from .base import AbstractStatistics
from ..compat import pickle
from ..price_parser import PriceParser

import datetime
import os
import pandas as pd
import numpy as np
import matplotlib
try:
    matplotlib.use('TkAgg')
except:
    pass
import matplotlib.pyplot as plt
import seaborn as sns


class SimpleStatistics(AbstractStatistics):
    """
    Simple Statistics provides a bare-bones example of statistics
    that can be collected through trading.

    Statistics included are Sharpe Ratio, Drawdown, Max Drawdown,
    Max Drawdown Duration.

    TODO think about Alpha/Beta, compare strategy of benchmark.
    TODO think about speed -- will be bad doing for every tick
    on anything that trades sub-minute.
    TODO think about slippage, fill rate, etc
    TODO brokerage costs?

    TODO need some kind of trading-frequency parameter in setup.
    Sharpe calculations need to know if daily, hourly, minutely, etc.
    """
    def __init__(self, config, portfolio_handler):
        """
        Takes in a portfolio handler.
        """
        self.config = config
        self.drawdowns = [0]
        self.equity = []
        self.equity_returns = []
        # Initialize in order for first-step calculations to be correct.
        current_equity = PriceParser.display(portfolio_handler.portfolio.equity)
        self.hwm = [current_equity]
        self.equity.append(current_equity)

    def update(self, timestamp, portfolio_handler):
        """
        Update all statistics that must be tracked over time.
        """
        # Retrieve equity value of Portfolio
        current_equity = PriceParser.display(portfolio_handler.portfolio.equity)
        self.equity.append(current_equity)

        if(len(self.equity) > 1):
            # Calculate percentage return between current and previous equity value.
            pct = ((self.equity[-1] - self.equity[-2]) / self.equity[-1]) * 100
            self.equity_returns.append(round(pct, 4))
            # Calculate Drawdown.
            self.hwm.append(max(self.hwm[-1], self.equity[-1]))
            self.drawdowns.append(self.hwm[-1] - self.equity[-1])

    def get_results(self):
        """
        Return a dict with all important results & stats.
        """
        statistics = {}
        statistics["sharpe"] = self.calculate_sharpe()
        statistics["drawdowns"] = pd.Series(self.drawdowns)
        statistics["max_drawdown"] = max(self.drawdowns)
        statistics["max_drawdown_pct"] = self.calculate_max_drawdown_pct()
        statistics["equity"] = pd.Series(self.equity)
        statistics["equity_returns"] = pd.Series(self.equity_returns)
        return statistics

    def calculate_sharpe(self, benchmark_return=0.00):
        """
        Calculate the sharpe ratio of our equity_returns.

        Expects benchmark_return to be, for example, 0.01 for 1%
        """
        excess_returns = pd.Series(self.equity_returns) - benchmark_return / 252

        # Return the annualised Sharpe ratio based on the excess daily returns
        return round(self.annualised_sharpe(excess_returns), 4)

    def annualised_sharpe(self, returns, N=252):
        """
        Calculate the annualised Sharpe ratio of a returns stream
        based on a number of trading periods, N. N defaults to 252,
        which then assumes a stream of daily returns.

        The function assumes that the returns are the excess of
        those compared to a benchmark.
        """
        return np.sqrt(N) * returns.mean() / returns.std()

    def calculate_max_drawdown_pct(self):
        """
        Calculate the percentage drop related to the "worst"
        drawdown seen.
        """
        drawdown_series = pd.Series(self.drawdowns)
        equity_series = pd.Series(self.equity)
        bottom_index = drawdown_series.idxmax()
        top_index = equity_series[:bottom_index].idxmax()

        pct = (
            (equity_series.ix[top_index] - equity_series.ix[bottom_index]) /
            equity_series.ix[top_index] * 100
        )
        return round(pct, 4)

    def plot_results(self):
        """
        A simple script to plot the balance of the portfolio, or
        "equity curve", as a function of time.
        """
        sns.set_palette("deep", desat=.6)
        sns.set_context(rc={"figure.figsize": (8, 4)})

        # Plot two charts: Equity curve, period returns
        fig = plt.figure()
        fig.patch.set_facecolor('white')

        df = pd.DataFrame()
        df["equity"] = pd.Series(self.equity)
        df["equity_returns"] = pd.Series(self.equity_returns)
        df["drawdowns"] = pd.Series(self.drawdowns)

        # Plot the equity curve
        ax1 = fig.add_subplot(311, ylabel='Equity Value')
        df["equity"].plot(ax=ax1, color=sns.color_palette()[0])

        # Plot the returns
        ax2 = fig.add_subplot(312, ylabel='Equity Returns')
        df['equity_returns'].plot(ax=ax2, color=sns.color_palette()[1])

        # drawdown, max_dd, dd_duration = self.create_drawdowns(df["Equity"])
        ax3 = fig.add_subplot(313, ylabel='Drawdowns')
        df['drawdowns'].plot(ax=ax3, color=sns.color_palette()[2])

        # Plot the figure
        plt.show()

    def get_filename(self, filename=""):
        if filename == "":
            now = datetime.datetime.utcnow()
            filename = "statistics_" + now.strftime("%Y-%m-%d_%H%M%S") + ".pkl"
            filename = os.path.expanduser(os.path.join(self.config.OUTPUT_DIR, filename))
        return filename

    def save(self, filename=""):
        filename = self.get_filename(filename)
        print("Save results to '%s'" % filename)
        with open(filename, 'wb') as fd:
            pickle.dump(self, fd)

@@ -185,58 +185,8 @@
 lib%0A
-try:%0A    matplotlib.use('TkAgg')%0Aexcept:%0A    pass%0A
 impo

Remove force backend