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Automated Trading System menggunakan Support Vector Machine
(SVM) untuk pasar Valuta Asing
Adhityo Priyambodo
Why Foreign Exchange?
Likuiditas Tinggi
4 Triliun dollar transaksi per hari
Volatilitas Tinggi
Risiko Tinggi vs Gain Tinggi
Factor To Consider?
Politik
Psikologi Pasar
Ekonomi
The Market Structure?
THE PROBLEM?
“90-95% of Retail Traders Lose All Their Money”
forexfactory.com
HAD LOST
3.000 USD dalam waktu 3 jamNovember 2010
12.450 USD dalam waktu 4 jamFOMC meeting Mei 2011
..... THE CAUSE?
Analisa trend pergerakan harga.Pengelolaan resiko (risk management).Pengelolaan dana (money management).Kurang kendali atas emosi diri (greed, fear,
ego).
..... SO ?
“Bagaimana membuat Automated Trading
System untuk melakukan proses perdagangan
valuta asing dengan memanfaatkan teknik
data mining yang efektif dan efisien?”
Research Focus?
Support Vector Machine (SVM)
GBP/USD
Why SVM?
Akurasi Lebih Tinggi Ukuran dataset lebih kecilProses training lebih cepatPencegahan Overfitting (SRM)
The Research Method?
PENDEFINISIAN MASALAH
STUDI LITERATUR
ANALISIS
PERANCANGAN
IMPLEMENTASI
UJI COBA
The Research Data?
The Concept?
The Design?
The Data Acquisition Process?
SIMULASI TAHAP 1
EKSTRAKSI DATA BID CLASSIFICATION
LEARNING BID CLASSIFICATION DATA
SIMULASI TAHAP 2
EKSTRAKSI DATA LOSS TRANSACTION
The Model Design?
Bid Classification Service
I N P U TBID CLASSIFICATION
SERVICE
BUY
SELL
Trend Classification Service
I N P U TTREND CLASSIFICATION
SERVICE
STRONG BULLISH
BEARISH
STRONG BEARISH
BULLISH
CONSOLIDATION
Risk Classification
I N P U TRISK CLASSIFICATION
SERVICE
VERY HIGH
LOW
VERY LOW
HIGH
Buy Risk Classification Model
BUY SIGNALBUY RISK
CLASSIFICATION
RISK SIGNAL
RISK LEVEL
Sell Risk Classification Model
SELL SIGNALSELL RISK
CLASSIFICATION
RISK SIGNAL
RISK LEVEL
The Experiment?
Simulasi Backtest Timeframe 15 Menit (2003 – 2010)
Simulasi Backtest Timeframe 30 Menit (2003 – 2010)
Simulasi Backtest Timeframe 60 Menit (2003 – 2010)
Simulasi Realtime 25 Nov 2011 – 1 Desember 2011
TREND CLASSIFICATION
BID CLASSIFICATION
SIZE CALCULATION
RISK CLASSIFICATION
DATA GATHERING
ORDER
.... And The Result?
Algoritma 1 Algoritma 2 Algoritma 3 Data Mining82
84
86
88
90
92
94
TF 15TF 30TF 60
Algoritma 1 Algoritma 2 Algoritma 3 Data Mining0.00
2,000.00
4,000.00
6,000.00
8,000.00
10,000.00
12,000.00
14,000.00
.. And The Conclusion?
SVM mempunyai akurasi lebih tinggi
Kondisi ekstrim, SVM tidak berfungsi dengan baik
.. For Future Works?
Penggunaan mekanisme dimension reduction seperti SOM, PCA, ICA.
Implementasi Searching Algorithm untuk Risk Management