sharavsambuu

#https://pastebin.com/jgr4ZBVK
#https://www.reddit.com/r/algotrading/comments/p4flbp/can_i_make_tick_volume_and_dollar_bars_from_ohlc/

class VolumeBarAggregator():

    class NumpyWrapper():
        def __init__(self, length):
            self.arr = np.array(dtype=float, )

    def __init__(self):

sharavsambuu

def cpcv_generator(t_span, n, k, verbose=True):
    # split data into N groups, with N << T
    # this will assign each index position to a group position
    group_num = np.arange(t_span) // (t_span // n)
    group_num[group_num == n] = n-1
    
    # generate the combinations 
    test_groups = np.array(list(itt.combinations(np.arange(n), k))).reshape(-1, k)
    C_nk = len(test_groups)
    n_paths = C_nk * k // n 

sharavsambuu

Visit the following links first, u'll get some ideas

• https://stackoverflow.com/questions/66081709/whats-the-proposed-way-to-switch-to-native-homebrew-on-m1-macbooks
• https://stackoverflow.com/questions/65901162/how-can-i-run-pyqt5-on-my-mac-with-m1chip

.zprofile

sharavsambuu

#
# Binance-аас 1 минутын чартыг бодит хугацаанд татаж харуулах
#
# sudo apt-get install python3-pyqt5  
# virtualenv -p python3 env && source env/bin/activate
# pip install finplot requests websocket scipy

import json
import requests
import websocket

sharavsambuu

brew install hdf5
export HDF5_DIR=/opt/homebrew/Cellar/hdf5/1.12.0_4
pip3 install --no-binary=h5py h5py

virtualenv -p python env && source env/bin/activate

pip install h5py
pip install tensorflow-macos
pip install tensorflow-metal

sharavsambuu

#!/usr/bin/env python3
'''2021-03-26: Reverse-engineer by searching for the following terms in features*.js:
    - bracketMaintenanceMarginRate
    - cumFastMaintenanceAmount
    - bracketNotionalFloor
    - bracketNotionalCap'''


# (max) position, maintenance margin, maintenance amount
maint_lookup_table = [

sharavsambuu

    # Store the short term moving average in a new column 'window_ST'
    prices.loc[:, 'window_ST'] = prices['close'].rolling(
                                            context.short_term_window).mean()

    # Store the long term moving average in a new column 'window_LT'
    prices.loc[:, 'window_LT'] = prices['close'].rolling(
                                            context.long_term_window).mean()

    # Get the latest signal, 1 for golden cross, -1 for death cross
    prices.loc[:, 'signal'] = np.where(

sharavsambuu

Date	Open	High	Low	Close	Volume
2020-05-06 18:15:00-04:00	2832	2838.5	2832	2838	3750
2020-05-06 18:30:00-04:00	2838	2838.5	2826	2827.75	6864
2020-05-06 18:45:00-04:00	2827.75	2830.5	2827	2829.5	2937
2020-05-06 19:00:00-04:00	2829.5	2830.5	2823	2826.5	4619
2020-05-06 19:15:00-04:00	2826.75	2829.5	2825.25	2827.75	3610
2020-05-06 19:30:00-04:00	2827.75	2829.5	2825.5	2826.75	2460
2020-05-06 19:45:00-04:00	2826.75	2830.75	2826.25	2830.5	2531
2020-05-06 20:00:00-04:00	2830.5	2833.5	2830.25	2833.5	2361

sharavsambuu

# References :
# - https://stackoverflow.com/questions/61776425/logic-for-real-time-algo-trading-expert

import pytz
import pandas as pd
import MetaTrader5 as mt5
import time
from datetime import datetime
from threading import Timer

sharavsambuu

import select
import datetime

import psycopg2
import psycopg2.extensions

conn = psycopg2.connect(database="postgres", user="vagrant")
#conn.set_isolation_level(psycopg2.extensions.ISOLATION_LEVEL_AUTOCOMMIT)

curs = conn.cursor()