Overconfidence Factor in China
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Quant Buffet native backtest IDEEdit and run Quant Buffet Python for Overconfidence 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: Overconfidence Factor in China
# Detected pattern: SMA trend
# 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 where close > SMA(200); equal-weight; monthly.
def Rebalance(self):
longs = []
for symbol in self.symbols:
hist = self.History(symbol, 200 + 5, Resolution.Daily)
if hist.empty: continue
close = hist["close"].unstack(level=0).iloc[:, 0] if hasattr(hist["close"], "unstack") else hist["close"]
if len(close) < 200: continue
if float(close.iloc[-1]) > float(close.iloc[-200:].mean()):
longs.append(symbol)
weight = 1.0 / len(longs) if longs else 0.0
for symbol in self.symbols:
self.SetHoldings(symbol, weight if symbol in longs else 0.0)
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
Behavioural Factors in China Stock Market
Jinpeng Liu
- Southwestern University of Finance and Economics
- ?Southwestern University of Finance and Economics (SWUFE) - China Center for Behavior Economics and Finance
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4134890
Strategy in a nutshell
The strategy focuses on A-share stocks listed on the Shanghai and Shenzhen stock exchanges, using data from the China Stock Market & Accounting Research (CSMAR) database. Overconfidence is proxied through abnormal trading volume (ATV), derived from time-series regressions of individual stock trading volume on total market trading volume. The residuals represent ATV, capturing deviations unexplained by market-wide activity.
Each month, stocks are first divided into two groups by size—small and large—based on the median market capitalization at time t–1. Within each size group, stocks are further split into high- and low-overconfidence portfolios according to their median ATV from the prior month. The strategy goes long on both small and large overconfident portfolios, while shorting their low-confidence counterparts. Portfolios are value-weighted and rebalanced monthly.
Economic rationale
Investor overconfidence reflects the tendency to overestimate one’s skill, information, or trading ability, often resulting in excessive trading activity. Abnormal trading volume is therefore a natural proxy for this behavioral bias.
While in developed markets overconfidence often leads to mispricing and negative outcomes, evidence from the Chinese A-share market suggests a different short-term dynamic. On a one-month horizon, portfolios of overconfident stocks tend to outperform, as sophisticated arbitrageurs do not fully exploit these inefficiencies within such a short timeframe. This makes abnormal trading volume a profitable behavioral signal in the context of China’s stock market.