This is an initial proposal for an algo to model dividends and splits. The intention is to be able to use unadjusted price data and to get historically correct transactions (position sizes and commissions). It should be tested more extensively before adoption.

The only way I could find to change positions due to splits on the fly was by changing the `_position` member on each security (otherwise security and portfolio values wouldn't be updated correctly). This requires the algo to be run on every row and so, it needs to be positioned before any algos that skip bars, such as RunMonthly. This and other constraints are described in the last paragraph of the docstring. This algorithm could be improved by someone more familiar with the library internals.

The input data is similar to what can be obtained from free sources such as Yahoo Finance. An example is provided below, showing backtests with adjusted prices and unadjusted prices with splits and dividends. The results will not be identical, since adjusted data considers that the dividend inflows are reinvested on the same security at the `ex` date. The simulation using CorporateActions will get the dividend inflows as cash at the `ex` date, but the cash will be reinvested on the whole portfolio only at the next rebalancing event.

Finally, this simulation doesn't completely correspond to reality since dividends are paid at a later time and not on the `ex` date. But since data about payment dates is not easy to obtain, this improvement was left for the future.

Sample code:
```
import pandas as pd
import yahooquery as yq
import bt

data = yq.Ticker(['GLD', 'TLT', 'VTI']).history(period='max', interval='1d').unstack('symbol')
data.index = pd.to_datetime(data.index)

divs = data['dividends']

splits = data['splits'].replace(0.0, 1.0)

close = data['adjclose']

splits_multiplier = splits.sort_index(ascending=False).cumprod().shift(1).ffill().fillna(1.0).sort_index()
unadjclose = data['close'] * splits_multiplier

s_adj = bt.Strategy('adj', [
                        bt.algos.RunMonthly(run_on_end_of_period=True),
                        bt.algos.SelectAll(),
                        bt.algos.WeighEqually(),
                        bt.algos.Rebalance()])

s_div = bt.Strategy('div', [
                        bt.algos.CorporateActions(divs, splits),
                        bt.algos.RunMonthly(run_on_end_of_period=True),
                        bt.algos.SelectAll(),
                        bt.algos.WeighEqually(),
                        bt.algos.Rebalance()])

b_adj = bt.Backtest(s_adj, close, initial_capital=100000, commissions=lambda quantity, price: 0, integer_positions=False)
b_div = bt.Backtest(s_div, unadjclose, initial_capital=100000, commissions=lambda quantity, price: 0, integer_positions=True)

r = bt.run(b_adj, b_div)

r.display()
r.plot()
```

Update algos.py

Removed white space on empty lines.

Merge from upstream

…olumns); added extra price column on test data to check