First backtest I have posted, this site is awesome! Instead of using this strategy w/ individual stocks I chose major market etfs (i.e. spy, dia, qqqq). I changed the long moving average to 100 days from 50, left the short moving average at 10. I added one line that adds commissions per shares traded (not sure how realistic this is, I don't trade in real life) I really like the way this performs during major downturns in SP500.
Total Returns
--
Alpha
--
Beta
--
Sharpe
--
Sortino
--
Max Drawdown
--
Benchmark Returns
--
Volatility
--
| Returns | 1 Month | 3 Month | 6 Month | 12 Month |
| Alpha | 1 Month | 3 Month | 6 Month | 12 Month |
| Beta | 1 Month | 3 Month | 6 Month | 12 Month |
| Sharpe | 1 Month | 3 Month | 6 Month | 12 Month |
| Sortino | 1 Month | 3 Month | 6 Month | 12 Month |
| Volatility | 1 Month | 3 Month | 6 Month | 12 Month |
| Max Drawdown | 1 Month | 3 Month | 6 Month | 12 Month |
def initialize(context):
# Define which stocks we want to work with. I'm starting with just one, but you can add up to 10.
context.sids = [sid(8554), sid(2174), sid(19920)]
context.N = len(context.sids)
# This section initializes a dictionary that will later track whether we invested in a particular stock
context.invested = {}
for cur_sid in context.sids:
context.invested[cur_sid] = False
# Adds commission per trade
# Add commission per trade
# Add commission per trade
set_commission(commission.PerShare(cost=.10))
def handle_data(context, data):
# Uncomment the line below if you want to log the sids that you're testing. Be careful, it's a lot of
# log lines if you're doing a full backtest!
# log.info(data.keys())
# allocate part of our portfolio for each sid in our list
cash_per_sid = context.portfolio.starting_cash / context.N
# This loop goes over the specified sids
for cur_sid in context.sids:
# skip sids that have no data (like Facebook pre-IPO)
if not (cur_sid) in data:
continue
# buy if short moving average crossed long moving average going up
if (data[cur_sid].mavg(10) > data[cur_sid].mavg(100)) and not context.invested[cur_sid]:
order_in_cash(data, cur_sid, cash_per_sid)
context.invested[cur_sid] = True
# sell if short moving average crossed long moving average going down
elif (data[cur_sid].mavg(10) < data[cur_sid].mavg(100)) and context.invested[cur_sid]:
# log the fact that we're closing our position, and the conditions that triggered it
log.info('Selling SID %i by %i shares. mavg(10) is %i, mavg(100) is %i' %(cur_sid, context.portfolio.positions[cur_sid].amount, data[cur_sid].mavg(10), data[cur_sid].mavg(100)))
# sell the amount we invested
order(cur_sid, -context.portfolio.positions[cur_sid].amount)
context.invested[cur_sid] = False
def order_in_cash(data, security, cash):
# This function calculates how many shares to buy given the price and cash available
shares = cash // data[security].price
# log.debug("security: %i, cash: %f, shares: %i"% (security, cash, shares))
if shares < 0:
shares += 1
# to fix python's flooring of negative integer division.
# log the fact that we're going long, and the conditions that triggered it
log.info('Ordering %i shares of sid %i. mavg(10) is %i, mavg(100) is %i' %(shares, security, data[security].mavg(10), data[security].mavg(100)))
order(security, shares)
