import pandas as pd
import numpy as np
import openpyxl
from openpyxl.utils.dataframe import dataframe_to_rows
from openpyxl.styles import Font

# Define the sample data for the updated dynamic Excel structure with adjustable SMA and EMA periods.
sample_data = {
    "Timestamp": pd.date_range(start="2024-01-01", periods=10, freq="T"),
    "Open": np.random.uniform(30000, 40000, 10),
    "High": np.random.uniform(30000, 40000, 10),
    "Low": np.random.uniform(30000, 40000, 10),
    "Close": np.random.uniform(30000, 40000, 10),
    "Volume": np.random.uniform(100, 500, 10)
}

# Convert to DataFrame
df_raw_data = pd.DataFrame(sample_data)

# Calculate SMA and EMA with adjustable initial periods
sma_period = 5
ema_period = 5
df_raw_data['SMA'] = df_raw_data['Close'].rolling(window=sma_period).mean()
df_raw_data['EMA'] = df_raw_data['Close'].ewm(span=ema_period, adjust=False).mean()

# Initialize workbook
wb = openpyxl.Workbook()
wb.remove(wb.active)

# Add Raw Data sheet
ws_raw = wb.create_sheet("Raw Data")
for r in dataframe_to_rows(df_raw_data, index=False, header=True):
    ws_raw.append(r)

# Create Feature Engineering sheet with formula placeholders
ws_feature = wb.create_sheet("Feature Engineering")
ws_feature["A1"].value = "Adjustable SMA and EMA"
ws_feature["A1"].font = Font(bold=True)
ws_feature["A2"].value = "SMA Period:"
ws_feature["B2"].value = sma_period
ws_feature["A3"].value = "EMA Period:"
ws_feature["B3"].value = ema_period

# Formula cells for recalculated SMA and EMA
ws_feature["D2"].value = "Close Price"
ws_feature["E2"].value = "SMA (Dynamic)"
ws_feature["F2"].value = "EMA (Dynamic)"

# Insert formulas in the feature sheet (assuming the raw data is in 'Raw Data' sheet starting from A2)
for i in range(3, len(df_raw_data) + 3):
    ws_feature[f"D{i}"] = f"=Raw Data!E{i}"  # Close price
    ws_feature[f"E{i}"] = f"=AVERAGE(OFFSET(D{i}-$B$2+1,0,0,$B$2,1))"  # SMA formula using offset
    ws_feature[f"F{i}"] = f"=EXPONENTIALMOVINGAVERAGE(D{i}, $B$3)"  # Placeholder for EMA dynamic

# Add Synthetic Relationships sheet with parameters section
ws_synthetic = wb.create_sheet("Synthetic Relationships")
ws_synthetic["A1"].value = "Synthetic Relationships - Parameter Adjustments"
ws_synthetic["A1"].font = Font(bold=True)

# Populate example query columns for visualizing adjusted parameters
ws_synthetic.append(["Query Date", "Found Close Price", "Adjusted SMA", "Adjusted EMA"])
for i in range(3, len(df_raw_data) + 3):
    ws_synthetic[f"A{i}"] = ws_raw[f"A{i}"].value  # Query Date
    ws_synthetic[f"B{i}"] = f"=Raw Data!E{i}"  # Found Close Price
    ws_synthetic[f"C{i}"] = f"=Feature Engineering!E{i}"  # Adjusted SMA
    ws_synthetic[f"D{i}"] = f"=Feature Engineering!F{i}"  # Adjusted EMA

# Save the file
file_path = "ActiveGraphTheory.xlsx"
wb.save(file_path)

file_path