Upload multi_crypto.py

multi_crypto.py

@@ -1,7 +1,7 @@
 import marimo
 
 __generated_with = "0.9.15"
-app = marimo.App(width="medium")
+app = marimo.App(width="full")
 
 
 @app.cell(hide_code=True)
@@ -21,6 +21,7 @@ def __():
     import mplfinance as mpf
     import altair as alt
     from datetime import date, timedelta, datetime
+    import pytz
 
     from sklearn.preprocessing import MinMaxScaler
     return (
@@ -33,6 +34,7 @@ def __():
         np,
         pd,
         plt,
+        pytz,
         sns,
         timedelta,
         yf,
@@ -40,15 +42,61 @@ def __():
 
 
 @app.cell(hide_code=True)
-def __(datetime, timedelta, yf):
+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',
     ]]
-    yesterday = (datetime.now() - timedelta(days=1)).strftime('%Y-%m-%d')
-    data = yf.download(tickers, start=yesterday, interval='5m')
+
+    # 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, data, tickers, yesterday
+    return closes, current_time, data, now, start_date, tickers
+
+
+@app.cell
+def __(closes):
+    closes
+    return
 
 
 @app.cell(hide_code=True)
@@ -78,102 +126,65 @@ def __(mo):
 
 
 @app.cell(hide_code=True)
-def __(highest_percentage_change, mo, tickers):
+def __(highest_percentage_change, pd):
     def show_highest_percentage(ticker):
         percentage = highest_percentage_change[ticker]
-        up = 'success' if percentage > 0 else 'danger'
-        percentage_text = '%.2f%%' % percentage if percentage is not None else 'N/A'
-        return mo.callout(value=f'{ticker} {percentage_text}', kind=up)
+        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]
 
-    # Generate markdown explanations
-    callouts_length = int(len(tickers) / 2)
-    top_callout = 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}%.
-    """)
-
-    callouts_top = mo.hstack([show_highest_percentage(ticker) for ticker in tickers[:callouts_length]])
+    # 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)
 
-    callouts_bottom = mo.hstack([show_highest_percentage(ticker) for ticker in tickers[callouts_length:]])
+    # 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]
 
-    # mo.vstack([top_callout, callouts_top, callouts_bottom], align='start', heights='equal')
+    # Membuat DataFrame dengan kolom yang sudah sesuai
+    highest_percentage = pd.DataFrame(
+        {
+            'Ticker': sorted_tickers,
+            'Percentage': sorted_percentages
+        }
+    )
     return (
-        callouts_bottom,
-        callouts_length,
-        callouts_top,
         highest,
+        highest_percentage,
         highest_percentage_sorted,
         lowest,
+        percentage_to_text,
         second_highest,
         second_lowest,
         show_highest_percentage,
-        top_callout,
+        sorted_percentages,
+        sorted_tickers,
     )
 
 
 @app.cell(hide_code=True)
-def __(top_callout):
-    top_callout
-    return
-
-
-@app.cell(hide_code=True)
-def __(callouts_top):
-    callouts_top
-    return
-
-
-@app.cell(hide_code=True)
-def __(callouts_bottom):
-    callouts_bottom
-    return
-
-
-@app.cell(hide_code=True)
-def __(mo):
-    mo.md(r"""## Log Returns Correlation""")
-    return
-
-
-@app.cell(hide_code=True)
-def __(closes, mo, np):
-    # Calculate log returns and correlation matrix
-    log_returns = np.log(closes / closes.shift(1))
-    correlation_matrix = log_returns.corr()
-
-    # Identify the highest and lowest correlations
-    highest_corr = correlation_matrix.unstack().sort_values(ascending=False).drop_duplicates().iloc[1]
-    lowest_corr = correlation_matrix.unstack().sort_values().drop_duplicates().iloc[0]
-
-    mo.md(f"""
-    ### Highest Correlation\n\nThe highest correlation is between `{highest_corr}` and `{highest_corr}` with a value of **{highest_corr:.2f}**, indicating a strong positive relationship. This suggests that these two assets move similarly over the selected period.\n
-    ### Lowest Correlation\n\nThe lowest correlation is between `{lowest_corr}` and `{lowest_corr}` with a value of **{lowest_corr:.2f}**, indicating a weak or almost no relationship. These assets may provide diversification benefits in a portfolio.
-
-    ### Additional Insights
-
-    - **High Positive Correlations**: Assets with high correlations tend to respond similarly to market events, which can be useful for **trend-following strategies**.
-    - **Low or Negative Correlations**: Assets with low or negative correlations might help reduce overall portfolio risk, making them useful for **diversification**. 
-    - **Portfolio Strategy**: If you're seeking to **balance risk**, consider assets with lower correlations, while highly correlated assets can be beneficial for **momentum-based** portfolios.
-    """)
-    return correlation_matrix, highest_corr, log_returns, lowest_corr
-
-
-@app.cell
-def __(log_returns, plt, sns):
-    plt.figure(figsize=(6, 5))
-    sns.heatmap(log_returns.corr(), cmap="YlGnBu", annot=True)
-    plt.gca()
-    return
-
-
-@app.cell
-def __():
+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