other changes

testy/domfreq.py

@@ -0,0 +1,62 @@
+import numpy as np
+import matplotlib.pyplot as plt
+from scipy.fft import fft
+
+# Define the sampling frequency and time vector
+fs = 500  # Sampling frequency
+t = np.arange(0, 1, 1/fs)  # Time vector
+
+# Define the frequencies
+f1 = 5    # Frequency that occurs most often but with lower amplitude
+f2 = 20   # Frequency with the highest amplitude
+
+# Creating the individual signals
+signal_f1 = 0.5 * np.sin(2 * np.pi * f1 * t)  # Signal with frequency f1
+signal_f2 = 2 * np.sin(2 * np.pi * f2 * t)    # Signal with frequency f2
+
+# Composite signal
+signal = signal_f1 + signal_f2
+
+# Performing a Fourier Transform
+freq = np.fft.fftfreq(len(t), 1/fs)
+fft_values = fft(signal)
+
+# Plotting all the signals and the frequency spectrum
+plt.figure(figsize=(14, 10))
+
+# Plot 1: Composite Signal
+plt.subplot(4, 1, 1)
+plt.plot(t, signal)
+plt.title('Composite Signal (f1 + f2)')
+plt.xlabel('Time [s]')
+plt.ylabel('Amplitude')
+
+# Plot 2: Frequency f1 Signal
+plt.subplot(4, 1, 2)
+plt.plot(t, signal_f1)
+plt.title('Individual Frequency f1 Signal')
+plt.xlabel('Time [s]')
+plt.ylabel('Amplitude')
+
+# Plot 3: Frequency f2 Signal
+plt.subplot(4, 1, 3)
+plt.plot(t, signal_f2)
+plt.title('Individual Frequency f2 Signal')
+plt.xlabel('Time [s]')
+plt.ylabel('Amplitude')
+
+# Plot 4: Frequency Spectrum
+plt.subplot(4, 1, 4)
+plt.plot(freq, np.abs(fft_values))
+plt.title('Frequency Spectrum of Composite Signal')
+plt.xlabel('Frequency [Hz]')
+plt.ylabel('Amplitude')
+plt.xlim([0, 30])
+
+# Highlighting the dominant frequencies in the spectrum
+plt.axvline(x=f1, color='green', linestyle='--', label='Frequency f1')
+plt.axvline(x=f2, color='red', linestyle='--', label='Frequency f2')
+
+plt.legend()
+plt.tight_layout()
+plt.show()

testy/getlastcalendardate.py

@@ -0,0 +1,28 @@
+from alpaca.data.historical import CryptoHistoricalDataClient, StockHistoricalDataClient
+from alpaca.data.requests import CryptoLatestTradeRequest, StockLatestTradeRequest, StockLatestBarRequest, StockTradesRequest, StockBarsRequest
+from alpaca.data.enums import DataFeed
+from config import API_KEY, SECRET_KEY, MAX_BATCH_SIZE
+import datetime
+import time
+from alpaca.data import Quote, Trade, Snapshot, Bar
+from alpaca.data.models import BarSet, QuoteSet, TradeSet
+from alpaca.data.timeframe import TimeFrame
+# import mplfinance as mpf
+import pandas as pd
+from rich import print
+from v2realbot.utils.utils import zoneNY
+from v2realbot.config import ACCOUNT1_PAPER_API_KEY, ACCOUNT1_PAPER_SECRET_KEY
+from alpaca.trading.requests import GetCalendarRequest
+from alpaca.trading.client import TradingClient
+
+parametry = {}
+
+clientTrading = TradingClient(ACCOUNT1_PAPER_API_KEY, ACCOUNT1_PAPER_SECRET_KEY, raw_data=False)
+
+#get previous days bar
+
+datetime_object_from = datetime.datetime(2023, 10, 11, 4, 0, 00, tzinfo=datetime.timezone.utc)
+datetime_object_to = datetime.datetime(2023, 10, 16, 16, 1, 00, tzinfo=datetime.timezone.utc)
+calendar_request = GetCalendarRequest(start=datetime_object_from,end=today)
+cal_dates = clientTrading.get_calendar(calendar_request)
+print(cal_dates)

testy/histogramnumpy.py

@@ -0,0 +1,34 @@
+import numpy as np
+
+data = np.array([1,2,3,4,3,2,4,7,8,4,3,0,0,0,0,9,9,9,11,23,2,3,4,29,23])
+
+counts, bin_edges = np.histogram(data, bins=4)
+# returns a tuple containing two arrays:
+# counts: An array containing the number of data points in each bin.
+# bin_edges: An array containing the edges of each bin.
+#(array([10,  6,  0,  1]), array([ 1. ,  6.5, 12. , 17.5, 23. ]))
+print(counts, bin_edges)
+
+edge_from = bin_edges[3]
+edge_to = bin_edges[4]
+print(edge_from)
+print(edge_to)
+print("test where", data[np.where((edge_from<data) & (data<edge_to))])
+
+ctvrty_bin = [datum for datum in data if edge_from <= datum <= edge_to]
+
+print(np.mean(ctvrty_bin))
+
+#print(histo[0][-2])
+
+bins = 4
+mean_of_4th_bin = np.mean(data[np.where(np.histogram(data, bins)[1][3] <= data)[0]])
+# print(mean_of_4th_bin)
+# print(mean_of_fourth_bucket)
+
+
+
+
+# Print the data from the 3rd bin using a list comprehension
+
+#print([datum for datum in data if bin_edges[2] <= datum < bin_edges[3]])

testy/numpylistmutability.py

@@ -0,0 +1,14 @@
+import numpy as np
+from array import array
+
+# Original list
+puvodni = array('i', [1, 2, 3, 4])
+
+# Create a NumPy array using the original list
+numpied = np.array(puvodni)
+
+# Now, if puvodni changes, numpied will be updated as well
+puvodni.append(5)
+
+# Check the updated numpied array
+print(numpied)

testy/picklequeue.py

@@ -0,0 +1,25 @@
+import queue
+import msgpack
+# Creating the original queue
+original_queue = queue.Queue()
+new_queue = queue.Queue()
+
+# Adding elements to the original queue
+original_queue.put(5)
+original_queue.put(10)
+original_queue.put(15)
+
+# Pickling the queue
+pickled_queue = msgpack.packb(original_queue)
+
+# Unpickling the queue
+unpickled_queue = msgpack.unpackb(pickled_queue)
+# Pickling the queue
+new_queue.queue = unpickled_queue.queue
+
+
+print(new_queue)
+
+# Checking the contents of the new queue
+while not new_queue.empty():
+    print(new_queue.get())