1. BIG NIKKEI [INSERT WITTY TAGLINE] QF206 March2017
Presentation by: Joseph Kang, Noel,
Ng Min Min, Li Lin, Paw Jia Xun
BIG NIKKEI
[INSERT WITTY TAGLINE]

2. Index Introduction 1. Trading Strategies 2. Performance Metrics 3.4.
Tick-by-Tick Data Analysis 5.
Conclusion

3. Overview: Big Nikkei Futures (Large Contracts)
Index Overview
Project Objective
Index: Big Nikkei
Bloomberg Ticker: NK
Nikkei 225 Futures is based on The Nikkei Stock Average which is a major Japanese stock index used as a benchmark in various financial instruments
To find the optimal holding period by utilizing TD strategies with conservative & non-conservative approaches in conjunction with the 5 different alternative strategies
Compare and contrast the P&L generated by the various strategies against a set of performance metrics
Project Assumptions
Min by min data in separate csv files for different days
Per contract commission: 1000 Yen per round trip
Trade on Open
Maximum 20min holding period
70% in-sample, 30% out-sample
Bloomberg Ticker NK
Street Name Big  
Onshore
Before July 11 for onshore, July 19 for offshore 8:00 AM to 2:09 PM
Default 7:45 AM to 2:09 PM
Commission per contract ¥400

4. 2. Trading Strategies

5. Trading Strategies: TD Countdown & Alternative Strategies
Entering into a Long Position
TD Countdown
Comparing the current close with the low two bars earlier for a potential buy
Comparing the current close with the high two bars earlier for a prospective sell
Alternative Strategies: Requirements to Enter into a Long Position
TD Camouflage
The price of the current price bar must be below the close of the previous price bar
The close of the current price bar must be above the open of the current price bar
The low of the current price bar must be lower than the true low two price bars earlier
TD Clop
The open of the current price bar must be below the close and open of the previous price bar
The market must subsequently trade above both the open and close of the previous bar
TD Clopwin
The open and close of the current price bar must be contained within the open and close range of the previous price bar
The close of the current price bar must be above the close of the prior price bar
TD Open
The current price bar’s open must be less than the low of the prior price bar
It must then trade above that low
TD Trap
The open of the current price bar
Must be within the range of the previous price bar
Must then break above the high of that range

6. Model Upgrade: Inverse Optimiser
Inverse Optimiser Description
Example:
Holding Period: 10
Clop Sell
Inverse ? No
Yes
T-Statistic (In-sample)
1.1652
T-Statistic (Out-sample)
2.1033
Sharpe (In-sample)
0.0746
Sharpe (Out-sample)
0.2125
Risk-Reward (In-sample)
1.1185
1.5656
Risk-Reward (Out-sample)
0.8140
1.7331
St. Dev. (In-sample)
19,
St. Dev. (Out-sample)
20,
P&L (In-sample)1)
-83,9000
35,1000
P&L (Out-sample)1)
-62,1000
42,5000
Key Feature of Model: Inverse Optimiser
With incoming signal, making a “sell” instead of “buy”
Comparison between an inversed and non-inversed trade
Working on the basis of recycling a “bad” strategy/ signal,
Making a profit from a supposed loss
P&L are trade-based , inclusive of commissions

7. 3. Index Performance

8. Performance Metrics: In-Sample T-Statistics for All Trading Strategies
In-Sample T-Statistics against Holding Period for All Strategies

9. Performance Metrics: Breakdown of In-Sample T-Statistics
Countdown Buy & Sell
Clop Buy & Sell
Clopwin Buy & Sell
Camouflage Buy & Sell
Open Buy & Sell
Trap Buy & Sell

10. Performance Metrics: T-Statistics for Selected Trading Strategies
Countdown Buy
Countdown Sell
Camouflage Buy
Trap Sell

11. Performance Metrics: Reward-Risk Ratio
Risk-Reward Ratio for In-Sample
Risk-Reward Ratio for Out-Sample

12. Performance Metrics: Sharpe Ratio
Sharpe Ratio for In-Sample
Sharpe Ratio for Out-Sample

13. Performance Metrics (3): Equity Curve & Drawdown
In samples
Out samples

14. Performance Metrics (3): Equity Curve & Drawdown
In samples
Out samples

15. Performance Metrics (3): Equity Curve & Drawdown
In samples
Out samples

16. Performance Metrics (3): Equity Curve & Drawdown
In samples
Out samples

17. 4. Tick-by-Tick Data Analysis

18. Preliminary Data Findings
In-Sample Ratio X Y
Total PnL
Trade Count
Average PnL
St. Dev.
Sharpe
T-stat
RR Ratio 6 10
40000
1852
1479 7
-3000
1526
-10000
1726
-21000
1576 9
-50000
1561
Out-Sample Ratio X Y
Total PnL
Trade Count
Average PnL
St. Dev.
Sharpe
T-stat
RR Ratio 6 10
821
Outcome
Using optimal X = 6, and Y = 10;
While t-stat of in-sample is fairly large = ,
t-stat of out-sample =
Therefore highlighting the fact that the strategy is not sufficiently robust

19. 5. Conclusion

20. Several strategies with holding period that generate high t-statistics (>2) for in-sample tests
Countdown Buy: 3.75
Countdown Sell: 3.74
Camoflouge Buy: 2.37
Trap Sell: 2.37
Could not replicate the same performance out-sample tests
Countdown Buy:
Countdown Sell:
Camoflouge Buy:
Trap Sell:
In-sample vs Out-sample equity demonstrated contrasting performances
Unlikely that the strategies tested generate alpha