import datetime as dt
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np

plt.rcParams.update({'figure.max_open_warning': 0})

p = 'backtesting-hns-liquidation-strategies/handshake-token_2020-2-18_2021-11-19.csv'
d = pd.read_csv(p,
                skiprows=1)
d.info()
d.head()

d.columns = d.columns.str.lower()
d['date'] = pd.to_datetime(d['date'])
d['yy-mm'] = (d['date'].dt.year.astype(str).str[2:]
              + '-' +
              d['date'].dt.month.astype(str).str.zfill(2))
d = (d[['date','yy-mm','close']]
     .sort_values(['date'])
     .reset_index(drop=True))
d.info()

print('\nDate Range: ' + str(d['date'].min())[:10] + ' - ' + str(d['date'].max())[:10])
d.head()

monthly_starting_hns = 1000
daily_mined_hns = 7
c = d.drop(columns=['yy-mm'])
summary = pd.DataFrame()
for start_row in np.arange(d.shape[0]-30):
    sub = c[start_row:start_row + 30].reset_index(drop=True)

    sub['open'] = sub.loc[0]['close']
    sub = sub[['date','open','close']].copy()
    sub['base_dollars'] = monthly_starting_hns
    sub['base_hns'] = 0
    sub['base_hns'] = monthly_starting_hns / sub.loc[0]['close']
    sub['mined_hns'] = daily_mined_hns
    sub['cum_mined_hns'] = sub['mined_hns'].cumsum()
    sub = sub.drop(columns=['mined_hns'])
    sub['total_hns'] = sub['base_hns'] + sub['cum_mined_hns']
    sub['total_dollars'] = sub['total_hns'] * sub['close']
    sub['month_end_delta'] = sub['total_dollars'] - monthly_starting_hns
    
    new_summary_row = pd.DataFrame(sub.loc[29]).T
    summary = summary.append(new_summary_row)
summary = summary.reset_index(drop=True)
summary.head()

plt.figure(figsize=(16,9))
bins=np.arange(-1000,2000,100)
plt.hist(summary['month_end_delta'],
         bins=bins,
         alpha=0.8)
plt.axvline(summary['month_end_delta'].quantile(.25), c='C1', alpha=0.8, zorder=0)
plt.axvline(summary['month_end_delta'].quantile(.50), c='C1', alpha=0.8, zorder=0)
plt.axvline(summary['month_end_delta'].quantile(.75), c='C1', alpha=0.8, zorder=0)
plt.gca().set_ylim([0,250])
plt.grid()
for spine in plt.gca().spines.values():
    spine.set_visible(False)
plt.gca().tick_params(
        axis='x',
        bottom=False)
plt.gca().tick_params(
    axis='y',
    left=False,
    right=False)

plt.ylabel('# 30-Day Periods')
plt.xlabel('30-Day Income (Delta From 30-Day Starting Balance: ${})'.format(monthly_starting_hns))
plt.title('''30-Day HNS Income Assuming Each 30-Day Period Starts with HNS Balance worth \${}
Daily HNS Income of {:2.2f} HNS, Backtesting 30-Day Periods over 2020-02-19 - 2021-11-18
{}% ({}/{}) / {}% ({}/{}) Periods with no Income / \$1K+ Income
25th, 50th, 75th Percentile End-of-Period Income = \${}, \${}, \${}'''
              .format(monthly_starting_hns, daily_mined_hns,
                      int(summary[summary['month_end_delta']<0].shape[0]/summary.shape[0]*100.),
                      summary[summary['month_end_delta']<0].shape[0], summary.shape[0],
                      int(summary[summary['month_end_delta']>=1000].shape[0]/summary.shape[0]*100.),
                      summary[summary['month_end_delta']>=1000].shape[0], summary.shape[0],
                      int(round(summary['month_end_delta'].quantile(.25))),
                      int(round(summary['month_end_delta'].quantile(.50))),
                      int(round(summary['month_end_delta'].quantile(.75)))))
rects = plt.gca().patches
for rect in rects:
    if rect.get_x() < 0:
        rect.set_color('C3')
    if rect.get_x() >= 0:
        rect.set_color('C0')

for monthly_starting_hns in np.arange(100,1100,100):
    daily_mined_hns = 7
    summary = pd.DataFrame()
    for start_row in np.arange(d.shape[0]-30):
        sub = c[start_row:start_row + 30].reset_index(drop=True)

        sub['open'] = sub.loc[0]['close']
        sub = sub[['date','open','close']].copy()
        sub['base_dollars'] = monthly_starting_hns
        sub['base_hns'] = 0
        sub['base_hns'] = monthly_starting_hns / sub.loc[0]['close']
        sub['mined_hns'] = daily_mined_hns
        sub['cum_mined_hns'] = sub['mined_hns'].cumsum()
        sub = sub.drop(columns=['mined_hns'])
        sub['total_hns'] = sub['base_hns'] + sub['cum_mined_hns']
        sub['total_dollars'] = sub['total_hns'] * sub['close']
        sub['month_end_delta'] = sub['total_dollars'] - monthly_starting_hns

        new_summary_row = pd.DataFrame(sub.loc[29]).T
        summary = summary.append(new_summary_row)
    summary = summary.reset_index(drop=True)

    plt.figure(figsize=(16,9))
    bins=np.arange(-1000,2000,100)
    plt.hist(summary['month_end_delta'],
             bins=bins,
             alpha=0.8)
    plt.axvline(summary['month_end_delta'].quantile(.25), c='C1', alpha=0.8, zorder=0)
    plt.axvline(summary['month_end_delta'].quantile(.50), c='C1', alpha=0.8, zorder=0)
    plt.axvline(summary['month_end_delta'].quantile(.75), c='C1', alpha=0.8, zorder=0)
    plt.gca().set_ylim([0,250])
    plt.grid()
    for spine in plt.gca().spines.values():
        spine.set_visible(False)
    plt.gca().tick_params(
            axis='x',
            bottom=False)
    plt.gca().tick_params(
        axis='y',
        left=False,
        right=False)

    plt.ylabel('# 30-Day Periods')
    plt.xlabel('30-Day Income (Delta From 30-Day Starting Balance: ${})'.format(monthly_starting_hns))
    plt.title('''30-Day HNS Income Assuming Each 30-Day Period Starts with HNS Balance worth \${}
    Daily HNS Income of {:2.2f} HNS, Backtesting 30-Day Periods over 2020-02-19 - 2021-11-18
    {}% ({}/{}) / {}% ({}/{}) Periods with no Income / \$1K+ Income
    25th, 50th, 75th Percentile End-of-Period Income = \${}, \${}, \${}'''
                  .format(monthly_starting_hns, daily_mined_hns,
                          int(summary[summary['month_end_delta']<0].shape[0]/summary.shape[0]*100.),
                          summary[summary['month_end_delta']<0].shape[0], summary.shape[0],
                          int(summary[summary['month_end_delta']>=1000].shape[0]/summary.shape[0]*100.),
                          summary[summary['month_end_delta']>=1000].shape[0], summary.shape[0],
                          int(round(summary['month_end_delta'].quantile(.25))),
                          int(round(summary['month_end_delta'].quantile(.50))),
                          int(round(summary['month_end_delta'].quantile(.75)))))
    rects = plt.gca().patches
    for rect in rects:
        if rect.get_x() < 0:
            rect.set_color('C3')
        if rect.get_x() >= 0:
            rect.set_color('C0')

    plt.savefig('backtesting-hns-liquidation-strategies/figures/final_hists_'+str(monthly_starting_hns)+'.pdf',
                dpi=450)

monthly_starting_hns = 500
daily_mined_hns = 7

stack = pd.DataFrame()
for yymm in d['yy-mm'].unique():
    sub = d[d['yy-mm']==yymm].reset_index(drop=True)
    sub.loc[sub['date'].dt.day==1, 'start/end'] = 'Start'
    sub.loc[sub.shape[0]-1, 'start/end'] = 'End'
    
    sub.loc[sub['start/end']=='Start', 'start_hns'] = monthly_starting_hns / sub['close']
    sub['daily_hns'] = daily_mined_hns + sub['start_hns'].fillna(0)
    sub['cum_hns'] = sub['daily_hns'].cumsum()
    sub['start_dollars'] = sub['start_hns'].fillna(method='ffill') * sub['close']
    sub['cum_dollars'] = sub['cum_hns'] * sub['close']
    
    sub.loc[sub['start/end']=='End', 'month_end_delta'] = monthly_starting_hns
    sub['month_end_delta'] = sub['cum_dollars'] - sub['month_end_delta']
    
    stack = (stack.append(sub).reset_index(drop=True))
stack.tail(32)

stack['c_low_price'] = stack['close'].rolling(30).min()
stack['c_high_price'] = stack['close'].rolling(30).max()
stack['c_low_usd'] = stack['cum_hns'] * stack['c_low_price']
stack['c_high_usd'] = stack['cum_hns'] * stack['c_high_price']

sub = stack[stack['yy-mm']=='20-03'].reset_index(drop=True)

plt.figure(figsize=(16,9))

ax1 = plt.gca()
lns1 = ax1.plot(sub['date'], sub['cum_dollars'], 
                label='Value of Total (Starting + Daily Purchased) hns (Dollars)')
lns2 = ax1.plot(sub['date'], sub['start_hns'].fillna(method='ffill')*sub['close'],
                label='Value of Starting hns (Dollars)')

ax1.set_title('Daily Dollar Value of Purchases and Retained hns')
ax1.set_xlabel('Date')
ax1.set_ylabel('Accumulated Dollar Value')
ax1.grid()
ax1.set_ylim([-100,2000])

ax2 = ax1.twinx()
lns3 = ax2.bar(sub['date'], sub['close'], color='C2', alpha=0.125,
               label='Daily HNS Price')
ax2.set_ylim(bottom = ax1.get_ylim()[0]*0.001,
             top = ax1.get_ylim()[1]*0.001)
ax2.set_ylabel('Daily HNS Price')

lns4 = ax1.fill_between(sub['date'], sub['c_low_usd'], sub['c_high_usd'], alpha=0.1,
                        label='30-Day High/Low Price Band')

for ax in [ax1, ax2]:
    for spine in ax.spines.values():
        spine.set_visible(False);
    ax.tick_params(
        axis='y',
        left=False,
        right=False)
    ax.tick_params(
        axis='x',
        bottom=False)

lines = lns1+lns2+[lns3]+[lns4]
labels = [l.get_label() for l in lines]
ax.legend(lines, labels, loc=2);

for yymm in stack['yy-mm'].unique()[1:]:
    sub = stack[stack['yy-mm']==yymm].reset_index(drop=True)

    plt.figure(figsize=(16,9))

    ax1 = plt.gca()
    lns1 = ax1.plot(sub['date'], sub['cum_dollars'], 
                    label='Value of Total (Starting + Daily Purchased) HNS (Dollars)')
    lns2 = ax1.plot(sub['date'], sub['start_hns'].fillna(method='ffill')*sub['close'],
                    label='Value of Starting HNS (Dollars)')

    ax1.set_title('''Daily Dollar Value of Purchases and Retained HNS During {}
Assuming Each Month Starts with hns Balance worth \${}
Daily HNS Income of {:2.2f} HNS
Starting / Ending HNS Price \${:1.3f} / \${:1.3f}'''.format(yymm,
                                                            monthly_starting_hns,
                                                            daily_mined_hns,
                                                            sub.iloc[0]['close'],
                                                            sub.iloc[-1]['close']))
    
    ax1.set_xlabel('Date')
    ax1.set_ylabel('Accumulated Dollar Value')
    ax1.grid()
    ax1.set_ylim([-100,1800])

    ax2 = ax1.twinx()
    lns3 = ax2.bar(sub['date'], sub['close'], color='C2', alpha=0.125,
                   label='Daily HNS Price')
    ax2.set_ylim(bottom = ax1.get_ylim()[0]*0.001,
                 top = ax1.get_ylim()[1]*0.001)
    ax2.set_ylabel('Daily HNS Price')
    
    lns4 = ax1.fill_between(sub['date'], sub['c_low_usd'], sub['c_high_usd'], alpha=0.1,
                            label='30-Day High/Low Price Band')

    for ax in [ax1, ax2]:
        for spine in ax.spines.values():
            spine.set_visible(False);
        ax.tick_params(
            axis='y',
            left=False,
            right=False)
        ax.tick_params(
            axis='x',
            bottom=False)

    lines = lns1+lns2+[lns3]+[lns4]
    labels = [l.get_label() for l in lines]
    ax.legend(lines, labels, loc=2);
    
    plt.savefig('backtesting-hns-liquidation-strategies/figures/lines_' + yymm + '.pdf',
                dpi=450)