Coreversal in Chinese Equities
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Quant Buffet native backtest IDEEdit and run Quant Buffet Python for Coreversal in Chinese Equities 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
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: Coreversal in Chinese Equities
# Detected pattern: Momentum rotation
# 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: Hold top 1 by 21-day return; monthly.
def Rebalance(self):
scores = {}
for symbol in self.symbols:
hist = self.History(symbol, 21 + 5, Resolution.Daily)
if hist.empty: continue
close = hist["close"]
if hasattr(close, "unstack"):
close = close.unstack(level=0).iloc[:, 0]
if len(close) < 21 + 1: continue
scores[symbol] = float(close.iloc[-1] / close.iloc[-21 - 1] - 1)
ranked = sorted(scores.items(), key=lambda kv: kv[1], reverse=True)[:1]
for symbol in self.symbols:
self.SetHoldings(symbol, 0)
if ranked:
w = 1.0 / len(ranked)
for symbol, _ in ranked:
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
Coreversal: The Booms and Busts of Arbitrage Activities in China
Xin Liu; Zhigang Qiu; Luyao Shen; Weinan Zheng
- Renmin University of China
- ?School of Finance, Renmin University of China
- Capital University of Economics and Business
- ?International School of Economics and Management, Capital University of Economics and Business
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4122691
Strategy in a nutshell
The strategy targets Chinese A-share stocks from the Wind Economic Database, excluding the smallest 30% of firms and those listed under seven months. At the end of each month, stocks are ranked into deciles based on the past twelve months’ cumulative returns. A standard reversal strategy is formed by buying the bottom decile (past losers) and selling the top decile (past winners), with monthly rebalancing. To enhance timing, the strategy computes the average pairwise abnormal correlations within the winner and loser deciles to derive the CoREV (coreversal) measure. If CoREV is in the top quintile at month-end, the long-short reversal portfolio is implemented over the next year; otherwise, the strategy holds cash.
Economic rationale
Empirical research shows that arbitrageurs can temporarily destabilize prices, causing overshoots and subsequent reversals. High reversal trading intensity (high CoREV) indicates crowded trades that exaggerate deviations from fundamentals. By timing the reversal strategy based on CoREV, the strategy captures short-term profits from these exaggerated mispricings before they revert in the long term. This approach combines traditional reversal trading with market timing, exploiting periods when arbitrage activity amplifies potential gains.