Bitcoin Price Forecasting

Naveen VenkataramanJune 4th, 2015

Goal: Forecast Net Profit For May 2015

• Collect BTC Data• Analyze Price Volatility• Isolate Stationary Series• accounting for stationarity, seasonality and trends

• Comparison of Models, Forecasts and Metrics• Potential Model Improvements / Follow-up Analysis

STEP 1: FETCH BTC PRICE DATA

STEP 2: CONDUCT EXPLORATORY ANALYSIS

summary(btc.data)OHLC, Volume, Date Ranges

tsdisplay(btc.data)

adf.test(btc.data)

plot(decompose(ts(data, frequency=365), type=type))Separating out trend and seasonality

STEP 3: ISOLATE 2015 PRICES(TO CREATE TRAIN / TEST DATA)

2015 Data is Stationary

Train: Jan – Apr 2015Test: May 2015 (28 days)

JAN – APR data: Possibly an MA(2) process

STEP 4: MODEL FITTING AND FORECASTING

Holt Winters 1Exponential smoothing

With trendWithout seasonal component

Holt Winters 2Exponential smoothing

Without trendWithout seasonal component

ARIMA

ARFIMAfractional differencing

Forecasting Metrics Indicate ARFIMA To Be The Best ModelHolt Winters (without trend and without seasonality) is next best

Follow-on Analysis• Use Cross Validation for tuning parameters• Factor in Volume information in relation to price• Estimate discontinuous / missing information on certain

trading days• Use Complete dataset (2011 – 2015)• Weigh recent information more• Smooth Extreme Volatility

• Evaluate Co-pair trading strategy with other asset classes• Gold• Currencies

APPENDIX

Using Quandl• Setup an account (FREE)• Get an API KEY (FREE)• Install Quandl library (based on platform)• Choose data exchange format

• Supported platforms: https://www.quandl.com/tools/full-list

Learning About Bitcoin• “Mastering Bitcoin”

by Andreas Antonopoulos• MOOC:

http://digitalcurrency.unic.ac.cy/free-introductory-mooc• MIT • Bitcoin Club• Media Lab