107 lines
3.5 KiB
Python
107 lines
3.5 KiB
Python
import numpy as np
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import matplotlib.pyplot as plt
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from v2realbot.controller.services import get_archived_runner_details_byID
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from v2realbot.common.model import RunArchiveDetail
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from scipy.signal import argrelextrema
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id = "c5ae757f-6bdd-4d1f-84a8-98bdaad65a28"
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res, val = get_archived_runner_details_byID(id)
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if res < 0:
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print(res)
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detail = RunArchiveDetail(**val)
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# detail.indicators[0]
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price_series = np.array(detail.bars["vwap"])
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#price_series = detail.bars["vwap"]
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timestamps = detail.bars["time"]
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prices = []
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#TODO pridat k indikatorum convert to numpy, abych mohl pouzivat numpy operace v expressionu
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def get_local_maxima_numpy(
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series: np.ndarray,
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debug=False,
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) -> np.ndarray:
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"""calculate local maximal point"""
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if series.size == 0:
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return np.array([])
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# Calculate the difference between adjacent elements.
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diff = np.diff(series)
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# Find the indices of the elements where the difference changes sign from positive to negative.
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high_index = np.where((diff[:-1] >= 0) & (diff[1:] < 0))[0] + 1
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# Return a NumPy array containing the local maxima.
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return high_index#series[high_index]
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def get_local_minima_numpy(
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series: np.ndarray,
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debug=False,
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) -> np.ndarray:
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"""calculate local maximal point"""
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if series.size == 0:
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return np.array([])
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# Calculate the difference between adjacent elements.
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diff = np.diff(series)
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# Find the indices of the elements where the difference changes sign from positive to negative.
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low_index = np.where((diff[:-1] <= 0) & (diff[1:] > 0))[0] + 1
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# Return a NumPy array containing the local maxima.
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return low_index#series[high_index]
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def get_local_minima(prices):
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return prices[-2] if len(prices) >= 3 and prices[-2] > prices[-3] and prices[-2] > prices[-1] else None
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# iter_prices = []
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# for price in detail.bars["vwap"]:
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# iter_prices.append(price)
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# get_local_minima(iter_prices)
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def calculate_support_resistance(bars, window=5):
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lows = np.array(bars['low'])
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highs = np.array(bars['high'])
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rolling_support = np.minimum.accumulate(lows)[::-1][:window][::-1]
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rolling_resistance = np.maximum.accumulate(highs)[::-1][:window][::-1]
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return {'rolling_support': rolling_support.tolist(), 'rolling_resistance': rolling_resistance.tolist()}
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rolling = calculate_support_resistance(detail.bars, 5)
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print(rolling)
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# func = "prices[-1] if np.all(prices[-1] > prices[-2:]) else 0"
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# #func = "prices[-2] if len(prices) >= 3 and prices[-2] > prices[-3] and prices[-2] > prices[-1] else None"
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# for price in price_series:
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# prices.append(price)
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# print(eval(func))
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# maxima_indices = argrelextrema(price_series, np.greater)[0]
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# minima_indices = argrelextrema(price_series, np.less)[0]
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# # Print the indices of local maxima and minima
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# print("Local Maxima Indices:", maxima_indices)
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# print("Local Minima Indices:", minima_indices)
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print("from new function")
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maxima_indices = get_local_maxima_numpy(price_series)
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minima_indices = get_local_minima_numpy(price_series)
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print("Local Maxima Indices:", maxima_indices)
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print("Local Minima Indices:", minima_indices)
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# Plot the price series with local maxima and minima
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plt.figure(figsize=(10, 6))
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plt.plot(range(len(price_series)), price_series, label='Price Series')
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plt.scatter(maxima_indices, price_series[maxima_indices], color='r', label='Local Maxima', zorder=5)
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plt.scatter(minima_indices, price_series[minima_indices], color='g', label='Local Minima', zorder=5)
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plt.xlabel('Time')
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plt.ylabel('Price')
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plt.title('Price Series with Local Maxima and Minima')
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plt.legend()
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plt.show()
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