result types separate.
- Model execution costs. State when an order could be placed and include fees, spread, and slippage assumptions. Check whether a result survives plausible changes to those assumptions; a gross signal is not a net strategy result.
- Test stability. Where data permits, repeat the evaluation across instruments, periods, and market regimes. A result that depends on one asset, one narrow date range, or one threshold is not broad evidence.
- Keep the final test untouched. Repeatedly tuning choices against the test interval turns it into development data. If you change the rule after seeing test results, reserve a new future interval for a fresh evaluation.
For a simple directional label, one possible target is whether the close several bars after a signal exceeds the signal-bar close. That target must be aligned so every feature uses information available at signal time; it also does not model an entry price, stop, position size, or costs. A strategy evaluation needs those additional rules and assumptions, defined before results are interpreted.
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What controls belong around an AI-assisted scanner?
A model can make incorrect classifications or generate false positives. Make each flag auditable: preserve the input timestamp and data version, show the pattern and feature values that contributed, and log subsequent outcomes under the predeclared evaluation rule. Monitor data quality and behavior over time; a change in feed, instrument mix, or market regime can make old results less informative.
In a speech on machine learning and risk assessment, SEC staff speaker Scott W. Bauguess said, “good data is better than more data.” The speech discusses data quality and the limits of applying machine-learning methods to poor or unstructured inputs. It also describes experts critically examining model outputs and the possibility of false positives. That is a cautionary analogy from the SEC’s risk-assessment setting, not evidence about trading performance or a legal checklist for every research scanner. The SEC’s 2020 staff report on algorithmic trading, updated in 2023, provides an overview of algorithmic trading in U.S. capital markets; obligations depend on how a system is used, who operates it, the instruments involved, and the relevant jurisdiction.
A separate 2026 arXiv preprint evaluates LLM technical-market-analysis tasks. As a preprint with simulated findings, it should not be treated as established evidence that an LLM scanner is reliable in live deployment.
Conclusion
The useful question is not whether a fixed percentage of candlestick patterns fail. It is whether a precisely defined rule adds measurable value for a stated market, timeframe, and execution plan. A transparent Python scanner can organize candidates and make assumptions inspectable; only careful out-of-sample testing can show what the signals did under specified conditions, and no backtest guarantees future results.
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