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import cudf
import cuml
import cupy

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conda install -c rapidsai -c nvidia -c conda-forge -c defaults rapids=0.21 python=3.10

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conda activate rapids

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conda create -n rapids python=3.10

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# Summary statistics for the Buy-and-Hold strategy
pf.create_simple_tear_sheet(plot_data['buy_hold_cum_rets'].pct_change().dropna())

# Summary statistics for the 4-day SMA strategy
pf.create_simple_tear_sheet(plot_data['basic_stra_cum_rets'].pct_change().dropna())

# Summary statistics for the HMM-DC based 4-day SMA strategy
pf.create_simple_tear_sheet(plot_data['hmm_dc_stra_cum_rets'].pct_change().dropna())

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# Set the figure size
plt.figure(figsize=(15,7))

# Plot both the Buy-and-hold, SMA and HMM-DC-based strategy cumulative returns
plt.plot(plot_data.index, plot_data['buy_hold_cum_rets'], label = "Buy and Hold Returns")
plt.plot(plot_data.index, plot_data['basic_stra_cum_rets'], label = "SMA Strategy Returns", color='g')
plt.plot(plot_data.index, plot_data['hmm_dc_stra_cum_rets'], label = "HMM-DC-based Strategy Returns", color='r')
#plt.plot(plot_data.index, plot_data['hmm_stra_cum_rets'], label = "HMM-based Strategy Returns", color='y')

# Set the title of the graph

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# Compute the Buy-and-Hold cumulative returns
plot_data['buy_hold_cum_rets'] = np.exp(plot_data['returns'].cumsum())

# Compute the simple 4-day moving average strategy returns
plot_data['basic_stra_rets'] = plot_data['returns']*plot_data['signal'].shift(1)

# Compute the simple 4-day moving average strategy cumulative returns
plot_data['basic_stra_cum_rets'] = np.exp(plot_data['basic_stra_rets'].cumsum())

# Compute the HMM-DC-based strategy returns

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# Subset the data for plotting
plot_data = data.loc['2018':]

# Count the number of days in which we face different leverages
plot_data['dc_leverage'].value_counts()

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# Set the initial day to start the trading backtesting loop
initial_t = data.index.get_loc(data.loc['2018':].index[0])

# Print the initial day
initial_t

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# Create the backtesting loop
for t in range(initial_t, (len(data.index)-1)):
    # Create a data sample to be used for the trading computations
    data_sample = data[['R','returns']].iloc[:(t+1)]

    # Create an HMM model object
    dc_model = hmm.GaussianHMM(n_components = 2, covariance_type = "diag", n_iter = 200, random_state = 100)

    # Create the array input to be used for the HMM model
    dc_X = data_sample[['R']].values