import pandas as pd
import plotly.graph_objects as go
import csv
from datetime import datetime


# load data file
qqq_data = []
with open('qqq.csv', 'r') as file:
    reader = csv.DictReader(file)
    for row in reader:
        qqq_data.append({
            'date': datetime.strptime(row['date'], '%Y-%m-%d'),
            'close': float(row['close'])
        })

# set tqqq ipo price and last record
tqqq_price = 0.4325520098209381
tqqq_data = [{'date': qqq_data[-1]['date'], 'close': tqqq_price}]

# calculate % change to find previous day closing price
daily_expense_ratio = 0.88 / 100 / 252
for i in range(len(qqq_data) - 1, 0, -1):
    percent_change = (qqq_data[i]['close'] - qqq_data[i-1]['close']) / qqq_data[i-1]['close']
    tqqq_price /= (1 + 3 * percent_change) * (1 + daily_expense_ratio)
    tqqq_data.append({'date': qqq_data[i-1]['date'], 'close': tqqq_price})

tqqq_data.reverse()


# Save to a CSV file
df = pd.DataFrame(tqqq_data)
df.to_csv("tqqq.csv", index=False)

# Find highest and lowest closing prices
min_row = df.loc[df['close'].idxmin()]
min_value = min_row['close']
min_date = min_row['date']

max_row = df.loc[df['close'].idxmax()]
max_value = max_row['close']
max_date = max_row['date']

print(f"Lowest value: {min_value} on {min_date}")
print(f"Highest value: {max_value} on {max_date}")




# Visualize the tqqq closing prices
fig = go.Figure()

fig.add_trace(go.Scatter(x=df['date'], y=df['close'], mode='lines', name='Close'))

fig.add_trace(go.Scatter(
    x=[min_date], y=[min_value],
    mode='markers+text',
    name='Lowest',
    text=['Lowest'],
    textposition='bottom center',
    marker=dict(color='red', size=10)
))

fig.add_trace(go.Scatter(
    x=[max_date], y=[max_value],
    mode='markers+text',
    name='Highest',
    text=['Highest'],
    textposition='top center',
    marker=dict(color='green', size=10)
))

fig.update_layout(
    title='TQQQ Close Prices with Highest and Lowest Points',
    xaxis_title='Date',
    yaxis_title='Close Price',
    showlegend=True
)

fig.show()