import sys
!{sys.executable} -m pip install pandas-ta

import warnings
warnings.filterwarnings("ignore")

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

def generate_larger_data(periods: int) -> pd.DataFrame:
    """Generates a larger synthetic OHLCV dataset with more realistic price fluctuations."""
    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 # Starting price
    volatility_scale = 0.005 # Controls the general magnitude of price changes
    wick_deviation_scale = 0.002 # Controls how much wicks extend beyond body

    for i in range(periods):
        # Open price drifts slightly from the previous close
        open_price = last_close + np.random.normal(0, last_close * volatility_scale * 0.1)

        # Simulate a price change to determine the closing price
        price_change = np.random.normal(0, last_close * volatility_scale)
        close_price = open_price + price_change

        # Determine the high and low of the candle body
        body_high = max(open_price, close_price)
        body_low = min(open_price, close_price)

        # Simulate wicks extending beyond the body
        # High wick should be above the body_high
        high_wick_extension = np.abs(np.random.normal(0, last_close * wick_deviation_scale))
        high_price = body_high + high_wick_extension

        # Low wick should be below the body_low
        low_wick_extension = np.abs(np.random.normal(0, last_close * wick_deviation_scale))
        low_price = body_low - low_wick_extension

        # Ensure OHLC integrity: High must be the absolute highest, Low the absolute lowest
        high_price = max(high_price, open_price, close_price)
        low_price = min(low_price, open_price, close_price)

        # Ensure High is never less than Low
        if high_price < low_price:
            high_price, low_price = low_price, high_price # Swap if somehow invalid

        # Ensure all values are positive integers
        open_price = max(1, int(open_price))
        high_price = max(1, int(high_price))
        low_price = max(1, int(low_price))
        close_price = max(1, int(close_price))

        price_data.append({
            "open": open_price,
            "high": high_price,
            "low": low_price,
            "close": close_price
        })
        last_close = close_price # Update last_close for the next iteration

    df_large = pd.DataFrame(price_data, index=datetime_index)
    df_large.index.name = "datetime"

    # Simulate volume with some fluctuation
    df_large["volume"] = np.random.uniform(100.0, 500.0, periods)

    return df_large

df = generate_larger_data(periods=30)

print("--- Raw OHLCV Data ---")
display(df.head())

def compute_indicators_pandas_ta(df: pd.DataFrame) -> pd.DataFrame:
    df = df.copy()

    df.ta.sma(length=10,  append=True)   # Simple Moving Average
    df.ta.ema(length=10,  append=True)   # Exponential Moving Average
    df.ta.rsi(length=14,  append=True)   # Relative Strength Index
    df.ta.macd(append=True)              # MACD, Signal, Histogram
    df.ta.bbands(length=10, append=True) # Bollinger Bands
    df.ta.atr(length=10,  append=True)   # Average True Range
    df.ta.obv(append=True)               # On-Balance Volume
    df.ta.stoch(append=True)             # Stochastic Oscillator

    return df

df_indicators = compute_indicators_pandas_ta(df)

print("--- Indicators Output ---")
display(df_indicators.tail())
df_indicators.info()

# Generate a larger dataset (e.g., 200 data points)
df_large = generate_larger_data(200)

print("--- Larger Raw OHLCV Data ---")
display(df_large.head())
display(df_large.tail())
df_large.info()

# Recompute indicators on the larger dataset
df_indicators_large = compute_indicators_pandas_ta(df_large)

print("--- Indicators Output for Larger Data ---")
display(df_indicators_large.tail())
df_indicators_large.info()

fig = go.FigureWidget(data=[
    go.Candlestick(
        x=df_indicators_large.index,
        open=df_indicators_large['open'],
        high=df_indicators_large['high'],
        low=df_indicators_large['low'],
        close=df_indicators_large['close'],
        name='Price'
    )
])

# Add SMA_10
fig.add_trace(go.Scatter(
    x=df_indicators_large.index,
    y=df_indicators_large['SMA_10'],
    mode='lines',
    name='SMA 10',
    line=dict(color='blue', width=1)
))

# Add EMA_10
fig.add_trace(go.Scatter(
    x=df_indicators_large.index,
    y=df_indicators_large['EMA_10'],
    mode='lines',
    name='EMA 10',
    line=dict(color='orange', width=1)
))

fig.update_layout(
    title_text='Candlestick Chart with SMA and EMA',
    xaxis_rangeslider_visible=False,
    xaxis_title='Date',
    yaxis_title='Price',
    height=600,
    yaxis=dict(autorange=True) # Ensure y-axis scales to visible candles
)

fig.show()

fig = go.FigureWidget(data=[
    go.Candlestick(
        x=df_indicators_large.index,
        open=df_indicators_large['open'],
        high=df_indicators_large['high'],
        low=df_indicators_large['low'],
        close=df_indicators_large['close'],
        name='Price'
    )
])

# Add Bollinger Bands
fig.add_trace(go.Scatter(
    x=df_indicators_large.index,
    y=df_indicators_large['BBL_10_2.0_2.0'],
    mode='lines',
    name='BB Lower',
    line=dict(color='red', width=1)
))
fig.add_trace(go.Scatter(
    x=df_indicators_large.index,
    y=df_indicators_large['BBM_10_2.0_2.0'],
    mode='lines',
    name='BB Middle',
    line=dict(color='green', width=1, dash='dot')
))
fig.add_trace(go.Scatter(
    x=df_indicators_large.index,
    y=df_indicators_large['BBU_10_2.0_2.0'],
    mode='lines',
    name='BB Upper',
    line=dict(color='red', width=1)
))

fig.update_layout(
    title_text='Candlestick Chart with Bollinger Bands',
    xaxis_rangeslider_visible=False,
    xaxis_title='Date',
    yaxis_title='Price',
    height=600,
    yaxis=dict(autorange=True) # Ensure y-axis scales to visible candles
)

fig.show()

fig = make_subplots(
    rows=2, cols=1,
    shared_xaxes=True,
    vertical_spacing=0.03,
    row_heights=[0.7, 0.3]
)

# Candlestick chart
fig.add_trace(go.Candlestick(
    x=df_indicators_large.index,
    open=df_indicators_large['open'],
    high=df_indicators_large['high'],
    low=df_indicators_large['low'],
    close=df_indicators_large['close'],
    name='Price'
), row=1, col=1)

# RSI chart
fig.add_trace(go.Scatter(
    x=df_indicators_large.index,
    y=df_indicators_large['RSI_14'],
    mode='lines',
    name='RSI 14',
    line=dict(color='purple', width=1)
), row=2, col=1)

# Add RSI overbought/oversold levels
fig.add_hline(y=70, line_dash="dot", line_color="red", row=2, col=1)
fig.add_hline(y=30, line_dash="dot", line_color="green", row=2, col=1)

fig.update_layout(
    title_text='Candlestick Chart with RSI',
    xaxis_rangeslider_visible=False,
    xaxis_title='Date',
    yaxis_title='Price',
    height=800,
    yaxis=dict(autorange=True) # Ensure y-axis scales to visible candles for candlestick
)

fig.update_yaxes(title_text="RSI", row=2, col=1)

fig.show()

fig = make_subplots(
    rows=2, cols=1,
    shared_xaxes=True,
    vertical_spacing=0.03,
    row_heights=[0.7, 0.3]
)

# Candlestick chart
fig.add_trace(go.Candlestick(
    x=df_indicators_large.index,
    open=df_indicators_large['open'],
    high=df_indicators_large['high'],
    low=df_indicators_large['low'],
    close=df_indicators_large['close'],
    name='Price'
), row=1, col=1)

# MACD traces
# Note: pandas-ta names MACD as MACD_12_26_9, Signal as MACDs_12_26_9, Histogram as MACDh_12_26_9
fig.add_trace(go.Scatter(
    x=df_indicators_large.index,
    y=df_indicators_large['MACD_12_26_9'],
    mode='lines',
    name='MACD',
    line=dict(color='blue', width=1)
), row=2, col=1)

fig.add_trace(go.Scatter(
    x=df_indicators_large.index,
    y=df_indicators_large['MACDs_12_26_9'],
    mode='lines',
    name='Signal',
    line=dict(color='red', width=1)
), row=2, col=1)

fig.add_trace(go.Bar(
    x=df_indicators_large.index,
    y=df_indicators_large['MACDh_12_26_9'],
    name='Histogram',
    marker_color='green' # Green for positive, could add logic for red for negative
), row=2, col=1)

fig.update_layout(
    title_text='Candlestick Chart with MACD',
    xaxis_rangeslider_visible=False,
    xaxis_title='Date',
    yaxis_title='Price',
    height=800,
    yaxis=dict(autorange=True) # Ensure y-axis scales to visible candles for candlestick
)

fig.update_yaxes(title_text="MACD", row=2, col=1)

fig.show()

fig = make_subplots(
    rows=2, cols=1,
    shared_xaxes=True,
    vertical_spacing=0.03,
    row_heights=[0.7, 0.3]
)

# Candlestick chart
fig.add_trace(go.Candlestick(
    x=df_indicators_large.index,
    open=df_indicators_large['open'],
    high=df_indicators_large['high'],
    low=df_indicators_large['low'],
    close=df_indicators_large['close'],
    name='Price'
), row=1, col=1)

# ATR chart
fig.add_trace(go.Scatter(
    x=df_indicators_large.index,
    y=df_indicators_large['ATRr_10'],
    mode='lines',
    name='ATR 10',
    line=dict(color='darkorange', width=1)
), row=2, col=1)

fig.update_layout(
    title_text='Candlestick Chart with ATR',
    xaxis_rangeslider_visible=False,
    xaxis_title='Date',
    yaxis_title='Price',
    height=800,
    yaxis=dict(autorange=True) # Ensure y-axis scales to visible candles for candlestick
)

fig.update_yaxes(title_text="ATR", row=2, col=1)

fig.show()

fig = make_subplots(
    rows=2, cols=1,
    shared_xaxes=True,
    vertical_spacing=0.03,
    row_heights=[0.7, 0.3]
)

# Candlestick chart
fig.add_trace(go.Candlestick(
    x=df_indicators_large.index,
    open=df_indicators_large['open'],
    high=df_indicators_large['high'],
    low=df_indicators_large['low'],
    close=df_indicators_large['close'],
    name='Price'
), row=1, col=1)

# OBV chart
fig.add_trace(go.Scatter(
    x=df_indicators_large.index,
    y=df_indicators_large['OBV'],
    mode='lines',
    name='OBV',
    line=dict(color='brown', width=1)
), row=2, col=1)

fig.update_layout(
    title_text='Candlestick Chart with OBV',
    xaxis_rangeslider_visible=False,
    xaxis_title='Date',
    yaxis_title='Price',
    height=800,
    yaxis=dict(autorange=True) # Ensure y-axis scales to visible candles for candlestick
)

fig.update_yaxes(title_text="OBV", row=2, col=1)

fig.show()

fig = make_subplots(
    rows=2, cols=1,
    shared_xaxes=True,
    vertical_spacing=0.03,
    row_heights=[0.7, 0.3]
)

# Candlestick chart
fig.add_trace(go.Candlestick(
    x=df_indicators_large.index,
    open=df_indicators_large['open'],
    high=df_indicators_large['high'],
    low=df_indicators_large['low'],
    close=df_indicators_large['close'],
    name='Price'
), row=1, col=1)

# Stochastic Oscillator chart
# Note: pandas-ta names Stochastic %K as STOCHk_14_3_3 and %D as STOCHd_14_3_3
fig.add_trace(go.Scatter(
    x=df_indicators_large.index,
    y=df_indicators_large['STOCHk_14_3_3'],
    mode='lines',
    name='%K',
    line=dict(color='blue', width=1)
), row=2, col=1)

fig.add_trace(go.Scatter(
    x=df_indicators_large.index,
    y=df_indicators_large['STOCHd_14_3_3'],
    mode='lines',
    name='%D',
    line=dict(color='red', width=1)
), row=2, col=1)

# Add overbought/oversold levels for Stochastic
fig.add_hline(y=80, line_dash="dot", line_color="red", row=2, col=1)
fig.add_hline(y=20, line_dash="dot", line_color="green", row=2, col=1)

fig.update_layout(
    title_text='Candlestick Chart with Stochastic Oscillator',
    xaxis_rangeslider_visible=False,
    xaxis_title='Date',
    yaxis_title='Price',
    height=800,
    yaxis=dict(autorange=True) # Ensure y-axis scales to visible candles for candlestick
)

fig.update_yaxes(title_text="Stochastic", row=2, col=1)

fig.show()