Leveraging FinTech Compliance to Mitigate Cryptocurrency Volatility for Secure US Employee Retire…
Log in to collectOnsite backtest IDE
Quant Buffet native backtest IDEEdit and run Quant Buffet Python for Leveraging FinTech Compliance to Mitigate Cryptocurrency Volatility for Secure US Employee Retire… 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: Leveraging FinTech Compliance to Mitigate Cryptocurrency Volatility for Secure US Employee Retire…
# 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
Samuel Oladiipo Olabanji; Tunbosun Oyewale Oladoyinbo; Christopher Uzoma Asonze; Chinasa Susan Adigwe; Olalekan Jamiu Okunleye; Oluwaseun Oladeji Olaniyi
University of Maryland Global Campus; Federal University of Technology Owerri; University of the Cumberlands
Teaser
Hold assets with positive trailing return; equal-weight the winners. Universe: BTC-USD, ETH-USD. Parameters: lookback=252; rebalance=monthly. Rebalanced on the engine's template schedule with 5 bps commission and 2 bps slippage.
Strategy in a nutshell
The integration of cryptocurrencies, particularly Bitcoin, into retirement savings plans has recently garnered significant attention. This interest has been amplified by the U.S. Securities and Exchange Commission's approval of Bitcoin Exchange-Traded Funds (ETFs) in January 2024 and Fidelity Investments' decision to include Bitcoin in their 401(k) plans. These landmark developments represent a paradigm shift in retirement investment strategies, merging traditional financial planning with the dynamic and volatile world of cryptocurrencies. The entry of Bitcoin introduces novel challenges, including increased volatility and regulatory uncertainty, necessitating a comprehensive examination of its impacts on retirement savings. The study sought to explore the role of Financial Technology (Fin
Economic rationale
Positive time-series momentum indicates a favorable trend state; holding only assets with positive trailing returns tilts toward that premium. Related evidence from “Leveraging FinTech Compliance to Mitigate Cryptocurrency Volatility for Secure US Employee Retirement Benefits: Bitcoin ETF Case Study”: The integration of cryptocurrencies, particularly Bitcoin, into retirement savings plans has recently garnered significant attention. This interest has been amplified by the U.S. Securities and Exchange Commission's approval of Bitcoin Exchange-Traded Funds (ETFs) in January 2024 and Fidelity Investments' decision to include Bitcoin in their 401(k) plans. These landmark developments represent a paradigm shift in retirement investment strategies, merging traditional financial planning with the dy