125 lines
4.0 KiB
Python
125 lines
4.0 KiB
Python
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)
|