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Bitcoin Price Forecasting
Naveen VenkataramanJune 4th, 2015
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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
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STEP 1: FETCH BTC PRICE DATA
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STEP 2: CONDUCT EXPLORATORY ANALYSIS
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summary(btc.data)OHLC, Volume, Date Ranges
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tsdisplay(btc.data)
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adf.test(btc.data)
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plot(decompose(ts(data, frequency=365), type=type))Separating out trend and seasonality
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STEP 3: ISOLATE 2015 PRICES(TO CREATE TRAIN / TEST DATA)
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2015 Data is Stationary
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Train: Jan – Apr 2015Test: May 2015 (28 days)
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JAN – APR data: Possibly an MA(2) process
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STEP 4: MODEL FITTING AND FORECASTING
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Holt Winters 1Exponential smoothing
With trendWithout seasonal component
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Holt Winters 2Exponential smoothing
Without trendWithout seasonal component
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ARIMA
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ARFIMAfractional differencing
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Forecasting Metrics Indicate ARFIMA To Be The Best ModelHolt Winters (without trend and without seasonality) is next best
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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
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APPENDIX
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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
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Learning About Bitcoin• “Mastering Bitcoin”
by Andreas Antonopoulos• MOOC:
http://digitalcurrency.unic.ac.cy/free-introductory-mooc• MIT • Bitcoin Club• Media Lab