def __init__(self, ticker, start_date, start_value, strategy):
"""Initialize for a certain ticker and start value.
- Ticker is a Yahoo ticker.
- Start value is assumed to be in the same currency as the underlying
ticker.
- Strategy should be a subclass of Strategy.
Note: this involves a HTTP call to Yahoo, so you need an internet
connection.
"""
self._strategy = strategy
if not isinstance(strategy, AbstractStrategy):
raise Exception("strategy argument should be a subclass of "
"AbstractStrategy class.")
self._state = State(ticker, start_date, start_value)
class StockMarketStrategySim:
_strategy = None
_state = None
_silent = False
def __init__(self, ticker, start_date, start_value, strategy):
"""Initialize for a certain ticker and start value.
- Ticker is a Yahoo ticker.
- Start value is assumed to be in the same currency as the underlying
ticker.
- Strategy should be a subclass of Strategy.
Note: this involves a HTTP call to Yahoo, so you need an internet
connection.
"""
self._strategy = strategy
if not isinstance(strategy, AbstractStrategy):
raise Exception("strategy argument should be a subclass of "
"AbstractStrategy class.")
self._state = State(ticker, start_date, start_value)
def set_silent(self):
self._silent = True
return self
def get_current_value(self):
"""Get the current value."""
return self._state.get_current_value()
def run(self):
"""Run the simulation from the given startdate until latest available
date in data.
"""
state = self._state
state.reset()
while True:
df_today = state.get_df_for_current_date()
self._evaluate_day(df_today)
if not state.increment_current_date():
self._log((
"Done! Reached date {}. Total trades: {}. "
"Total transactioncosts: {:0.2f}.")
.format(state.get_current_date(), state.get_num_trades(),
state.get_total_transactioncosts()))
break
if not self._silent:
plot = Plot()
plot.plot_results(state, self._strategy.__class__.__name__)
def _evaluate_day(self, df_day):
"""Process actions for a single day and update the portfolio value."""
open = df_day['Open'][0]
close = df_day['Close'][0]
prev_close = df_day['PrevClose'][0]
avg = (open + close) / 2.0
ticker_proc_change = (close - prev_close) / prev_close
prevclose_value = self._state.get_current_value()
if self._state.position_is_long():
if self._strategy.sell_trigger(self._state):
# Selling at halfway point today.
# Add perc. diff from Open to avg and sell.
self._state.apply_percentual_change((1 + (avg - open) / open))
self._sell()
else:
# Had a full day exposure to the market.
# Add perc. diff from Open to Close
self._state.apply_percentual_change(1 + ticker_proc_change)
else:
# Position is not long, so it's cash
if self._strategy.buy_trigger(self._state):
self._buy()
# Buying at halfway point today.
# Add perc. diff from avg to Close
self._state.apply_percentual_change((1 + (close - avg) / avg))
self._state.save_value_in_df()
self._strategy.update(self._state)
value_proc_change = (self._state.get_current_value() -
prevclose_value) / prevclose_value
self._log((
"{}. Your value at Close: {:8.2f} ({:5.2f} %). "
"Ticker at Close: {:8.2f} "
"({:5.2f} %). {}")
.format(df_day.index[0].date(), self._state.get_current_value(),
value_proc_change * 100, close, ticker_proc_change * 100,
self._strategy.status_update_suffix()))
def _buy(self):
"""Perform a buy action using all available cash."""
tc = self._get_transaction_cost(self._state.get_current_value())
self._state.set_long_position()
self._log("Bought long position for {0} (transaction cost: {1})"
.format(self._state.get_current_value(), tc))
self._state.apply_transaction_cost(tc)
def _sell(self):
"""Sell everything."""
tc = self._get_transaction_cost(self._state.get_current_value())
self._state.set_cash_position()
self._log("Sold long position for {0} (transaction cost: {1})"
.format(self._state.get_current_value(), tc))
self._state.apply_transaction_cost(tc)
def _get_transaction_cost(self, amount):
"""Return transaction costs for a given amount. This is for a Dutch
bank (in euro).
"""
return min(max(amount * 0.001 + 6.50, 10.0), 150.0)
def _log(self, s):
if not self._silent:
print(s)