MickyDowns/deep-cnn-sat-image-time-series

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MickyDowns/deep-cnn-sat-image-time-series

MickyDowns

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Deep CNN and RNN for Satellite Image Time Series

deep-theano-rnn-lstm-car

Pack of Drones: Layered reinforcement learning (Q-learning w/ RNN) for complex "hunt" behaviors

Jupyter Notebook

fin_hft_analytics

analysis of high frequency quote and trade data for F ang GM. finds a variety of "seasonal" patterns using vwap and duration measures.

fin_portfolio_volatility

builds stock portfolios based on: 1. price momentum strategies as described by Jegadeesh and Titman (2001), 2. tripartite momentum as described by Lewellen (2002), and 3. volatility quantile as proposed by Han, Yang and Zhou (2013). reports sharpe, treynor, sortino, calmar, information, and capm alpha metrics and benchmarks against buy-and-hold strategy.

fin_equity_option_pricing

pricing european puts/calls and up-and-out puts using closed form and monte carlo simulation (cev process, euler and millstein discretization)

deep_tf_notminst

two sets of (tensorflow) neural nets: 1. convnets for notMINST image recognition, 2. RNNs for natural language processing

Jupyter Notebook

fin_time_series_fundamentals

implements basic time series analysis including: estimating IBM intra-day trade durations using Epanechnikov and GCV, fitting a MARS model for SP500 options and futures prices based on moneyness and days-to-exp, performing ADF unit-root test for a multivariate rate time series, finding and estimating cointegration vectors, and regressing COFI on selected rates.

fin_pricing_asian_options

pricing asian equity options used closed form (Turnbull, Levy and Geometric Average) and monte carlo simulation. employ a range of variance reduction techniques including antithetic variates, common random numbers, control variates, conditional monte carlo, stratified sampling and importance sampling.