The strategy trades 11 interest rate futures, buying losers and selling winners based on Friday-Monday returns, rebalanced weekly, with positions held from Monday open to Friday close.
ASSET CLASS: CFDs, futures, swaps | REGION: Global | FREQUENCY:
Weekly | MARKET: bonds | KEYWORD: Overnight-Intraday, Weekly, Reversal, Interest, Rate, Futures
I. STRATEGY IN A NUTSHELL
The strategy focuses on 11 major interest rate futures, including Federal Funds, Eurodollar, and U.S. Treasury contracts, using TickData open and close prices.
Formation: Identify winners and losers from Friday close to Monday open.
Portfolio construction: Go long past losers and short past winners.
Weighting: Contracts are weighted using formulas from the referenced academic paper.
Holding period: Monday open → Friday close (weekly cycle).
Rebalancing: Performed weekly.
This creates a zero-investment long-short portfolio exploiting short-term inefficiencies.
II. ECONOMIC RATIONALE
Investor overreaction → Price corrections follow sharp moves around market closures.
Low liquidity periods → Overnight/weekend closures amplify uncertainty and volatility.
Hedging demands → As shown by Hong and Wang (2000), shifts in hedging during closures drive reversals.
Nagel (2012) finds the Close-to-Open (CO-OC) reversal stronger in futures than stocks, making this setup particularly profitable.
The result is a systematic strategy capturing predictable weekly reversals in interest rate futures.
III. SOURCE PAPER
Market Closure and Short-Term Reversal [Click to Open PDF]
Corte, Kosowski, Wang
<Abstract>
A strategy that buys securities with low past overnight returns and sells securities with high past overnight returns generates sizeable out-of-sample intraday returns and Sharpe ratios in all major asset classes. This strategy, labeled as overnight-intraday reversal, delivers an average return that is about two to five times larger than those generated by the conventional reversal strategy. Investor heterogeneity, sentiment, market uncertainty and market-wide illiquidity fail to explain this overnight-intraday reversal return. Our findings are consistent with an asset class-specific market maker liquidity provision mechanism, and we find that cross-sectional return dispersion can predict the strategy returns in every asset class. A global two-factor model, consisting of the market and overnight-intraday reversal factor, well explains the intraday return variation of diversified portfolios across asset classes.
IV. BACKTEST PERFORMANCE
| Annualised Return | 6.88% |
| Volatility | 8.44% |
| Beta | 0.002 |
| Sharpe Ratio | 0.81 |
| Sortino Ratio | -6.567 |
| Maximum Drawdown | N/A |
| Win Rate | 35% |
V. FULL PYTHON CODE
from AlgorithmImports import *
class OvernightIntradayWeeklyReversalInterestRate(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2000, 1, 1)
self.SetCash(100000)
self.symbols = [
"CME_TY1", # 10 Yr Note Futures, Continuous Contract #1
"CME_FV1", # 5 Yr Note Futures, Continuous Contract #1
"CME_TU1" # 2 Yr Note Futures, Continuous Contract #1
"CME_ED1", # Eurodollar Futures, Continuous Contract #1
"CME_FF1", # 30-Day Fed Funds Futures, Continuous Contract #1
"CME_US1" # US Treasury Bond Futures, Continuous Contract #1
"CME_UT1" # US Ultra T-Bond Bond Futures, Continuous Contract #1
]
self.friday_close = {}
self.leverage = 1
for symbol in self.symbols:
data = self.AddData(QuantpediaFutures, symbol, Resolution.Daily)
data.SetFeeModel(CustomFeeModel())
data.SetLeverage(5)
self.friday_close[symbol] = 0
self.settings.minimum_order_margin_portfolio_percentage = 0.
def OnData(self, data):
# Saturday -> Friday close available
if self.Time.date().weekday() == 5:
for symbol in self.symbols:
if symbol in data and data[symbol]:
price = data[symbol].Value
if price != 0:
self.friday_close[symbol] = price
self.Liquidate()
# Tuesday -> Monday close available
elif self.Time.date().weekday() == 1:
returns = {}
for symbol in self.symbols:
# Check if data is still coming.
if self.securities[symbol].get_last_data() and self.time.date() > QuantpediaFutures.get_last_update_date()[symbol]:
self.liquidate(symbol)
continue
if symbol in data and data[symbol]:
price = data[symbol].Value
if price != 0 and symbol in self.friday_close and self.friday_close[symbol] != 0:
returns[symbol] = price / self.friday_close[symbol] - 1
self.friday_close.clear()
if len(returns) == 0:
return
ret_mean = np.mean([x[1] for x in returns.items()])
weight = {}
N = len(returns)
for symbol in returns:
weight[symbol] = -(1/N) * (returns[symbol] - ret_mean) * 100
for symbol in weight:
if data.contains_key(symbol) and data[symbol]:
self.SetHoldings(symbol, self.leverage * weight[symbol])
# Quantpedia data.
# NOTE: IMPORTANT: Data order must be ascending (datewise)
class QuantpediaFutures(PythonData):
_last_update_date:Dict[Symbol, datetime.date] = {}
@staticmethod
def get_last_update_date() -> Dict[Symbol, datetime.date]:
return QuantpediaFutures._last_update_date
def GetSource(self, config, date, isLiveMode):
return SubscriptionDataSource("data.quantpedia.com/backtesting_data/futures/{0}.csv".format(config.Symbol.Value), SubscriptionTransportMedium.RemoteFile, FileFormat.Csv)
def Reader(self, config, line, date, isLiveMode):
data = QuantpediaFutures()
data.Symbol = config.Symbol
if not line[0].isdigit(): return None
split = line.split(';')
data.Time = datetime.strptime(split[0], "%d.%m.%Y") + timedelta(days=1)
data['back_adjusted'] = float(split[1])
data['spliced'] = float(split[2])
data.Value = float(split[1])
if config.Symbol.Value not in QuantpediaFutures._last_update_date:
QuantpediaFutures._last_update_date[config.Symbol.Value] = datetime(1,1,1).date()
if data.Time.date() > QuantpediaFutures._last_update_date[config.Symbol.Value]:
QuantpediaFutures._last_update_date[config.Symbol.Value] = data.Time.date()
return data
# Custom fee model.
class CustomFeeModel(FeeModel):
def GetOrderFee(self, parameters):
fee = parameters.Security.Price * parameters.Order.AbsoluteQuantity * 0.00005
return OrderFee(CashAmount(fee, "USD"))VI. Backtest Performance
