Illiquidity Factor in China
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Quant Buffet native backtest IDEEdit and run Quant Buffet Python for Illiquidity Factor in China 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: Illiquidity Factor in China
# 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 126-day return; monthly.
def Rebalance(self):
scores = {}
for symbol in self.symbols:
hist = self.History(symbol, 126 + 5, Resolution.Daily)
if hist.empty: continue
close = hist["close"]
if hasattr(close, "unstack"):
close = close.unstack(level=0).iloc[:, 0]
if len(close) < 126 + 1: continue
scores[symbol] = float(close.iloc[-1] / close.iloc[-126 - 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
Is Illiquidity Priced in the Chinese Stock Market?
Jun Liu; Kai Wu; Lang Zheng
- NLTIAS School for Business and Society
- NLTilburg University
- ?Tilburg University - TIAS School for Business and Society
- Central University of Finance and Economics
- ?Central University of Finance and Economics (CUFE) - School of Finance
- ?Central University of Finance and Economics (CUFE)
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3787113
Strategy in a nutshell
Universe: Chinese A-shares from WIND (Shanghai + Shenzhen Stock Exchanges).
Signal: Inverse turnover (INVTO) = 1 ÷ turnover ratio.
Turnover ratio = traded shares ÷ total shares (per month).
Portfolio construction:
Value-weighted, rebalanced monthly.
Sort all stocks each month into 5 quintiles by INVTO.
Long quintile 5 (highest INVTO = lowest turnover = least traded = more illiquid).
Short quintile 1 (lowest INVTO = highest turnover = most liquid).
Economic rationale
Liquidity is central in China:
Trading mechanism: Call + continuous auction; low counterparties = higher liquidity risk.
Investor base: Retail dominance (≈99.7% of accounts) → speculative, short-term, high turnover.
Short selling restrictions: Limited (<1% of trading volume), gradually rising but still low → increases market friction.
Higher cost of capital: China’s equity cost is higher than the US (Carpenter et al., 2021), making liquidity factors especially powerful predictors.
Implication: Illiquidity premium exists → stocks with lower turnover (higher INVTO) deliver higher returns because investors demand compensation for bearing liquidity risk.