MT5 Multi-Timeframe Candlestick Pattern Scanner & Backtester v7

A comprehensive tool to scan M5, M15, H1, H4, D1 charts for classical candlestick patterns, backtest their performance with realistic entry/exit simulation, and run a live scanner that scores and alerts when a new pattern appears.

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Features

• 20+ patterns: Doji, Hammer, Shooting Star, Engulfing, Morning/Evening Star, Three White Soldiers, Three Black Crows, Marubozu, Harami, Tweezers, Rising/Falling Three Methods, Inverted Hammer, and more
• Multi-timeframe backtesting — backtest all 5 timeframes in a single run with per-TF statistics
• Pattern tier system — patterns auto-classified as A:ELITE, B:TRADEABLE, C:MARGINAL, or D:AVOID based on historical win rate
• Session quality classification — sessions ranked as PRIME, FAVORABLE, NEUTRAL, or UNFAVORABLE
• Signal scoring (0-100) — each live signal scored using TF-specific pattern WR, session gradient, confluence bonus, tier bonus, and MFE bonus
• Confluence scoring (0-6 with D1 filter, 0-7 without) — each backtest detection gets a confluence score based on trend alignment, volume, S/R context, RSI extreme, swing level, and session quality (D1 trend factor is skipped when the D1 trend filter is active, since it's already guaranteed)
• Support/Resistance context — swing high/low detection tags each signal as near_support, near_resistance, at_swing_low, or at_swing_high
• RSI context — RSI(14) computed at each detection; oversold/overbought contributes to confluence
• Variable R:R by pattern — configurable rr_by_pattern dict overrides TP multiplier per pattern
• MAE/MFE tracking — Max Adverse Excursion and Max Favorable Excursion in R-multiples per trade
• Time-to-SL/TP — bars until SL or TP hit, enabling trade management optimization
• Open-proximity SL/TP resolution — when both SL and TP are within a candle's range, the level closer to the open price is assumed hit first (replaces the old candle-direction heuristic)
• Wilder's ATR smoothing — standard industry ATR method (alpha = 1/period), matching MT5's built-in indicator
• Enriched stats JSON — latest_stats_multitf.json now includes per-TF patterns, sessions, cross-stats, and confluence breakdown (scanner starts instantly, no CSV re-parse)
• D1 forward window extended — D1 forward evaluation increased from 5 to 20 candles (4 trading weeks) for meaningful D1 stats
• Historical edge dashboard — displayed at scanner startup showing top setups, pattern x session combos, Tier D avoid list, and recommended live setups
• Per-timeframe WR columns — pattern table shows win rate broken down by M5/M15/H1/H4/D1 so you can see which TF each pattern performs best on
• Stop Loss / Take Profit based on ATR (configurable multiplier, R:R ratio)
• Higher-timeframe ATR for fast timeframes (M5/M15 automatically use H1 ATR for realistic SL/TP)
• Entry verification (stop orders only filled if price touches entry on the next candle)
• Forward evaluation with intra-candle path simulation — avoids look-ahead bias
• R-level tracking (up to R5) and hit-rate analysis
• Volume confirmation (optional)
• D1 trend filter (enabled by default) — requires the daily SMA 20 trend to align with the pattern direction
• Deduplication — picks the highest-priority pattern per candle
• Session classification (Asia, Pacific, London Open, London Morning, London/NY Overlap, NY Afternoon)
• Full backtests over date ranges — CSV reports, summary tables, JSON stats cache, and text reports
• Live scanner — monitors all active timeframes and prints formatted alerts when a new candle closes
• Sound alerts (Windows only) — high-Hz triple beep for STRONG BUY, low-Hz triple beep for STRONG SELL; starts muted, type m + Enter to toggle
• Position sizing (risk-based, standard lots) displayed in alerts
• Auto-reconnect with exponential backoff if MT5 connection drops

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Installation

1. Install MetaTrader 5

2. Install Python dependencies:

   pip install MetaTrader5 pandas numpy colorama python-dotenv
   

3. Copy mt5_multitf_pattern_scanner.py into your project folder.

4. Create a .env file in the same directory as the script:

   MT5_PATH=C:\Program Files\Broker\terminal64.exe
   MT5_ACCOUNT=12345678
   MT5_PASSWORD=YourPassword
   MT5_SERVER=YourBrokerServer1
   

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Quick Start

Step 1 — Run the Backtest

The backtest generates the probability data that powers the live scanner's pattern tiers, signal scores, and historical edge display. Always run the backtest first.

python mt5_multitf_pattern_scanner.py --mode fullbacktest --from 2025-06-01 --to 2026-05-15

This scans all 5 timeframes (M5, M15, H1, H4, D1) over the date range and saves:

• Per-TF CSV files (detections, pattern summary, session summary) in ./backtest_results/
• latest_stats_multitf.json — the enriched stats cache the live scanner loads at startup (now includes per-TF patterns, sessions, cross-stats, and confluence breakdown)

With D1 trend filter enabled (default), only signals that aligned with the daily trend are counted. This gives the most accurate stats for live trading.

Step 2 — Run the Live Scanner

python mt5_multitf_pattern_scanner.py --mode live

The scanner starts, loads the backtest stats, and displays the Historical Setups Dashboard:

• Overall and per-timeframe win rates
• Pattern tiers with per-TF WR columns (M5 | M15 | H1 | H4 | D1)
• Session quality rankings
• Top pattern x session combos
• Tier D patterns to avoid
• Recommended live setups with signal scores

Then it monitors all timeframes and alerts on every new candle close when a pattern is detected, showing:

• Pattern name, tier, direction, session, D1 trend alignment
• Entry, SL, TP with pip distances and R:R ratio
• Prob(TP) percentage based on historical SL/TP hit rates
• Historical edge breakdown (pattern WR, session WR, cross-stat WR, signal score)
• Risk-based position sizing

Sound alerts start muted by default. Type m + Enter in the terminal to unmute and hear audio alerts for strong signals. See Sound Alerts for details.

Press Ctrl+C to stop.

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Usage

Full Backtest (date-ranged)

Runs a complete backtest across all active timeframes over a specified date range. This is the primary way to generate stats for the live scanner.

# All timeframes, Jan 2025 to May 2026
python mt5_multitf_pattern_scanner.py --mode fullbacktest --from 2025-01-01 --to 2026-05-14

# Specific timeframes only
python mt5_multitf_pattern_scanner.py --mode fullbacktest --timeframes H4 D1 --from 2025-01-01 --to 2026-05-14

# With D1 trend filter ON (default) and volume filter
python mt5_multitf_pattern_scanner.py --mode fullbacktest --from 2025-01-01 --to 2026-05-14 --d1-trend-filter --volume-filter

# Without D1 trend filter
python mt5_multitf_pattern_scanner.py --mode fullbacktest --from 2025-01-01 --to 2026-05-14 --no-d1-trend-filter

Output: ./backtest_results/ (change with --output)

Quick Backtest (last N bars)

Fast snapshot on the most recent N candles (default 500) for a single timeframe.

python mt5_multitf_pattern_scanner.py --mode backtest --bars 500

Live Scanner

# All timeframes (default)
python mt5_multitf_pattern_scanner.py --mode live

# Specific timeframes
python mt5_multitf_pattern_scanner.py --mode live --timeframes H1 H4 D1

# Without D1 trend filter (must match how backtest was run)
python mt5_multitf_pattern_scanner.py --mode live --no-d1-trend-filter

One-Shot Scan

Scan the latest closed candle on all active timeframes and exit.

python mt5_multitf_pattern_scanner.py --mode scan

Test Sound Alerts

Play both the STRONG BUY and STRONG SELL test beeps to verify audio is working, then exit.

python mt5_multitf_pattern_scanner.py --test-sound

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Sound Alerts

The scanner includes Windows-only sound alerts so you don't have to stare at the screen waiting for strong signals.

How it works

• STRONG BUY (Bullish signal with score >= 65) — triple high-Hz beep (1200 Hz by default)
• STRONG SELL (Bearish signal with score >= 65) — triple low-Hz beep (400 Hz by default)
• The scanner starts muted — you must explicitly unmute to hear alerts
• Type m + Enter in the terminal to toggle mute/unmute at any time
• The keyboard listener runs in a background thread, so there is no delay — it responds instantly

Startup message

When the live scanner starts with sound enabled, you will see:

Sound Alerts: ENABLED | Buy: 1200Hz | Sell: 400Hz | Threshold: 65
Sound is MUTED — Type "m" + Enter to unmute

Toggling mute

Type m and press Enter at any time while the scanner is running:

Sound UNMUTED  |  Type 'm' + Enter to toggle
Sound MUTED    |  Type 'm' + Enter to toggle

Testing sound

Before relying on alerts, verify your audio works:

python mt5_multitf_pattern_scanner.py --test-sound

This temporarily unmutes, plays both test beeps, then restores the muted state.

Sound configuration

These settings are in the CFG dict at the top of the script (not exposed as CLI args):

Key	Default	Description
sound_enabled	True	Master switch — set False to disable all sound
sound_buy_hz	1200	Frequency in Hz for STRONG BUY triple beep
sound_sell_hz	400	Frequency in Hz for STRONG SELL triple beep
sound_beep_duration	150	Duration of each individual beep in milliseconds
sound_beep_pause	100	Pause between beeps in milliseconds
sound_strong_threshold	65.0	Signal score must be >= this to trigger a sound alert

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v7 Enhancements

Open-Proximity SL/TP Resolution

When both SL and TP fall within a single candle's range, the old heuristic used the candle's direction (bullish/bearish close) to decide which was hit first — this is look-ahead bias. The new heuristic uses open-proximity: whichever level is closer to the candle's open price was likely hit first. This is more realistic and corrects a 2-5% WR distortion.

Confluence Scoring (0-6 with D1 filter, 0-7 without)

Each backtest detection receives a confluence score based on how many confirming factors align:

Factor	+1 When
Trend alignment	Local trend agrees with trade direction
D1 trend alignment	Daily trend agrees with trade direction (skipped when --d1-trend-filter is active)
Volume confirmation	Signal candle has above-average volume
S/R context	Near support (bullish) or near resistance (bearish)
RSI extreme	RSI < 35 for bullish, RSI > 65 for bearish
At swing level	At swing low (bullish) or swing high (bearish)
Session quality	London/NY Overlap or London Open session

Why skip D1 trend when the filter is active? When --d1-trend-filter is on (the default), every signal already has D1 trend alignment guaranteed by the filter. Counting it as a confluence factor would inflate every score by +1 and destroy score differentiation. With the filter active, the effective range is 0-6; without the filter, it's 0-7.

The backtest report and JSON include confluence breakdowns, e.g. "Confluence >= 4: 72% WR vs Confluence 0-2: 48% WR".

Variable R:R by Pattern

Different patterns have different optimal R:R profiles. Configure overrides in CFG:

'rr_by_pattern': {
    'Bullish Engulfing': 1.5,    # Quick scalp
    'Morning Star': 2.5,         # Larger move expected
    'Three White Soldiers': 3.0, # Strong continuation
},

When a pattern is listed here, its TP multiplier is overridden. The RR_Override column in the detections CSV shows which patterns used overrides.

MAE/MFE Tracking

Every trade now records:

• MAE (Max Adverse Excursion) — worst drawdown in R-multiples before the trade closed
• MFE (Max Favorable Excursion) — best profit in R-multiples before the trade closed

This enables trade management optimization like: "Move SL to breakeven after price reaches 1R" or "If MAE exceeds 0.8R, the trade has low probability of reaching TP".

Time-to-SL/TP

Each trade records Bars_to_SL and Bars_to_TP — the number of forward candles until SL or TP was hit. This enables:

• Early exit strategies: "If not in profit after 8 M5 candles, close for breakeven"
• Trailing stop timing: "Move SL to breakeven after 4 H4 candles"

Support/Resistance Context

The backtest now detects swing highs and lows (using a 5-bar local extreme window over the last 50 bars) and tags each detection with:

• Near_Support — price within 1 ATR of a swing low
• Near_Resistance — price within 1 ATR of a swing high
• At_Swing_Low — candle low is the lowest in the lookback window
• At_Swing_High — candle high is the highest in the lookback window

Patterns near support/resistance have dramatically different win rates.

RSI Context

RSI(14) is computed at each detection using Wilder's smoothing method. The value is stored in the RSI column and contributes to confluence scoring (oversold for bullish, overbought for bearish).

Enriched Stats JSON

latest_stats_multitf.json now includes per-TF breakdown of:

{
  "timeframes": {
    "H4": {
      "overall": { "win_rate": 53.6, "total_signals": 252, "avg_mae_r": 0.45, "avg_mfe_r": 0.72, "avg_bars_to_tp": 5.2 },
      "patterns": { "Bullish Engulfing": { "win_rate": 58.2, "total": 312 } },
      "sessions": { "London/NY Overlap": { "win_rate": 55.1, "signals": 89 } },
      "cross": { "Bullish Engulfing|London/NY Overlap": { "win_rate": 62.3, "signals": 14 } },
      "confluence": { "3": { "win_rate": 68.2, "signals": 42 }, "0": { "win_rate": 44.1, "signals": 31 } }
    }
  }
}

The live scanner now loads all data from JSON — no CSV re-parsing at startup, so the scanner starts instantly.

Improved Signal Scoring

The v7 score formula fixes the old formula's problems:

Factor	v6 (old)	v7 (new)
Base	WR * confidence (low-sample patterns got lower base)	Raw WR as base (no multiplication)
Sample size	Confidence multiplier	Sample penalty (-15 for small samples, 0 for 30+)
Session	Binary +10/-10	Proportional gradient based on session WR
R-factor	min(amr, 2.0) * 10 (up to +20, too large)	MFE bonus +1/+3 for amr >= 0.5/0.8
Confluence	Not used	+5 if high-confluence signals have WR >= 55%
TF-specific	Used merged stats	Prefers TF-specific stats when available

D1 Forward Window Fix

D1 forward evaluation was only 5 candles (5 trading days). Since D1 ATR-based SL/TP often needs 2-4 weeks to resolve, this produced meaningless D1 stats (avg_max_r = 0.12R was an artifact). Now set to 20 candles (4 trading weeks).

Wilder's ATR Smoothing

ATR now uses Wilder's exponential smoothing (alpha = 1/period) instead of simple moving average. This matches MT5's built-in ATR indicator and the industry standard. The difference from SMA can be 5-15% on SL/TP sizing.

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How Backtest Stats Flow Into the Live Scanner

1. Backtest creates per-TF CSV files and the enriched latest_stats_multitf.json (includes per-TF patterns, sessions, cross-stats, confluence breakdown)
2. Live scanner calls load_latest_backtest_stats() at startup, which:
   • Reads latest_stats_multitf.json — if it has per-TF pattern/session/cross data (v7+), loads directly without CSV parsing
   • Falls back to CSV parsing only for older JSON formats
   • Caches results for 4 hours (configurable via stats_cache_hours)
3. Dashboard displays: overall WR, per-TF WR table, pattern tiers with per-TF columns, session quality, top cross-stats, avoid list, recommended setups
4. Each live signal is enriched with: pattern tier badge, quality summary line, Prob(TP), historical edge breakdown, TF-specific signal score, and confluence context

Important

: The D1 trend filter setting must match between backtest and live mode. If you run the backtest with --d1-trend-filter (default), run live with --mode live (also default). If you run backtest with --no-d1-trend-filter, run live with --no-d1-trend-filter.

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Configuration

Command-Line Arguments

Argument	Description	Default
--symbol	Trading symbol	EURUSD
--timeframes	Active timeframes	M5 M15 H1 H4 D1
--atr	ATR period	14
--sl	Stop loss multiplier (x ATR)	1.5
--tp	Take profit multiplier (x ATR)	1.5
--forward	Forward evaluation candles	Scaled per TF
--d1-trend-filter	Require D1 SMA trend alignment	True
--no-d1-trend-filter	Disable D1 trend filter
--d1-sma-period	D1 trend SMA period	20
--volume-filter	Enable volume confirmation	False
--no-volume-filter	Disable volume filter (default)
--volume-ma-period	Volume MA period	20
--volume-threshold	Volume threshold ratio	1.0
--account-balance	Account size for position sizing	100000
--risk-percent	Risk % of account per trade	1.0
--min-signal-score	Minimum signal score to display (0-100)	0
--alert-only-strong	Only alert on strong signals	False
--test-sound	Play STRONG BUY and STRONG SELL test beeps, then exit
--output	Backtest output directory	./backtest_results

CFG Config (in script)

These are set in the CFG dict at the top of the script:

Key	Default	Description
rr_by_pattern	{}	Variable R:R overrides — map pattern name to TP multiplier
sound_enabled	True	Master switch for sound alerts
sound_buy_hz	1200	Hz for STRONG BUY triple beep
sound_sell_hz	400	Hz for STRONG SELL triple beep
sound_beep_duration	150	Duration per beep in ms
sound_beep_pause	100	Pause between beeps in ms
sound_strong_threshold	65.0	Min signal score to trigger sound

Pattern Thresholds

Argument	Description	Default
--doji-body-ratio	Max body/shadow ratio for Doji	0.1
--spinning-top-body-ratio	Max body ratio for Spinning Top	0.33
--marubozu-wick-ratio	Max wick ratio for Marubozu	0.05
--hammer-lower-wick-ratio	Min lower wick ratio for Hammer	0.6
--hammer-upper-wick-ratio	Max upper wick ratio for Hammer	0.33
--long-candle-ratio	Min body/shadow ratio for long candle	0.6
--small-candle-ratio	Max body/shadow ratio for small candle	0.3
--tweezer-tolerance	Tweezer tolerance in pips	0.5

See --help for the full list of arguments.

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Signal Scoring System (v7)

Each live signal is scored 0-100 based on:

Factor	Description
Base score	Raw pattern win rate (0-100)
Sample penalty	-15 to 0 based on sample size (0 at 30+ signals, -20 below minimum)
Session gradient	Proportional bonus/penalty based on session WR (e.g., +8 at 60% WR, -8 at 40% WR)
Confluence bonus	+5 if high-confluence signals (score >= 3) have WR >= 55%
Tier bonus	+5 for Tier A, +3 for Tier B
MFE bonus	+1 for avg_max_r >= 0.5, +3 for >= 0.8

Per-TF scoring: When tf_label is available (live scanner), the score prefers TF-specific pattern stats over merged aggregate stats. A Bullish Engulfing on H4 (58% WR) gets a different score than the same pattern on M5 (52% WR).

Patterns below --min-signal-score are filtered out (default: 0, i.e. show all).

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Pattern Tiers

Tier	WR Range	Meaning
A: ELITE	>= 58% AND n >= 30 AND avg_max_r >= 0.35	Highest edge, trade with confidence
B: TRADEABLE	>= 50% AND n >= 10 AND avg_max_r >= 0.25	Solid edge, reliable setups
C: MARGINAL	>= 40%	Use only with strong confluence
D: AVOID	< 40% OR insufficient data	Negative edge, skip these

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Output Files

Backtest Results (./backtest_results/)

File	Description
EURUSD_{TF}_{date}_to_{date}_detections.csv	Every pattern detected with entry, SL, TP, outcome, R-levels, MAE/MFE, RSI, S/R context, confluence score
EURUSD_{TF}_{date}_to_{date}_pattern_summary.csv	Per-pattern stats: WR, signals, SL/TP hit %, R-level hit rates, avg MAE/MFE, avg confluence
EURUSD_{TF}_{date}_to_{date}_session_summary.csv	Per-session stats: WR, signals, avg SL, TP hit %
EURUSD_{TF}_{date}_to_{date}_report.txt	Human-readable text report
latest_stats_multitf.json	Enriched per-TF stats cache: overall, patterns, sessions, cross, confluence breakdown

New Detection CSV Columns (v7)

Column	Description
Bars_to_SL	Number of forward candles until SL hit (None if not hit)
Bars_to_TP	Number of forward candles until TP hit (None if not hit)
MAE_R	Max Adverse Excursion in R-multiples
MFE_R	Max Favorable Excursion in R-multiples
RSI	RSI(14) value at the signal candle
Near_Support	True if price within 1 ATR of a swing low
Near_Resistance	True if price within 1 ATR of a swing high
At_Swing_Low	True if candle low is the lowest in lookback window
At_Swing_High	True if candle high is the highest in lookback window
Confluence_Score	0-7 confluence score
Confluence_Factors	Pipe-separated list of contributing factors (e.g., trend|d1_trend|support)
RR_Override	Pattern name if variable R:R was applied, empty string otherwise

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Session Classification

Session	Broker Time (UTC+2/3)	Description
Pacific	00:00 - 07:00	Low liquidity, Sydney/Tokyo overlap
Asia	07:00 - 00:00	Tokyo session
London Open	07:00 - 09:00	High volatility London open
London Morning	09:00 - 12:00	Active London morning
London/NY Overlap	12:00 - 17:00	Highest liquidity window
NY Afternoon	17:00 - 21:00	NY afternoon, declining volume

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Timezone Notes

• Log timestamps ([HH:MM:SS]) use your local computer time
• Candle close times and "Next:" candle times use broker server time
• Session classification uses broker server time hours
• This means candle times will differ from your local clock by your timezone offset