Overall Statistics
Total Orders
778
Average Win
0.52%
Average Loss
0%
Compounding Annual Return
27.736%
Drawdown
1.700%
Expectancy
0
Start Equity
100000
End Equity
100515.51
Net Profit
0.516%
Sharpe Ratio
2.125
Sortino Ratio
1.831
Probabilistic Sharpe Ratio
56.856%
Loss Rate
0%
Win Rate
100%
Profit-Loss Ratio
0
Alpha
0
Beta
0
Annual Standard Deviation
0.087
Annual Variance
0.008
Information Ratio
2.384
Tracking Error
0.087
Treynor Ratio
0
Total Fees
$4.79
Estimated Strategy Capacity
$33000000.00
Lowest Capacity Asset
QQQ RIWIV7K5Z9LX
Portfolio Turnover
24.82%
 
'''
Usage: 
    def Initialize(self):
        self.log = Log(self)

    # code xxxxxx
    self.log.log("---->1")        
        
'''


from AlgorithmImports import *

import time

class Log(AlphaModel):
    def __init__(self, algo):
        self.timer = round(time.time() * 1000)
        self.algo = algo
        self.maxLine = 200
        self.count = 0

    def log(self, message):
        self.algo.Log(f"[LOG] {message}")

    def info(self, message):
        now = round(time.time() * 1000)
        timer = (now - self.timer) / 1000
        self.timer = now
        if (self.algo.Time <= self.algo.Time.replace(hour=9, minute=35)):
            self.algo.Log(f"[INFO] {message}")

    def debug(self, message):
        if self.count < self.maxLine:
            self.algo.Log(f"[DEUBG] {message}")
            self.count += 1


# region imports
from AlgorithmImports import *
from log import *
# endregion

class IB00(QCAlgorithm):

    def initialize(self):
        # Locally Lean installs free sample data, to download more data please visit https://www.quantconnect.com/docs/v2/lean-cli/datasets/downloading-data
        self.set_start_date(2020, 1, 1)  # Set Start Date
        self.set_end_date(2020, 1, 8)  # Set Start Date
        
        self.set_cash(100000)  # Set Strategy Cash
        self.add_equity("QQQ", Resolution.MINUTE)
        self.log = Log(self)

        self.SetBrokerageModel(BrokerageName.InteractiveBrokersBrokerage, AccountType.Cash)

        self.settings.rebalance_portfolio_on_insight_changes = True
        self.set_portfolio_construction(EqualWeightingPortfolioConstructionModel())
        
        self.SetExecution(ImmediateExecutionModel())

    def on_data(self, data: Slice):
        if not self.portfolio.invested:
            self.emit_insights(Insight.price("QQQ", timedelta(days=4), InsightDirection.UP))