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feat: add TBN chart script, docs integration, and white theme
- Create scripts/charts/indicators/trendline_breakout_navigator.py using the shared white theme (generate_all compatible) - Add TBN to scripts/generate_docs.py INDICATOR_MAP - Add TBN to docs support-resistance overview grid and tables - Update notebook from dark (#131722) to white theme matching the project standard (bg=white, bull=#26a69a, bear=#ef5350) - Regenerate chart PNGs with the new theme
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‎analysis/indicators/trendline_breakout_navigator.html‎

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‎analysis/indicators/trendline_breakout_navigator.ipynb‎

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‎docs/content/indicators/support-resistance/overview.md‎

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</a>
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<a href="trendline-breakout-navigator" className="indicator-card">
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![Trendline Breakout Navigator](/img/indicators/trendline_breakout_navigator.png)
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Trendline Breakout Navigator
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</a>
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</div>
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## Indicators at a glance
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| [Liquidity Levels / Voids (VP)](liquidity-levels-voids) | 🟢 Real-time | `detection_length` bars | ≈ `detection_length` bars | Highlights volume-profile voids — price areas with little trading activity. Price tends to move quickly through voids. Use them to spot potential fast-move zones. |
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| [Internal & External Liquidity Zones](internal-external-liquidity-zones) | 🟢 Real-time | `2 × external_pivot_length + 1` bars | ≈ `external_pivot_length` bars | Distinguishes between internal (range-bound) and external (breakout) liquidity. Use it to understand whether price is targeting internal or external levels. |
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| [Premium / Discount Zones](premium-discount-zones) | 🟢 Real-time | `2 × swing_length + 1` bars | ≈ `swing_length` bars after the swing | Divides the current range into premium (upper) and discount (lower) zones. Buy in discount, sell in premium — the core SMC/ICT framework for directional bias. |
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| [Trendline Breakout Navigator](trendline-breakout-navigator) | 🔴 Lagging | `2 × swing_long + 1` bars | ≈ `swing_long` bars after the pivot | Multi-timeframe trendline detection. Constructs trendlines on HH/LL reversals at long, medium, and short swing lengths with a composite trend score (−3 to +3). |
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## Detailed descriptions
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Divides the current range into premium (upper) and discount (lower) zones. Buy in discount, sell in premium — the core SMC/ICT framework for directional bias.
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### [Trendline Breakout Navigator](trendline-breakout-navigator)
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> 🔴 **Lagging** — ≈ `swing_long` bars after the pivot
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>
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> **Warmup:** `2 × swing_long + 1` bars (default: 121 bars (swing_long=60))
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Multi-timeframe trendline detection ported from LuxAlgo PineScript. Constructs trendlines on HH/LL reversals at three swing lengths with a composite trend score (−3 to +3). Detects wick breaks and trend changes.

‎scripts/charts/indicators/trendline_breakout_navigator.py‎

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"""Trendline Breakout Navigator chart."""
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import numpy as np
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from pathlib import Path
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from scripts.charts.theme import (
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load_data, subplot_figure, apply_layout, save, COLORS,
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)
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import pyindicators as pi
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OUTPUT_IMAGE = "trendline_breakout_navigator.png"
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# Trendline colours
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TL_BULL = COLORS["bull"]
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TL_BEAR = COLORS["bear"]
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TL_BULL_MED = "rgba(8,153,129,0.60)"
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TL_BEAR_MED = "rgba(239,54,69,0.60)"
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TL_BULL_SHORT = "rgba(8,153,129,0.35)"
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TL_BEAR_SHORT = "rgba(239,54,69,0.35)"
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def generate(output_dir: Path | None = None,
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data_dir: Path | None = None) -> bool:
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df = load_data(dataset="btc_1d", data_dir=data_dir)
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df = pi.trendline_breakout_navigator(
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df, swing_long=60, swing_medium=30, swing_short=10,
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)
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df = pi.trendline_breakout_navigator_signal(df)
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fig = subplot_figure(df, row_heights=[0.70, 0.30])
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trend_long = df["tbn_trend_long"].values
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trend_med = df["tbn_trend_medium"].values
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trend_short = df["tbn_trend_short"].values
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val_long = df["tbn_value_long"].values
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val_med = df["tbn_value_medium"].values
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val_short = df["tbn_value_short"].values
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composite = (
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df["tbn_composite_trend"].fillna(0).astype(int).values
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)
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# ── Long trendlines (solid, thick) ──────────────────────────
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fig.add_scatter(
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x=df["Datetime"],
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y=np.where(trend_long == 1, val_long, np.nan),
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mode="lines",
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line=dict(color=TL_BULL, width=2.5),
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name="Long TL ↑",
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connectgaps=False,
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row=1, col=1,
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)
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fig.add_scatter(
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x=df["Datetime"],
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y=np.where(trend_long == -1, val_long, np.nan),
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mode="lines",
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line=dict(color=TL_BEAR, width=2.5),
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name="Long TL ↓",
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connectgaps=False,
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row=1, col=1,
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)
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# ── Medium trendlines (dashed) ──────────────────────────────
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fig.add_scatter(
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x=df["Datetime"],
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y=np.where(trend_med == 1, val_med, np.nan),
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mode="lines",
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line=dict(color=TL_BULL_MED, width=1.8, dash="dash"),
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name="Med TL ↑",
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connectgaps=False,
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row=1, col=1,
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)
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fig.add_scatter(
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x=df["Datetime"],
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y=np.where(trend_med == -1, val_med, np.nan),
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mode="lines",
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line=dict(color=TL_BEAR_MED, width=1.8, dash="dash"),
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name="Med TL ↓",
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connectgaps=False,
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row=1, col=1,
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)
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# ── Short trendlines (dotted, thin) ─────────────────────────
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fig.add_scatter(
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x=df["Datetime"],
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y=np.where(trend_short == 1, val_short, np.nan),
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mode="lines",
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line=dict(
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color=TL_BULL_SHORT, width=1.2, dash="dot",
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),
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name="Short TL ↑",
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connectgaps=False,
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row=1, col=1,
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)
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fig.add_scatter(
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x=df["Datetime"],
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y=np.where(trend_short == -1, val_short, np.nan),
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mode="lines",
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line=dict(
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color=TL_BEAR_SHORT, width=1.2, dash="dot",
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),
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name="Short TL ↓",
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connectgaps=False,
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row=1, col=1,
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)
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# ── HH / LL markers ────────────────────────────────────────
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hh = df["tbn_hh"] == 1
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ll = df["tbn_ll"] == 1
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if hh.any():
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fig.add_scatter(
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x=df.loc[hh, "Datetime"],
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y=df.loc[hh, "Low"] * 0.993,
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mode="markers", name="HH",
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marker=dict(
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symbol="triangle-up", size=10,
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color=COLORS["bull"],
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),
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row=1, col=1,
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)
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if ll.any():
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fig.add_scatter(
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x=df.loc[ll, "Datetime"],
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y=df.loc[ll, "High"] * 1.007,
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mode="markers", name="LL",
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marker=dict(
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symbol="triangle-down", size=10,
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color=COLORS["bear"],
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),
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row=1, col=1,
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)
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# ── Row 2: composite trend bar chart ────────────────────────
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comp_colors = [
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COLORS["bull"] if c > 0
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else COLORS["bear"] if c < 0
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else COLORS["grid"]
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for c in composite
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]
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fig.add_bar(
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x=df["Datetime"], y=composite,
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marker_color=comp_colors,
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showlegend=False, name="Composite",
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opacity=0.85,
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row=2, col=1,
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)
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fig.add_hline(
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y=0, line_dash="solid",
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line_color=COLORS["grid"], line_width=0.8,
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row=2, col=1,
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)
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apply_layout(
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fig,
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"Trendline Breakout Navigator",
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height=800,
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)
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fig.update_layout(showlegend=False)
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fig.update_yaxes(
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title_text="Composite",
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range=[-3.5, 3.5], dtick=1,
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row=2, col=1,
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)
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save(fig, OUTPUT_IMAGE, output_dir)
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return True

‎scripts/generate_docs.py‎

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"Premium / Discount Zones": (
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"indicators/support-resistance", "premium-discount-zones", 18
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),
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"Trendline Breakout Navigator": (
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"indicators/support-resistance",
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"trendline-breakout-navigator", 19
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),
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# ── Pattern Recognition ──
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"Detect Peaks": (
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"indicators/pattern-recognition", "detect-peaks", 1

‎static/images/indicators/trendline_breakout_navigator.png‎

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