!pip install pandas numpy plotly
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

def generate_data(periods):
    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 # Ensure high is always > low
        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)

def volume_momentum_confirmation(
    df: pd.DataFrame,
    roc_period: int = 5,
    vol_window: int = 20,
    vol_multiplier: float = 1.2,
) -> pd.DataFrame:
    """
    Calculates volume-confirmed momentum signals.

    Args:
        df (pd.DataFrame): Input DataFrame with 'datetime', 'close', and 'volume' columns.
        roc_period (int): The period over which to calculate the Rate of Change (ROC).
        vol_window (int): The window size for calculating the rolling average of volume.
        vol_multiplier (float): Multiplier for the average volume to determine 'high volume'.

    Returns:
        pd.DataFrame: The original DataFrame with added 'roc', 'avg_volume', 'high_volume',
                      and 'signal' columns.
                      'signal' will be 1 for buy, -1 for sell, and 0 for no signal.
    """
    df = df.copy().sort_values("datetime", ignore_index=True)

    # Calculate Rate of Change (Momentum Condition)
    df["roc"] = df["close"].pct_change(roc_period) * 100

    # Calculate Average Volume and High Volume Condition
    df["avg_volume"] = df["volume"].rolling(vol_window).mean()
    df["high_volume"] = df["volume"] > vol_multiplier * df["avg_volume"]

    # Generate Signals
    # A buy signal (1) occurs when ROC is positive and volume is high.
    # A sell signal (-1) occurs when ROC is negative and volume is high.
    # Otherwise, there is no signal (0).
    df["signal"] = np.where(
        (df["roc"] > 0) & df["high_volume"],  1,
        np.where(
        (df["roc"] < 0) & df["high_volume"], -1, 0))
    return df

df_signals = volume_momentum_confirmation(df)

# The `vol_multiplier` parameter (e.g., 1.2) specifies the factor by which current volume must exceed the
# `vol_window`-period average for a momentum signal to be considered valid.
# A higher `vol_multiplier` value results in fewer but potentially higher-quality signals,
# whereas a lower value increases signal frequency.

print("--- Signal Distribution ---"); 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 + Signals", "Volume vs Average Volume"],
    row_heights=[0.65, 0.35])

# Candlestick chart for price
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)

# Buy signals
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 (+1)"), row=1, col=1)

# Sell signals
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 (−1)"), row=1, col=1)

# Volume bars
fig.add_trace(go.Bar(x=df_signals["datetime"], y=df_signals["volume"],
    name="Volume", marker_color="steelblue"), row=2, col=1)

# Average volume line
fig.add_trace(go.Scatter(x=df_signals["datetime"], y=df_signals["avg_volume"],
    mode="lines", name="Avg Volume", line=dict(color="orange", width=1.5)), row=2, col=1)

fig.update_layout(title_text="Volume-Confirmed Momentum Strategy",
    xaxis_rangeslider_visible=False, height=700, yaxis=dict(autorange=True))

fig.show()