test2.py

import airbyte as ab
import pandas as pd
import matplotlib.pyplot as plt
# Create and configure the source connector:
source = ab.get_source(
    "source-coin-api",
    config={
        "api_key": ab.get_secret("COIN_API_KEY"),
        "environment": "production",
        "symbol_id": "COINBASE_SPOT_INDEX_USD",
        "period": "1DAY",
        "start_date": "2023-01-01T00:00:00",
    },
    streams="*",
)

# Verify the config and creds by running `check`:
source.check()

# Read data from the source into the default cache:
cache = ab.get_default_cache()
result = source.read(cache=cache)

# Read from the cache into a pandas Dataframe:
ohlcv_df = cache["ohlcv_historical_data"].to_pandas()

# Convert 'time_period_start' to datetime format and necessary columns to numeric
ohlcv_df["time_period_start"] = pd.to_datetime(ohlcv_df["time_period_start"])
numeric_columns = [
    "price_open",
    "price_high",
    "price_low",
    "price_close",
    "volume_traded",
    "trades_count",
]
ohlcv_df[numeric_columns] = ohlcv_df[numeric_columns].apply(
    pd.to_numeric, errors="coerce"
)

# Calculate daily price movement
ohlcv_df["daily_movement"] = ohlcv_df["price_close"] - ohlcv_df["price_open"]

# Set the 'time_period_start' column as the index for plotting
ohlcv_df.set_index("time_period_start", inplace=True)

# Plotting the daily movement
plt.figure(figsize=(12, 6))  # Set the figure size
plt.plot(ohlcv_df["daily_movement"], marker="o", linestyle="-")
plt.title("Daily Price Movement")
plt.xlabel("Date")
plt.ylabel("Price Movement")
plt.grid(True)
plt.xticks(rotation=45)  # Rotates the date labels for better readability
plt.tight_layout()  # Adjusts the plot to ensure everything fits without overlapping

# Save the plot as an image file
plt.savefig("daily_price_movement.png")
print("Plot saved as 'daily_price_movement.png'")