Optimal Portfolio and Trading Strategy Using Machine Learning
Log in to collectOnsite backtest IDE
Quant Buffet native backtest IDEEdit and run Quant Buffet Python for Optimal Portfolio and Trading Strategy Using Machine Learning in the browser. Results update live with equity, drawdown, and metrics charts. Allowed: backtest.data, backtest.engine, backtest.metrics, numpy, pandas. Define ASSETS and make_on_day(prices). Shortcut: Ctrl+Enter. API docs →
Quant Buffet syntax cheat sheet (copy / insert)
Paste these fragments into the editor. The sandbox rejects QuantConnect, os, and network libraries.
from __future__ import annotations
import numpy as np
import pandas as pd
from backtest.data import load_daily_prices
from backtest.engine import EngineConfig, PortfolioEngine
from backtest.metrics import compute_metricsASSETS = ["SPY", "QQQ", "TLT", "GLD", "BIL"]def make_on_day(prices: pd.DataFrame):
cols = [c for c in ASSETS if c in prices.columns]
sma = prices[cols].rolling(200, min_periods=200).mean()
state = {"last": None}
def on_day(engine: PortfolioEngine, dt: pd.Timestamp) -> None:
if sma.loc[dt].isna().all():
return
key = (dt.year, dt.month)
if state["last"] == key:
return
state["last"] = key
long = [
s for s in cols
if pd.notna(prices.at[dt, s]) and pd.notna(sma.at[dt, s])
and prices.at[dt, s] > sma.at[dt, s]
]
weights = {} if not long else {s: 1.0 / len(long) for s in long}
engine.set_target_weights(dt, weights)
ready = sma.dropna(how="all").index.min() if sma.notna().any().any() else None
return on_day, readyengine.set_target_weights(dt, {"SPY": 0.60, "BIL": 0.40})Live backtest performance
Accent = strategy · dashed grey = buy-and-hold benchmark
Export to your platform
Transform Quant Buffet lab code (ASSETS + make_on_day / PortfolioEngine) into native classes for a third-party IDE — then copy and paste.
# Generated from Quant Buffet → QuantConnect LEAN
# Strategy: Optimal Portfolio and Trading Strategy Using Machine Learning
# Detected pattern: Absolute momentum
# Source uses Quant Buffet lab APIs (ASSETS + make_on_day / PortfolioEngine).
# Review fees, data, and risk before live trading — educational export only.
from AlgorithmImports import *
class QuantBuffetExport(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2010, 1, 1)
self.SetCash(100000)
tickers = ["SPY", "TLT", "GLD", "BIL"]
self.symbols = []
for t in tickers:
if "-" in t: # crypto proxy e.g. BTC-USD
self.symbols.append(self.AddCrypto(t.replace("-USD", ""), Resolution.Daily).Symbol)
else:
self.symbols.append(self.AddEquity(t, Resolution.Daily).Symbol)
self.Schedule.On(
self.DateRules.MonthStart(self.symbols[0]),
self.TimeRules.AfterMarketOpen(self.symbols[0], 30),
self.Rebalance,
)
# Logic: Long assets with positive 252-day return; equal-weight; monthly.
def Rebalance(self):
# Pattern: abs_momentum — Long assets with positive 252-day return; equal-weight; monthly.
# Default: equal-weight. Port your make_on_day weights here via SetHoldings.
w = 1.0 / len(self.symbols) if self.symbols else 0.0
for symbol in self.symbols:
self.SetHoldings(symbol, w)
Exported code uses the platform’s native classes and libraries. Install dependencies in your third-party IDE, then run. Validate before live trading.
Academic paper
Optimal Portfolio and Trading Strategy Using Machine Learning
Karim Ouazzane; Kai Tang; Mohamed Chahine Ghanem
London Metropolitan University
Teaser
Pick the best absolute/relative momentum asset; fall back to T-bills when momentum is negative. Universe: XLB, XLE, XLF, XLI, XLK, XLP, XLU, XLV, XLY, XLC, XLRE, BIL. Parameters: lookback=252; rebalance=monthly. Rebalanced on the engine's template schedule with 5 bps commission and 2 bps slippage. Because the paper's primary signal (ML, sentiment, or proprietary data) is not available in our public ETF engine, this draft uses a liquid ETF rule that preserves the paper's economic theme rather tha
Strategy in a nutshell
This research presents machine learning models for forecasting the future returns of a portfolio from NASDAQ semiconductors assets by financial analysis, optimization, and technical analysis to form a trading strategy. The performance of the portfolio is evaluated by back-testing. Data were collected from 2011 to 2019 for the sector of semiconductor companies listed on Nasdaq. The project consists of 4 sub-tasks. The first sub-task is to use the annual financial ratios of each company under the sector of semiconductors from 2011 to 2018 to project the company returns in 2019 using machine learning algorithms. Then, the top 5 highest-return assets would be selected to form a portfolio. After the optimization of the portfolio by Monte Carlo simulation, the classifiers adopt the technical ind
Economic rationale
Relative momentum captures cross-sectional continuation; absolute momentum gates risk when the trend is negative, shifting to cash. Related evidence from “Optimal Portfolio and Trading Strategy Using Machine Learning”: This research presents machine learning models for forecasting the future returns of a portfolio from NASDAQ semiconductors assets by financial analysis, optimization, and technical analysis to form a trading strategy. The performance of the portfolio is evaluated by back-testing. Data were collected from 2011 to 2019 for the sector of semiconductor companies listed on Nasdaq. The project consists of 4 sub-tasks. The first sub-task is to use the annual financial ratios of each company under the