Pattern Indicator Confluence System

import pandas as pd
import numpy as np
import plotly.graph_objects as go
from plotly.subplots import make_subplots

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 pattern_indicator_confluence_system(
    df: pd.DataFrame,
    rsi_period: int = 14,
    rsi_bull_level: float = 40.0,
    rsi_bear_level: float = 60.0,
) -> pd.DataFrame:
    """
    Combine candlestick pattern detection with RSI confirmation.

    Core logic
    ----------
    1. Detect Hammer (bullish) and Shooting Star (bearish) candle patterns
       using body/range and wick/body ratios.
    2. Compute RSI for momentum confirmation.
    3. Emit Buy (+1) only when a Hammer AND RSI < rsi_bull_level.
    4. Emit Sell (-1) only when a Shooting Star AND RSI > rsi_bear_level.

    Parameters
    ----------
    df : pd.DataFrame   OHLCV DataFrame.
    rsi_period : int    RSI calculation period.
    rsi_bull_level : float  RSI threshold for bullish confirmation.
    rsi_bear_level : float  RSI threshold for bearish confirmation.

    Returns
    -------
    pd.DataFrame with: pattern, rsi, pattern_signal, rsi_signal, signal.
    """
    df = df.copy().sort_values("datetime", ignore_index=True)

    # ── Candle geometry ───────────────────────────────────────────────────────
    df["body"]       = (df["close"] - df["open"]).abs()
    df["full_range"] = df["high"] - df["low"]
    df["upper_wick"] = df["high"] - df[["open","close"]].max(axis=1)
    df["lower_wick"] = df[["open","close"]].min(axis=1) - df["low"]
    range_safe = df["full_range"].replace(0, np.nan)
    body_safe  = df["body"].replace(0, np.nan)

    # ── Pattern detection ─────────────────────────────────────────────────────
    hammer = (
        (df["lower_wick"] >= 2 * body_safe) &
        (df[["open","close"]].min(axis=1) > df["low"] + 0.7 * df["full_range"])
    )
    shooting_star = (
        (df["upper_wick"] >= 2 * body_safe) &
        (df[["open","close"]].max(axis=1) < df["low"] + 0.3 * df["full_range"])
    )
    df["pattern"]        = "none"
    df["pattern_signal"] = 0
    df.loc[hammer,        "pattern"] = "hammer"
    df.loc[shooting_star, "pattern"] = "shooting_star"
    df.loc[hammer,        "pattern_signal"] =  1
    df.loc[shooting_star, "pattern_signal"] = -1

    # ── RSI ───────────────────────────────────────────────────────────────────
    delta = df["close"].diff()
    gain  = delta.clip(lower=0).rolling(rsi_period).mean()
    loss  = (-delta.clip(upper=0)).rolling(rsi_period).mean()
    df["rsi"] = 100 - 100 / (1 + gain / loss.replace(0, np.nan))
    df["rsi_signal"] = np.where(df["rsi"] < rsi_bull_level, 1,
                       np.where(df["rsi"] > rsi_bear_level, -1, 0))

    # ── Confluence signal ─────────────────────────────────────────────────────
    df["signal"] = 0
    df.loc[(df["pattern_signal"] == 1) & (df["rsi_signal"] == 1),  "signal"] =  1
    df.loc[(df["pattern_signal"] == -1) & (df["rsi_signal"] == -1), "signal"] = -1

    return df

df_signals = pattern_indicator_confluence_system(df)
print(df_signals["pattern"].value_counts())
print(df_signals["signal"].value_counts())

buy_signals  = df_signals[df_signals["signal"] ==  1]
sell_signals = df_signals[df_signals["signal"] == -1]
fig = make_subplots(rows=2, cols=1, shared_xaxes=True,
    subplot_titles=["Price + Confluence Signals", "RSI"],
    row_heights=[0.65, 0.35])
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)
fig.add_trace(go.Scatter(x=buy_signals["datetime"],  y=buy_signals["low"]  * 0.999,
    mode="markers", marker=dict(symbol="triangle-up",   size=10, color="green"), name="Buy"),  row=1, col=1)
fig.add_trace(go.Scatter(x=sell_signals["datetime"], y=sell_signals["high"] * 1.001,
    mode="markers", marker=dict(symbol="triangle-down", size=10, color="red"),   name="Sell"), row=1, col=1)
fig.add_trace(go.Scatter(x=df_signals["datetime"], y=df_signals["rsi"],
    mode="lines", name="RSI", line=dict(color="purple")), row=2, col=1)
for lvl, clr in [(40, "green"), (60, "red")]:
    fig.add_hline(y=lvl, line_dash="dot", line_color=clr, row=2, col=1)
fig.update_layout(title_text="Pattern + Indicator Confluence",
    xaxis_rangeslider_visible=False, height=700, xaxis2_title="Datetime")
fig.show()