balik zmen uff

testy/testsuppressmedium.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
+import mplfinance
+
+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_series = np.array(detail.bars["vwap"])
+df = {}
+highs = np.array(detail.bars["high"])
+lows = np.array(detail.bars["low"])
+
+np_high = np.array(detail.bars["high"])
+np_low = np.array(detail.bars["low"])
+price_series = detail.bars["vwap"]
+timestamps = detail.bars["time"]
+
+prices = []
+#TODO pridat k indikatorum convert to numpy, abych mohl pouzivat numpy operace v expressionu
+
+# 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))
+
+class Sup_Res_Finder():
+    def __init__(self, s=None):
+        if s is None:
+            self.s = np.mean(np.diff(np.concatenate([[np.nan], np.highs, [np.nan]], axis=0)))
+        else:
+            self.s = s
+
+    def isSupport(self, lows, i):
+        support = lows[i] < lows[i-1] and lows[i] < lows[i+1] \
+            and lows[i+1] < lows[i+2] and lows[i-1] < lows[i-2]
+
+        return support
+
+    def isResistance(self, highs, i):
+        resistance = highs[i] > highs[i-1] and highs[i] > highs[i+1] \
+            and highs[i+1] > highs[i+2] and highs[i-1] > highs[i-2]
+
+        return resistance
+
+    def find_levels(self, highs, lows):
+        levels = []
+
+        for i in range(2, len(lows) - 2):
+            if self.isSupport(lows, i):
+                l = lows[i]
+
+                if not np.any([abs(l - x) < self.s for x in levels]):
+                    levels.append((i, l))
+
+            elif self.isResistance(highs, i):
+                l = highs[i]
+
+                if not np.any([abs(l - x) < self.s for x in levels]):
+                    levels.append((i, l))
+
+        return levels
+
+def plot_ohlc_with_support_resistance(bars, s=None):
+    highs = np.array(bars['high'])
+    lows = np.array(bars['low'])
+
+    finder = Sup_Res_Finder(s=s)
+    levels = finder.find_levels(highs, lows)
+
+    fig, ax = plt.subplots()
+
+    # Plot the candlesticks
+
+    ax.plot(bars['time'], highs, color='green', linestyle='-', linewidth=0.8)
+    ax.plot(bars['time'], lows, color='red', linestyle='-', linewidth=0.8)
+    ax.fill_between(bars['time'], highs, lows, color='green' if highs[0] > lows[0] else 'red', alpha=0.5)
+
+    # Plot the support and resistance levels
+
+    for level in levels:
+        ax.hlines(level[1], level[0] - 0.5, level[0] + 0.5, color='black', linewidth=1)
+
+    ax.set_xlabel('Time')
+    ax.set_ylabel('Price')
+    ax.set_title('OHLC Chart with Support and Resistance Levels')
+
+    plt.show()
+
+
+plot_ohlc_with_support_resistance(detail.bars, 0.05)
+
+# print(price_series)
+# # Find local maxima and minima using the optimized function.
+# maxima_indices = argrelextrema(price_series, np.greater)[0]
+# minima_indices = argrelextrema(price_series, np.less)[0]
+# print(maxima_indices)
+# print(minima_indices)
+# # # Find local maxima and minima
+# # maxima_indices = argrelextrema(price_series, np.greater)[0]
+# # minima_indices = argrelextrema(price_series, np.less)[0]
+
+# 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()
+
+# # Print the indices of local maxima and minima
+# print("Local Maxima Indices:", maxima_indices)
+# print("Local Minima Indices:", minima_indices)