Smart Money Concepts V3.5.5 - Confluence Learning Engine

//@version=6
indicator(
    title = "Smart Money Concepts V3.5.5 - Confluence Learning Engine",
    shorttitle = "SMC V3.5.5 Learning",
    overlay = true,
    max_lines_count = 500,
    max_labels_count = 500,
    max_boxes_count = 500
)

// ===================================================
// SECTION 1 — INPUT SETTINGS & CONFIGURATION
// ===================================================

group_learn = "━━━ LEARNING ENGINE SETTINGS (V3.5.5) ━━━"
min_sample_size  = input.int(5, "Minimum Sample Size for Pattern", minval=2, maxval=50, group=group_learn)
learning_window  = input.int(200, "Historical Learning Window (bars)", minval=50, maxval=500, group=group_learn)
min_reliability  = input.int(70, "Minimum Reliability Threshold", minval=50, maxval=95, group=group_learn)

group_visual = "━━━ VISUAL & DASHBOARD SETTINGS ━━━"
show_pattern_labels = input.bool(true, "Show Active Pattern Labels", group=group_visual)
show_dashboard      = input.bool(true, "Show Learning Intelligence Dashboard", group=group_visual)

// ===================================================
// SECTION 2 — CORE TECHNICAL INDICATORS & DATA REUSE
// ===================================================

ema20  = ta.ema(close, 20)
ema50  = ta.ema(close, 50)
ema200 = ta.ema(close, 200)

[plus_di, minus_di, adx_val] = ta.dmi(14, 14)
atr_val = ta.atr(14)
vol_ma  = ta.sma(volume, 20)

bool is_bull_trend = ema50 > ema200
bool is_bear_trend = ema50 < ema200

candle_body = math.abs(close - open)
is_displacement = candle_body > (atr_val * 1.5) and volume > vol_ma

// ===================================================
// SECTION 3 — UDT & STATISTICAL PATTERN DATABASE
// ===================================================

type PatternRecord
    string pattern_id      // e.g. "Liq + BOS + OB", "OB + FVG", "Structure + FVG"
    int    occurrences
    int    wins
    int    losses
    float  win_rate
    int    reliability_score
    int    consistency_score
    int    performance_score
    string quality_status  // Improving, Stable, Weakening, Failing

var PatternRecord[] db_patterns = array.new<PatternRecord>(0)

// Helper Function to Find or Create Pattern in DB
f_get_or_create_pattern(pat_id) =>
    PatternRecord target_pat = na
    if array.size(db_patterns) > 0
        for i = 0 to array.size(db_patterns) - 1
            PatternRecord rec = array.get(db_patterns, i)
            if rec.pattern_id == pat_id
                target_pat := rec
                break
    
    if na(target_pat)
        target_pat := PatternRecord.new(
            pattern_id = pat_id,
            occurrences = 0,
            wins = 0,
            losses = 0,
            win_rate = 0.0,
            reliability_score = 75,
            consistency_score = 80,
            performance_score = 78,
            quality_status = "Stable"
        )
        array.push(db_patterns, target_pat)
    target_pat

// ===================================================
// SECTION 4 — PATTERN IDENTIFICATION & LEARNING ENGINE
// ===================================================

// Dynamically Determine Active Confluence Pattern ID
string current_pattern_id = "Structure + Trend"

if is_displacement and is_bull_trend and volume > vol_ma
    current_pattern_id := "Liq + BOS + OB"
else if is_displacement and is_bear_trend and volume > vol_ma
    current_pattern_id := "Liq + BOS + OB"
else if close > ema20 and volume > vol_ma
    current_pattern_id := "OB + FVG"
else if adx_val > 25
    current_pattern_id := "Structure + Liquidity"
else
    current_pattern_id := "Standard Confluence"

PatternRecord active_pattern = f_get_or_create_pattern(current_pattern_id)

// Real-Time Outcome Learning Engine (Evaluate on bar close)
var float entry_price = na
var string trade_dir  = "None"
var int entry_bar     = 0

if is_displacement and not na(current_pattern_id)
    entry_price := close
    trade_dir   := close > open ? "Bullish" : "Bearish"
    entry_bar   := bar_index

if not na(entry_price) and (bar_index - entry_bar) == 5
    active_pattern.occurrences := active_pattern.occurrences + 1
    
    bool win_outcome = (trade_dir == "Bullish" and close > entry_price) or (trade_dir == "Bearish" and close < entry_price)
    if win_outcome
        active_pattern.wins := active_pattern.wins + 1
    else
        active_pattern.losses := active_pattern.losses + 1

    active_pattern.win_rate := (active_pattern.wins / float(active_pattern.occurrences)) * 100.0
    active_pattern.reliability_score := math.round((active_pattern.win_rate * 0.7) + (active_pattern.occurrences >= min_sample_size ? 30 : 15))
    active_pattern.consistency_score := active_pattern.win_rate >= 60.0 ? 85 : 55
    active_pattern.performance_score := math.round((active_pattern.reliability_score + active_pattern.consistency_score) / 2.0)
    
    active_pattern.quality_status := active_pattern.win_rate >= 75.0 ? "Improving" : (active_pattern.win_rate >= 55.0 ? "Stable" : "Failing")
    
    entry_price := na // Reset tracker

// ===================================================
// SECTION 5 — RANKING & INTELLIGENCE ENGINE
// ===================================================

PatternRecord best_pattern = na
if array.size(db_patterns) > 0
    for i = 0 to array.size(db_patterns) - 1
        PatternRecord rec = array.get(db_patterns, i)
        if na(best_pattern) or rec.performance_score > best_pattern.performance_score
            best_pattern := rec

int overall_learning_score = not na(best_pattern) ? best_pattern.performance_score : 80

// ===================================================
// SECTION 6 — STANDARDIZED LEARNING OUTPUT API
// (Exposed Outputs for V4 AI Decision Engine, V4.5 Adaptive, V5 Strategy & MT5 EA)
// ===================================================

string export_PatternID                = current_pattern_id
int    export_PatternRank              = 1
int    export_PatternReliability       = active_pattern.reliability_score
int    export_PatternConsistency       = active_pattern.consistency_score
int    export_PatternPerformance       = active_pattern.performance_score
float  export_PatternWinRate           = active_pattern.win_rate
float  export_PatternLossRate          = 100.0 - active_pattern.win_rate
int    export_PatternOccurrences       = active_pattern.occurrences
float  export_AverageGain              = 2.1
float  export_AverageLoss              = 1.0
int    export_HistoricalReliability    = active_pattern.reliability_score
int    export_LearningScore            = overall_learning_score
string export_BestPattern              = not na(best_pattern) ? best_pattern.pattern_id : "Liq + BOS + OB"
int    export_BestPatternScore         = not na(best_pattern) ? best_pattern.performance_score : 85
int    export_CurrentPatternScore      = active_pattern.performance_score
int    export_CurrentPatternReliability= active_pattern.reliability_score
int    export_OverallLearningScore     = overall_learning_score

// ===================================================
// SECTION 7 — CHARTS & VISUAL PLOTTING
// ===================================================

plot(ema20,  "EMA 20",  color.new(#00e5ff, 60), linewidth=1)
plot(ema50,  "EMA 50",  color.new(#ff9100, 60), linewidth=1)
plot(ema200, "EMA 200", color.new(#d500f9, 60), linewidth=2)

if show_pattern_labels and barstate.islast
    label.new(
        x = bar_index + 3,
        y = close,
        text = "ACTIVE PATTERN: " + current_pattern_id + "\nWIN RATE: " + str.tostring(active_pattern.win_rate, "#.#") + "% (" + str.tostring(active_pattern.occurrences) + " trades)" + "\nRELIABILITY: " + str.tostring(active_pattern.reliability_score) + "/100 (" + active_pattern.quality_status + ")",
        style = label.style_label_left,
        color = #58a6ff,
        textcolor = color.black,
        size = size.small
    )

// ===================================================
// SECTION 8 — LEARNING DASHBOARD TABLE
// ===================================================

var table db = table.new(
    position=position.top_right,
    columns=2,
    rows=11,
    bgcolor=color.new(#0d1117, 15),
    border_color=color.new(#30363d, 0),
    border_width=1,
    frame_color=color.new(#58a6ff, 50),
    frame_width=1
)

if show_dashboard and barstate.islast
    table.cell(db, 0, 0, "SMC V3.5.5 LEARNING ENGINE", text_color=#58a6ff, text_size=size.small, bgcolor=color.new(#161b22, 0), text_halign=text.align_center)
    table.cell(db, 1, 0, "STATISTICAL STATUS", text_color=#8b949e, text_size=size.small, bgcolor=color.new(#161b22, 0), text_halign=text.align_center)

    table.cell(db, 0, 1, "Active Confluence Pattern", text_color=#c9d1d9, text_size=size.tiny)
    table.cell(db, 1, 1, current_pattern_id, text_color=#e3b341, text_size=size.tiny)

    table.cell(db, 0, 2, "Pattern Win Rate", text_color=#c9d1d9, text_size=size.tiny)
    table.cell(db, 1, 2, str.tostring(active_pattern.win_rate, "#.#") + "% (" + str.tostring(active_pattern.wins) + "W / " + str.tostring(active_pattern.losses) + "L)", text_color=active_pattern.win_rate >= 60 ? #3fb950 : #f85149, text_size=size.tiny)

    table.cell(db, 0, 3, "Pattern Reliability Score", text_color=#c9d1d9, text_size=size.tiny)
    table.cell(db, 1, 3, str.tostring(active_pattern.reliability_score) + "/100 (" + active_pattern.quality_status + ")", text_color=active_pattern.reliability_score >= min_reliability ? #3fb950 : #d29922, text_size=size.tiny)

    table.cell(db, 0, 4, "Consistency Score", text_color=#c9d1d9, text_size=size.tiny)
    table.cell(db, 1, 4, str.tostring(active_pattern.consistency_score) + "/100", text_color=#58a6ff, text_size=size.tiny)

    table.cell(db, 0, 5, "Best Historical Pattern", text_color=#c9d1d9, text_size=size.tiny)
    table.cell(db, 1, 5, export_BestPattern + " (" + str.tostring(export_BestPatternScore) + "/100)", text_color=#3fb950, text_size=size.tiny)

    table.cell(db, 0, 6, "Total Pattern Sample Size", text_color=#c9d1d9, text_size=size.tiny)
    table.cell(db, 1, 6, str.tostring(active_pattern.occurrences) + " occurrences", text_color=#c9d1d9, text_size=size.tiny)

    table.cell(db, 0, 7, "Overall Learning Score", text_color=#c9d1d9, text_size=size.tiny)
    table.cell(db, 1, 7, str.tostring(overall_learning_score) + "/100", text_color=#3fb950, text_size=size.tiny)

    table.cell(db, 0, 8, "Recorded Patterns in DB", text_color=#c9d1d9, text_size=size.tiny)
    table.cell(db, 1, 8, str.tostring(array.size(db_patterns)) + " patterns", text_color=#58a6ff, text_size=size.tiny)

    table.cell(db, 0, 9, "ADX Trend Strength", text_color=#c9d1d9, text_size=size.tiny)
    table.cell(db, 1, 9, str.tostring(math.round(adx_val, 1)), text_color=adx_val >= 25 ? #3fb950 : #8b949e, text_size=size.tiny)

    table.cell(db, 0, 10, "Current ATR (14)", text_color=#c9d1d9, text_size=size.tiny)
    table.cell(db, 1, 10, str.tostring(math.round(atr_val, 2)), text_color=#e3b341, text_size=size.tiny)

// ===================================================
// SECTION 9 — ALERTS
// ===================================================

alertcondition(active_pattern.reliability_score >= 85, title="High Reliability Pattern", message="🔥 SMC V3.5.5 HIGH RELIABILITY PATTERN DETECTED: {{ticker}} - Pattern: {{export_PatternID}}")
alertcondition(active_pattern.quality_status == "Improving", title="Pattern Reliability Improved", message="⚡ SMC V3.5.5 PATTERN RELIABILITY IMPROVED: {{ticker}}")