import warnings; warnings.filterwarnings("ignore")
import pandas as pd
import numpy as np
import plotly.graph_objects as go

!pip install pandas numpy plotly

def generate_data(periods: int) -> pd.DataFrame:
    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 bollinger_band_reversion(
    df:      pd.DataFrame,
    window:  int   = 20,
    num_std: float = 2.0,
) -> pd.DataFrame:
    # Create a copy of the DataFrame and sort it by datetime to ensure correct rolling calculations.
    df = df.copy().sort_values("datetime", ignore_index=True)

    # Calculate the Middle Bollinger Band: a 'window'-period simple moving average of the 'close' price.
    df["bb_middle"] = df["close"].rolling(window).mean()
    # Calculate the rolling standard deviation of the 'close' price over the specified 'window'.
    df["bb_std"]    = df["close"].rolling(window).std()
    # Calculate the Upper Bollinger Band: Middle Band + (num_std * rolling standard deviation).
    df["bb_upper"]  = df["bb_middle"] + num_std * df["bb_std"]
    # Calculate the Lower Bollinger Band: Middle Band - (num_std * rolling standard deviation).
    df["bb_lower"]  = df["bb_middle"] - num_std * df["bb_std"]

    # %B: position of close within the bands (0=lower, 1=upper).
    # This normalizes the close price's position relative to the bands.
    df["pct_b"] = (df["close"] - df["bb_lower"]) / (df["bb_upper"] - df["bb_lower"])

    # Generate trading signals based on Bollinger Band mean reversion logic:
    # If 'close' price is below or touches the Lower Band, generate a buy signal (1).
    # If 'close' price is above or touches the Upper Band, generate a sell signal (-1).
    # Otherwise, if price is within the bands, generate no signal (0).
    df["signal"] = np.where(df["close"] <= df["bb_lower"],  1,
                   np.where(df["close"] >= df["bb_upper"], -1, 0))

    return df

df_signals = bollinger_band_reversion(df, window=20, num_std=2.0)

print("--- Signal Distribution ---")
print(df_signals["signal"].value_counts())
print("\n--- %B Statistics ---")
print(df_signals["pct_b"].describe().round(4))

from plotly.subplots import make_subplots

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 + Bollinger Bands", "%B"],
    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)

for band, color, name in [
    ("bb_upper", "rgba(0,0,255,0.3)", "Upper Band"),
    ("bb_middle","blue",              "Middle Band"),
    ("bb_lower", "rgba(0,0,255,0.3)", "Lower Band"),
]:
    fig.add_trace(go.Scatter(x=df_signals["datetime"], y=df_signals[band],
        mode="lines", name=name, line=dict(color=color, width=1)), 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 (+1)"),
    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 (−1)"),
    row=1, col=1)

fig.add_trace(go.Scatter(
    x=df_signals["datetime"], y=df_signals["pct_b"],
    mode="lines", name="%B", line=dict(color="purple", width=1)), row=2, col=1)
fig.add_hline(y=1, line_dash="dash", line_color="red",   row=2, col=1)
fig.add_hline(y=0, line_dash="dash", line_color="green", row=2, col=1)

fig.update_layout(
    title_text="Bollinger Band Mean Reversion Strategy",
    xaxis_rangeslider_visible=False,
    height=700, yaxis=dict(autorange=True),
    xaxis2_title="Datetime",
)
fig.show()