normanlmfung

from enum import Enum
import argparse
import time
from datetime import datetime
from typing import Any, Dict, Union
import logging
import json
from tabulate import tabulate
import asyncio
import pandas as pd

normanlmfung

import os
import sys
import traceback
import re
import json
import uuid
from typing import List, Dict
import time
import arrow
from datetime import datetime, timedelta, timezone

normanlmfung

import time
import datetime
from datetime import datetime, timedelta
import enum
from typing import List, Dict
import os.path

import pandas as pd
from tabulate import tabulate

normanlmfung

import asyncio
import random

async def generate_random_int():
    await asyncio.sleep(1)  # Simulating some asynchronous task
    return random.randint(1, 100)

async def main():
    results = await asyncio.gather(generate_random_int(), generate_random_int())
    print("Results:", results)

normanlmfung

'''
This is a simply python util that fetches order book using CCXT Pro
    https://docs.ccxt.com/#/ccxt.pro.manual?id=instantiation

Usage:
    python ccxt_ws_ob_provider.py --ticker BTC/USDT:USDT --ts_delta_observation_ms_threshold 150 --ts_delta_consecutive_ms_threshold 150

Keep track of latency issues:
    a) ts_delta_observation_ms (Default 150 ms): Keep track of server clock vs timestamp from exchange
    b) ts_delta_consecutive_ms (Default 150 ms): Keep track of gap between consecutive updates

normanlmfung

'''
Example 3, What if market just follow Fibonacci? After ten itterations, it'd converge to usd61.4k, as price_range narrows!
0: 54237.944
1: 65883.57060800001
2: 58686.57336425601
3: 63134.3176608898
4: 60385.61168557012
5: 62084.31197831768
6: 61034.51519739968

normanlmfung

# Example 2, Big chop on 1 May 2024
swing_low = 56500 # 20240501
swing_high = 64000 # 20240425
retracement_level = estimate_fib_retracement(swing_low, 2, swing_high, 1)
''' 
Fibonacci predicted bounce back to 61135.0 (vs actual 64.5k on 4 May 2024). 
What happenned?
a) weaker than expected employment data
    Unemployment act 3.9 est 3.8  https://tradingeconomics.com/united-states/unemployment-rate
    Non farm payroll act 175k est 243k https://tradingeconomics.com/united-states/non-farm-payrolls

normanlmfung

MAGIC_FIB_LEVELS = [0, 0.236, 0.382, 0.5, 0.618, 0.786, 1.00, 1.618, 2.618, 3.618, 4.236]

# https://blog.quantinsti.com/fibonacci-retracement-trading-strategy-python/
def estimate_fib_retracement(
        swing_low: float, 
        swing_low_idx: int, 
        swing_high: float, 
        swing_high_idx: int,
        target_fib_level: float = 0.618 # Magic 618
    ):

normanlmfung

from datetime import datetime
from typing import Dict, List, Union, NoReturn
import pandas as pd

from ccxt.binance import binance
from ccxt.bitmex import bitmex
from ccxt.okx import okx
from ccxt.base.exchange import Exchange
from ccxt.base.exchange import Exchange as CcxtExchange

normanlmfung

,exchange,symbol,timestamp_ms,open,high,low,close,volume,is_dummy,datetime_utc,datetime,year,month,day,datetime_diff_sec,hour,minute,candle_height,pct_change_close,ema_short_periods,ema_long_periods,ema_close,std,h_l,h_pc,l_pc,tr,atr,hurst_exp,boillenger_upper,boillenger_lower,close_delta,up,down,rsi,macd,signal,macd_minus_signal,ema_short_slope,ema_long_slope,gap_close_vs_ema,close_above_or_below_ema,close_vs_ema_inflection
0,okx_linear,BTC/USDT:USDT,1709222400000.0,62787.3,62920.1,61501.4,62208.9,1572168.0,False,2024-02-29 16:00:00,2024-03-01 00:00:00,2024,3,1,,0,0,1418.699999999997,,62208.9,62208.9,62208.9,,1418.699999999997,,,1418.699999999997,,,,,,,,,0.0,0.0,0.0,,,0.0,,0.0
1,okx_linear,BTC/USDT:USDT,1709226000000.0,62208.9,62433.0,60550.0,61038.5,1855491.0,False,2024-02-29 17:00:00,2024-03-01 01:00:00,2024,3,1,3600.0,1,0,1883.0,-1.8814028217827405,62125.3,62180.35365853659,62180.35365853659,,1883.0,224.09999999999854,1658.9000000000015,1883.0,,,,,-1170.4000000000015,0.0,1170.4000000000015,,-55.0536585365