!pip install pandas numpy plotly

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

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 keltner_channel_reversion(
    df:         pd.DataFrame,
    ema_window: int   = 20,
    atr_window: int   = 10,
    multiplier: float = 2.0,
) -> pd.DataFrame:
    # Create a copy of the DataFrame and sort it by datetime to ensure correct calculations
    df = df.copy().sort_values("datetime", ignore_index=True)

    # Calculate the Exponential Moving Average (EMA) of the 'close' price
    # 'span' is the period for the EMA, 'adjust=False' uses simpler weighting
    df["ema"] = df["close"].ewm(span=ema_window, adjust=False).mean()

    # Calculate True Range (TR) for ATR calculation
    # TR is the greatest of:
    #   1. Current High - Current Low
    #   2. Absolute difference between Current High and Previous Close
    #   3. Absolute difference between Current Low and Previous Close
    tr = pd.concat([
        df["high"] - df["low"],
        (df["high"] - df["close"].shift(1)).abs(),
        (df["low"]  - df["close"].shift(1)).abs(),
    ], axis=1).max(axis=1)
    # Calculate Average True Range (ATR) by taking a rolling mean of the True Range
    df["atr"] = tr.rolling(atr_window).mean()

    # Calculate the Keltner Channel Upper Band
    # EMA + (multiplier * ATR)
    df["kc_upper"] = df["ema"] + multiplier * df["atr"]
    # Calculate the Keltner Channel Lower Band
    # EMA - (multiplier * ATR)
    df["kc_lower"] = df["ema"] - multiplier * df["atr"]

    # Generate trading signals based on Keltner Channels
    # If close price is at or below the lower KC, signal is 1 (Buy)
    # If close price is at or above the upper KC, signal is -1 (Sell)
    # Otherwise (price is within channels), signal is 0 (No signal)
    df["signal"] = np.where(df["close"] <= df["kc_lower"],  1,
                   np.where(df["close"] >= df["kc_upper"], -1, 0))

    return df

df_signals = keltner_channel_reversion(df, ema_window=20, atr_window=10, multiplier=2.0)

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 = go.FigureWidget(data=[go.Candlestick(
    x=df_signals["datetime"],
    open=df_signals["open"], high=df_signals["high"],
    low=df_signals["low"],   close=df_signals["close"],
    name="Price"
)])

fig.add_trace(go.Scatter(x=df_signals["datetime"], y=df_signals["ema"],
    mode="lines", name="EMA", line=dict(color="blue",  width=1.5)))
fig.add_trace(go.Scatter(x=df_signals["datetime"], y=df_signals["kc_upper"],
    mode="lines", name="KC Upper", line=dict(color="orange", width=1, dash="dash")))
fig.add_trace(go.Scatter(x=df_signals["datetime"], y=df_signals["kc_lower"],
    mode="lines", name="KC Lower", line=dict(color="orange", width=1, dash="dash")))

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)"))
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)"))

fig.update_layout(
    title_text="Keltner Channel Reversion Strategy",
    xaxis_rangeslider_visible=False,
    xaxis_title="Datetime", yaxis_title="Price",
    height=600, yaxis=dict(autorange=True),
)
fig.show()