Presentasi Thesis - Untuk Print

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