balik zmen uff

testy/localmaximatest.py

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+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
+from scipy.signal import argrelextrema
+
+id = "c5ae757f-6bdd-4d1f-84a8-98bdaad65a28"
+
+res, val = get_archived_runner_details_byID(id)
+if res < 0:
+    print(res)
+
+detail = RunArchiveDetail(**val)
+# detail.indicators[0]
+price_series = np.array(detail.bars["vwap"])
+#price_series = detail.bars["vwap"]
+timestamps = detail.bars["time"]
+
+prices = []
+#TODO pridat k indikatorum convert to numpy, abych mohl pouzivat numpy operace v expressionu
+
+
+def get_local_maxima_numpy(
+    series: np.ndarray,
+    debug=False,
+) -> np.ndarray:
+    """calculate local maximal point"""
+    if series.size == 0:
+        return np.array([])
+
+    # Calculate the difference between adjacent elements.
+    diff = np.diff(series)
+
+    # Find the indices of the elements where the difference changes sign from positive to negative.
+    high_index = np.where((diff[:-1] >= 0) & (diff[1:] < 0))[0] + 1
+
+    # Return a NumPy array containing the local maxima.
+    return high_index#series[high_index]
+
+def get_local_minima_numpy(
+    series: np.ndarray,
+    debug=False,
+) -> np.ndarray:
+    """calculate local maximal point"""
+    if series.size == 0:
+        return np.array([])
+
+    # Calculate the difference between adjacent elements.
+    diff = np.diff(series)
+
+    # Find the indices of the elements where the difference changes sign from positive to negative.
+    low_index = np.where((diff[:-1] <= 0) & (diff[1:] > 0))[0] + 1
+
+    # Return a NumPy array containing the local maxima.
+    return low_index#series[high_index]
+
+def get_local_minima(prices):
+    return prices[-2] if len(prices) >= 3 and prices[-2] > prices[-3] and prices[-2] > prices[-1] else None
+
+# iter_prices = []
+# for price in detail.bars["vwap"]:
+#     iter_prices.append(price)
+#     get_local_minima(iter_prices)
+    
+def calculate_support_resistance(bars, window=5):
+    lows = np.array(bars['low'])
+    highs = np.array(bars['high'])
+
+    rolling_support = np.minimum.accumulate(lows)[::-1][:window][::-1]
+    rolling_resistance = np.maximum.accumulate(highs)[::-1][:window][::-1]
+
+    return {'rolling_support': rolling_support.tolist(), 'rolling_resistance': rolling_resistance.tolist()}
+
+rolling = calculate_support_resistance(detail.bars, 5)
+print(rolling)
+
+
+# func = "prices[-1] if np.all(prices[-1] > prices[-2:]) else 0"
+# #func = "prices[-2] if len(prices) >= 3 and prices[-2] > prices[-3] and prices[-2] > prices[-1] else None"
+# for price in price_series:
+#     prices.append(price)
+#     print(eval(func))
+# maxima_indices = argrelextrema(price_series, np.greater)[0]
+# minima_indices = argrelextrema(price_series, np.less)[0]
+# # Print the indices of local maxima and minima
+# print("Local Maxima Indices:", maxima_indices)
+# print("Local Minima Indices:", minima_indices)
+
+print("from new function")
+maxima_indices = get_local_maxima_numpy(price_series)
+minima_indices = get_local_minima_numpy(price_series)
+print("Local Maxima Indices:", maxima_indices)
+print("Local Minima Indices:", minima_indices)
+
+# Plot the price series with local maxima and minima
+plt.figure(figsize=(10, 6))
+plt.plot(range(len(price_series)), price_series, label='Price Series')
+plt.scatter(maxima_indices, price_series[maxima_indices], color='r', label='Local Maxima', zorder=5)
+plt.scatter(minima_indices, price_series[minima_indices], color='g', label='Local Minima', zorder=5)
+plt.xlabel('Time')
+plt.ylabel('Price')
+plt.title('Price Series with Local Maxima and Minima')
+plt.legend()
+plt.show()
+
+