!pip install pandas numpy plotly scipy

import warnings; warnings.filterwarnings("ignore")
import pandas as pd
import numpy as np
import plotly.graph_objects as go
from plotly.subplots import make_subplots
from scipy.signal import argrelextrema

def generate_data(periods: int) -> pd.DataFrame:
    """
    Generate synthetic OHLCV price data using a geometric random walk.

    Parameters
    ----------
    periods : int
        Number of 1-minute bars to generate.

    Returns
    -------
    pd.DataFrame
        DataFrame with columns: open, high, low, close, volume, datetime.
    """
    start_date     = pd.to_datetime("2024-01-01 00:00:00+00:00")
    datetime_index = pd.date_range(start_date, periods=periods, freq="1min", tz="UTC")
    price_data = []
    last_close = 42000
    for i in range(periods):
        open_price  = last_close + np.random.normal(0, last_close * 0.0005)
        close_price = open_price + np.random.normal(0, last_close * 0.005)
        body_high   = max(open_price, close_price)
        body_low    = min(open_price, close_price)
        high_price  = max(body_high + abs(np.random.normal(0, last_close * 0.002)), open_price, close_price)
        low_price   = min(body_low  - abs(np.random.normal(0, last_close * 0.002)), open_price, close_price)
        if high_price < low_price:
            high_price, low_price = low_price, high_price
        price_data.append({
            "open":  max(1, int(open_price)),
            "high":  max(1, int(high_price)),
            "low":   max(1, int(low_price)),
            "close": max(1, int(close_price)),
        })
        last_close = close_price
    df = pd.DataFrame(price_data, index=datetime_index)
    df.index.name = "datetime"
    df["volume"]   = np.random.uniform(100.0, 500.0, periods)
    df["datetime"] = df.index.to_series()
    return df.reset_index(drop=True)

df = generate_data(500)
display(df.head())

def market_structure_hh_ll(
    df: pd.DataFrame,
    order: int = 10,
) -> pd.DataFrame:
    """
    Classify market structure as uptrend, downtrend, or ranging using HH/HL/LH/LL.

    Core logic
    ----------
    1. Detect swing highs and lows via scipy argrelextrema.
    2. Label each swing high as HH (Higher High) or LH (Lower High) by
       comparing it with the immediately preceding swing high.
    3. Label each swing low as HL (Higher Low) or LL (Lower Low) similarly.
    4. Emit a bullish signal (+1) at bars where both the latest swing high is HH
       and the latest swing low is HL.  Emit bearish (-1) where both LH and LL.

    Parameters
    ----------
    df : pd.DataFrame
        OHLCV DataFrame with columns: open, high, low, close, volume, datetime.
    order : int
        Bars on each side required to qualify as a swing high or low.

    Returns
    -------
    pd.DataFrame
        Original DataFrame extended with: swing_high_label, swing_low_label,
        structure, signal.
    """
    df = df.copy().sort_values("datetime", ignore_index=True)
    df["swing_high_label"] = "none"
    df["swing_low_label"]  = "none"
    df["structure"]        = "ranging"
    df["signal"]           = 0

    highs = df["high"].values
    lows  = df["low"].values

    peak_idx   = argrelextrema(highs, np.greater, order=order)[0]
    trough_idx = argrelextrema(lows,  np.less,    order=order)[0]

    # ── Label swing highs ────────────────────────────────────────────────────
    for i in range(1, len(peak_idx)):
        prev_i, curr_i = peak_idx[i-1], peak_idx[i]
        label = "HH" if highs[curr_i] > highs[prev_i] else "LH"
        df.at[curr_i, "swing_high_label"] = label

    # ── Label swing lows ─────────────────────────────────────────────────────
    for i in range(1, len(trough_idx)):
        prev_i, curr_i = trough_idx[i-1], trough_idx[i]
        label = "HL" if lows[curr_i] > lows[prev_i] else "LL"
        df.at[curr_i, "swing_low_label"] = label

    # ── Derive structure and signal ──────────────────────────────────────────
    # Forward-fill the labels so every bar carries the latest swing classification
    df["_sh"] = df["swing_high_label"].replace("none", np.nan).ffill()
    df["_sl"] = df["swing_low_label"].replace("none", np.nan).ffill()

    uptrend   = (df["_sh"] == "HH") & (df["_sl"] == "HL")
    downtrend = (df["_sh"] == "LH") & (df["_sl"] == "LL")

    df.loc[uptrend,   "structure"] = "uptrend"
    df.loc[downtrend, "structure"] = "downtrend"
    df.loc[uptrend,   "signal"]   = 1
    df.loc[downtrend, "signal"]   = -1

    df.drop(columns=["_sh", "_sl"], inplace=True)
    return df

df_signals = market_structure_hh_ll(df, order=10)

print("--- Structure Distribution ---")
print(df_signals["structure"].value_counts())
print("\n--- Signal Distribution ---")
print(df_signals["signal"].value_counts())

fig = make_subplots(rows=1, cols=1, shared_xaxes=True,
    subplot_titles=["Price + Market Structure and Signals"]
)

colors = {"uptrend": "rgba(0,200,0,0.15)", "downtrend": "rgba(200,0,0,0.15)", "ranging": "rgba(128,128,128,0.05)"}

fig.add_trace(go.Candlestick(
    x=df_signals["datetime"],
    open=df_signals["open"], high=df_signals["high"],
    low=df_signals["low"],   close=df_signals["close"],
    name="Price"), row=1, col=1)

# Add background color for market structure
for struct, color in colors.items():
    mask = df_signals["structure"] == struct
    fig.add_trace(go.Scatter(
        x=df_signals.loc[mask, "datetime"],
        y=df_signals.loc[mask, "close"],
        mode="markers", marker=dict(color=color, size=4),
        name=struct,
        showlegend=False # Moved showlegend inside go.Scatter
    ),
        row=1, col=1
    )

# Add buy signals (green triangles above high)
buy_signals = df_signals[df_signals["signal"] == 1]
if not buy_signals.empty:
    fig.add_trace(go.Scatter(
        x=buy_signals["datetime"],
        y=buy_signals["high"] * 1.002, # Position slightly above the high
        mode="markers",
        marker=dict(symbol="triangle-up", size=10, color="green"),
        name="Buy Signal"),
        row=1, col=1
    )

# Add sell signals (red triangles below low)
sell_signals = df_signals[df_signals["signal"] == -1]
if not sell_signals.empty:
    fig.add_trace(go.Scatter(
        x=sell_signals["datetime"],
        y=sell_signals["low"] * 0.998, # Position slightly below the low
        mode="markers",
        marker=dict(symbol="triangle-down", size=10, color="red"),
        name="Sell Signal"),
        row=1, col=1
    )

fig.update_layout(
    title_text="Market Structure: Higher Highs / Lower Lows",
    xaxis_rangeslider_visible=False,
    height=700, xaxis_title="Datetime",
)
fig.show()