balik zmen uff

testy/volatilitytest.py

@@ -0,0 +1,51 @@
+import numpy as np
+import matplotlib.pyplot as plt
+from v2realbot.controller.services import get_archived_runner_details_byID
+from v2realbot.common.model import RunArchiveDetail
+# Generate sample price data
+timestamps = np.arange('2023-10-27', '2023-10-28', dtype='datetime64[s]')
+price = 100 + np.arange(100) * 0.5
+
+id = "e74b5d35-6552-4dfc-ba59-2eda215af292"
+
+res, val = get_archived_runner_details_byID(id)
+if res < 0:
+    print(res)
+
+detail = RunArchiveDetail(**val)
+# detail.indicators[0]
+price = detail.bars["vwap"]
+timestamps = detail.bars["time"]
+
+# Calculate the standard deviation of price changes over a specified time interval
+def calculate_volatility(price, window):
+    volatility = np.zeros_like(price)
+    for i in range(window, len(price)):
+        volatility[i] = np.std(price[i - window: i])
+    return volatility
+
+# Set a threshold for the indicator
+threshold = 0.4
+
+# Identify breakout points based on the threshold
+def identify_breakouts(volatility, threshold):
+    return volatility > threshold
+
+# Plot the price data and the volatility breakout points
+def plot_data(timestamps, price, breakout_points):
+    plt.figure(figsize=(12, 6))
+    plt.plot(timestamps, price, label='Price')
+    breakout_timestamps = timestamps[np.where(breakout_points)[0]]
+    breakout_prices = price[np.where(breakout_points)[0]]
+    plt.scatter(breakout_timestamps, breakout_prices, color='r', label='Volatility Breakout')
+    plt.xlabel('Time')
+    plt.ylabel('Price')
+    plt.title('Intraday Volatility Breakout Indicator')
+    plt.legend()
+    plt.show()
+
+# Applying the functions
+window = 30
+volatility = calculate_volatility(price, window)
+breakout_points = identify_breakouts(volatility, threshold)
+plot_data(timestamps, price, breakout_points)