Update multi_crypto.py

multi_crypto.py

@@ -1,197 +1,197 @@
-import marimo
-
-__generated_with = "0.9.15"
-app = marimo.App(width="full")
-
-
-@app.cell(hide_code=True)
-def __(mo):
-    mo.md(r"""# Cryptocurrency Minuet Dashboard""")
-    return
-
-
-@app.cell(hide_code=True)
-def __():
-    import marimo as mo
-    import pandas as pd
-    import numpy as np
-    import matplotlib.pyplot as plt
-    import seaborn as sns
-    import yfinance as yf
-    import mplfinance as mpf
-    import altair as alt
-    from datetime import date, timedelta, datetime
-    import pytz
-
-    from sklearn.preprocessing import MinMaxScaler
-    return (
-        MinMaxScaler,
-        alt,
-        date,
-        datetime,
-        mo,
-        mpf,
-        np,
-        pd,
-        plt,
-        pytz,
-        sns,
-        timedelta,
-        yf,
-    )
-
-
-@app.cell(hide_code=True)
-def __(datetime, mo):
-    start_date_input = mo.ui.date(value=datetime.now().date())
-
-    mo.md('## Input')
-    return (start_date_input,)
-
-
-@app.cell(hide_code=True)
-def __(mo, start_date_input):
-
-    mo.md(f"""
-    Please enter yout start date range: {start_date_input}
-    """)
-    return
-
-
-@app.cell(hide_code=True)
-def __(datetime, pd, pytz, start_date_input, yf):
-    tickers = [f'{ticker}-USD' for ticker in [
-        'BTC', 'ETH', 'AR', 'SOL', 'ADA', 'XMR',
-         'DOGE', 'SHIB', 'PEPE24478', 'BNB', 'AVAX',
-    ]]
-
-    # Mendapatkan waktu saat ini (jam, menit, detik)
-    current_time = datetime.now().time()
-
-    print(f'current time: {current_time}')
-
-    # Menggabungkan tanggal dari input dengan waktu sekarang
-    now = datetime.combine(start_date_input.value, current_time)
-
-    # Menambahkan zona waktu UTC
-    now = now.replace(tzinfo=pytz.UTC)
-
-    # Mendapatkan tanggal untuk start_date (menggunakan tanggal input)
-    start_date = start_date_input.value.strftime('%Y-%m-%d')
-
-    # Mendapatkan data dari Yahoo Finance
-    data = yf.download(tickers, start=start_date, interval='5m')
-
-    # Mengonversi index ke datetime dan mengubah zona waktunya ke UTC
-    data.index = pd.to_datetime(data.index).tz_convert('UTC')
-
-    # Memfilter data untuk hanya yang sebelum waktu sekarang
-    data = data[data.index > now]
-
-    # Mengambil harga penutupan
-    closes = data['Close']
-    return closes, current_time, data, now, start_date, tickers
-
-
-@app.cell
-def __(closes):
-    closes
-    return
-
-
-@app.cell(hide_code=True)
-def __(closes):
-    highest_percentage_change = {}
-    for ticker in closes.columns:
-        # Group by day and calculate min and max within the day
-        daily_high = closes[ticker].resample('D').max()
-        daily_low = closes[ticker].resample('D').min()
-        # Calculate percentage change
-        daily_percentage_change = ((daily_high - daily_low) / daily_low) * 100
-        # Get the highest percentage change within the day
-        highest_percentage_change[ticker] = daily_percentage_change.max()
-    return (
-        daily_high,
-        daily_low,
-        daily_percentage_change,
-        highest_percentage_change,
-        ticker,
-    )
-
-
-@app.cell(hide_code=True)
-def __(mo):
-    mo.md("""## Highest Returns in the since Yesterday""")
-    return
-
-
-@app.cell(hide_code=True)
-def __(highest_percentage_change, pd):
-    def show_highest_percentage(ticker):
-        percentage = highest_percentage_change[ticker]
-        return percentage
-
-    def percentage_to_text(percentage):
-        return '%.2f%%' % percentage if percentage is not None else 'N/A'
-
-    # Calculate and sort percentage changes
-    highest_percentage_sorted = sorted(highest_percentage_change.items(), key=lambda x: x[1], reverse=True)
-    highest, second_highest = highest_percentage_sorted[0], highest_percentage_sorted[1]
-    lowest, second_lowest = highest_percentage_sorted[-1], highest_percentage_sorted[-2]
-
-    # Calculate and sort percentage changes
-    # Urutkan berdasarkan persentase, hasilkan tuple (ticker, persentase)
-    highest_percentage_sorted = sorted(highest_percentage_change.items(), key=lambda x: x[1], reverse=True)
-
-    # Pisahkan ticker dan persentase yang sudah diurutkan
-    sorted_tickers = [ticker for ticker, _ in highest_percentage_sorted]
-    sorted_percentages = [percentage_to_text(percentage) for _, percentage in highest_percentage_sorted]
-
-    # Membuat DataFrame dengan kolom yang sudah sesuai
-    highest_percentage = pd.DataFrame(
-        {
-            'Ticker': sorted_tickers,
-            'Percentage': sorted_percentages
-        }
-    )
-    return (
-        highest,
-        highest_percentage,
-        highest_percentage_sorted,
-        lowest,
-        percentage_to_text,
-        second_highest,
-        second_lowest,
-        show_highest_percentage,
-        sorted_percentages,
-        sorted_tickers,
-    )
-
-
-@app.cell(hide_code=True)
-def __(
-    highest,
-    highest_percentage,
-    lowest,
-    mo,
-    second_highest,
-    second_lowest,
-):
-    mo.vstack([
-        mo.md(f"""
-        ### Overview of Highest and Lowest Daily Percentage Changes
-        The **highest daily percentage change** was recorded by `{highest[0]}` at {highest[1]:.2f}%. The **second highest** was `{second_highest[0]}` with a change of {second_highest[1]:.2f}%.
-        The **lowest daily percentage change** occurred for `{lowest[0]}` at {lowest[1]:.2f}%. The **second lowest** was `{second_lowest[0]}` with a change of {second_lowest[1]:.2f}%.
-        """), 
-        highest_percentage
-    ])
-    return
-
-
-@app.cell
-def __():
-    return
-
-
-if __name__ == "__main__":
-    app.run()
+import marimo
+
+__generated_with = "0.9.15"
+app = marimo.App(width="full")
+
+
+@app.cell(hide_code=True)
+def __(mo):
+    mo.md(r"""# Cryptocurrency Minuet Dashboard""")
+    return
+
+
+@app.cell(hide_code=True)
+def __():
+    import marimo as mo
+    import pandas as pd
+    import numpy as np
+    import matplotlib.pyplot as plt
+    import seaborn as sns
+    import yfinance as yf
+    import mplfinance as mpf
+    import altair as alt
+    from datetime import date, timedelta, datetime
+    import pytz
+
+    from sklearn.preprocessing import MinMaxScaler
+    return (
+        MinMaxScaler,
+        alt,
+        date,
+        datetime,
+        mo,
+        mpf,
+        np,
+        pd,
+        plt,
+        pytz,
+        sns,
+        timedelta,
+        yf,
+    )
+
+
+@app.cell(hide_code=True)
+def __(datetime, mo):
+    start_date_input = mo.ui.date(value=(datetime.now() - timedelta(days=1)).date())
+
+    mo.md('## Input')
+    return (start_date_input,)
+
+
+@app.cell(hide_code=True)
+def __(mo, start_date_input):
+
+    mo.md(f"""
+    Please enter yout start date range: {start_date_input}
+    """)
+    return
+
+
+@app.cell(hide_code=True)
+def __(datetime, pd, pytz, start_date_input, yf):
+    tickers = [f'{ticker}-USD' for ticker in [
+        'BTC', 'ETH', 'AR', 'SOL', 'ADA', 'XMR',
+         'DOGE', 'SHIB', 'PEPE24478', 'BNB', 'AVAX',
+    ]]
+
+    # Mendapatkan waktu saat ini (jam, menit, detik)
+    current_time = datetime.now().time()
+
+    print(f'current time: {current_time}')
+
+    # Menggabungkan tanggal dari input dengan waktu sekarang
+    now = datetime.combine(start_date_input.value, current_time)
+
+    # Menambahkan zona waktu UTC
+    now = now.replace(tzinfo=pytz.UTC)
+
+    # Mendapatkan tanggal untuk start_date (menggunakan tanggal input)
+    start_date = start_date_input.value.strftime('%Y-%m-%d')
+
+    # Mendapatkan data dari Yahoo Finance
+    data = yf.download(tickers, start=start_date, interval='5m')
+
+    # Mengonversi index ke datetime dan mengubah zona waktunya ke UTC
+    data.index = pd.to_datetime(data.index).tz_convert('UTC')
+
+    # Memfilter data untuk hanya yang sebelum waktu sekarang
+    data = data[data.index > now]
+
+    # Mengambil harga penutupan
+    closes = data['Close']
+    return closes, current_time, data, now, start_date, tickers
+
+
+@app.cell
+def __(closes):
+    closes
+    return
+
+
+@app.cell(hide_code=True)
+def __(closes):
+    highest_percentage_change = {}
+    for ticker in closes.columns:
+        # Group by day and calculate min and max within the day
+        daily_high = closes[ticker].resample('D').max()
+        daily_low = closes[ticker].resample('D').min()
+        # Calculate percentage change
+        daily_percentage_change = ((daily_high - daily_low) / daily_low) * 100
+        # Get the highest percentage change within the day
+        highest_percentage_change[ticker] = daily_percentage_change.max()
+    return (
+        daily_high,
+        daily_low,
+        daily_percentage_change,
+        highest_percentage_change,
+        ticker,
+    )
+
+
+@app.cell(hide_code=True)
+def __(mo):
+    mo.md("""## Highest Returns in the since Yesterday""")
+    return
+
+
+@app.cell(hide_code=True)
+def __(highest_percentage_change, pd):
+    def show_highest_percentage(ticker):
+        percentage = highest_percentage_change[ticker]
+        return percentage
+
+    def percentage_to_text(percentage):
+        return '%.2f%%' % percentage if percentage is not None else 'N/A'
+
+    # Calculate and sort percentage changes
+    highest_percentage_sorted = sorted(highest_percentage_change.items(), key=lambda x: x[1], reverse=True)
+    highest, second_highest = highest_percentage_sorted[0], highest_percentage_sorted[1]
+    lowest, second_lowest = highest_percentage_sorted[-1], highest_percentage_sorted[-2]
+
+    # Calculate and sort percentage changes
+    # Urutkan berdasarkan persentase, hasilkan tuple (ticker, persentase)
+    highest_percentage_sorted = sorted(highest_percentage_change.items(), key=lambda x: x[1], reverse=True)
+
+    # Pisahkan ticker dan persentase yang sudah diurutkan
+    sorted_tickers = [ticker for ticker, _ in highest_percentage_sorted]
+    sorted_percentages = [percentage_to_text(percentage) for _, percentage in highest_percentage_sorted]
+
+    # Membuat DataFrame dengan kolom yang sudah sesuai
+    highest_percentage = pd.DataFrame(
+        {
+            'Ticker': sorted_tickers,
+            'Percentage': sorted_percentages
+        }
+    )
+    return (
+        highest,
+        highest_percentage,
+        highest_percentage_sorted,
+        lowest,
+        percentage_to_text,
+        second_highest,
+        second_lowest,
+        show_highest_percentage,
+        sorted_percentages,
+        sorted_tickers,
+    )
+
+
+@app.cell(hide_code=True)
+def __(
+    highest,
+    highest_percentage,
+    lowest,
+    mo,
+    second_highest,
+    second_lowest,
+):
+    mo.vstack([
+        mo.md(f"""
+        ### Overview of Highest and Lowest Daily Percentage Changes
+        The **highest daily percentage change** was recorded by `{highest[0]}` at {highest[1]:.2f}%. The **second highest** was `{second_highest[0]}` with a change of {second_highest[1]:.2f}%.
+        The **lowest daily percentage change** occurred for `{lowest[0]}` at {lowest[1]:.2f}%. The **second lowest** was `{second_lowest[0]}` with a change of {second_lowest[1]:.2f}%.
+        """), 
+        highest_percentage
+    ])
+    return
+
+
+@app.cell
+def __():
+    return
+
+
+if __name__ == "__main__":
+    app.run()