normanlmfung

from enum import Enum
import time
from datetime import datetime
from typing import Dict, List
import logging
from tabulate import tabulate
import asyncio
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

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
import pandas as pd
from ccxt.binance import binance
from ccxt.base.exchange import Exchange

num_candles_limit : int = 100 # Depends on exchange but generally 100 ok!
param : Dict = {
        'apiKey' : None,
        'secret' : None,

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