#!/usr/bin/env python
# coding: utf-8

# # Example of using the model module
# This notebook shows how to use the components defined in the model. The model module is meant as a light-weight trade model which allows flexible portfolio construction and valuation. The portfolio then handles the position management (long/short), and p&l accounting.

# In[1]:


import tia.analysis.model as model
import pandas as pd

from tia.util.fmt import new_dynamic_formatter, DynamicColumnFormatter


# In[2]:


get_ipython().run_line_magic('matplotlib', 'inline')
import matplotlib.pyplot as plt
try:
    plt.style.use('fivethirtyeight')
    plt.rcParams['lines.linewidth'] = 1.4
except:
    pass


# In[3]:


# Load microsoft - note it retrieves the dividends too
msft = model.load_yahoo_stock('MSFT', start='1/1/2010')
msft.pxs.frame.tail()


# In[4]:


fig, axes = plt.subplots(1, 2, figsize=(12, 3))
msft.pxs.dvds.resample('Q', how='sum').to_period().plot(kind='bar', ax=axes[0], title='msft dividends')
msft.pxs.close.plot(ax=axes[1], title='msft close')


# In[5]:


#
# Create a signal for when the 10x20 moving average (buy/sell single contract)
# - you can create your own simulation and build trades (using model.Trade or
#   model.TradeBlotter)
from tia.analysis.ta import cross_signal, sma, Signal
ma10, ma20 = sma(msft.pxs.close, 10), sma(msft.pxs.close, 20)
sig = cross_signal(ma10, ma20)
trades = Signal(sig).close_to_close(msft.pxs.close)
# show last 10 trades
trades[-10:]


# In[6]:


# Build the portfolio
port = model.SingleAssetPortfolio(msft, trades)


# In[7]:


# show the ltd transaction level pl frame
port.pl.ltd_txn_frame.tail()


# In[8]:


# show the daily pl frame (rolled up by day)
port.pl.dly_frame.tail()


# In[9]:


# show the positions
F = new_dynamic_formatter(method='col', pcts=1, parens=0, trunc_dot_zeros=1)
F(port.positions.frame.tail(4))


# In[10]:


# Look at basic p/l stats
PLFMT = new_dynamic_formatter(method='row', precision=3, pcts=1, trunc_dot_zeros=1)

tmp = pd.DataFrame({'dly': port.pl.dly_details.summary, 
              'wkly': port.pl.weekly_details.summary, 
              'mthly': port.pl.monthly_details.summary, 
              'yrly': port.pl.annual_details.summary})
PLFMT(tmp)


# In[11]:


FMT = new_dynamic_formatter(method='row', precision=2, pcts=1, trunc_dot_zeros=1)


# In[12]:


# Look at basic ret stats (By default return on initial invest of each position)
tmp = pd.DataFrame({'dly': port.performance.dly_details.summary, 
              'wkly': port.performance.weekly_details.summary, 
              'mthly': port.performance.monthly_details.summary, 
              'yrly': port.performance.annual_details.summary})
FMT(tmp)


# In[13]:


# show the position stats
port.positions.stats


# In[14]:


# Easy to get portfolio subsets such as long/short, winner/losers
port.long.positions.frame.tail()


# In[15]:


# Get a specific position
pos = port.long.positions[57]


# In[20]:


# Get txn level detail associated with the position
pos.pl.txn_frame.head()


# ### Simple Report Provided (can add own splits but Long/Short, Win/Loss included)

# In[21]:


summary = model.PortfolioSummary()
# defaults included are long/short, win/loss but can provide a method to split as like
summary.include_long_short().include_win_loss()

# a few simple stats methods provided but easy to create own
analyze_fct = model.PortfolioSummary.analyze_returns
rpt = summary(port, analyze_fct)
FMT(rpt.T)


# ### Some graphs

# In[22]:


tmp = pd.DataFrame({'Long': port.long.pl.ltd_dly, 'Short': port.short.pl.ltd_dly, 'All': port.pl.ltd_dly})
_ = tmp.plot(title='P&L')


# In[24]:


# Show Daily Drawdowns
port.pl.dly_details.plot_ltd()
plt.title('P&L Drawdown')


# In[28]:


port.performance.dly_details.plot_ret_on_dollar(label='All')
port.long.performance.dly_details.plot_ret_on_dollar(label='Long')
port.short.performance.dly_details.plot_ret_on_dollar(label='Short')
plt.legend(loc='upper left', prop={'size':12})
plt.title('Return on $1')


# In[39]:


# Show the LTD Returns
port.performance.dly_details.plot_ltd()


# In[47]:


# Show the Monthly Return Histogram
port.performance.monthly_details.plot_hist(figsize=(7, 3))


# In[50]:


# See the range of returns for the positions
fig, ax = plt.subplots(1, 1, figsize=(9, 3))
port.positions.plot_ret_range(ax=ax)
plt.title('Position Return Range')