bitcoin price forecasting
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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
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