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pep8 conversion of python algos #13 (#7955)

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* t status
pep8 conversion

* Minor tweaks and rebase

* Various minor fixes

---------

Co-authored-by: Martin Molinero <martin.molinero1@gmail.com>
This commit is contained in:
Louis Szeto
2024-04-20 03:14:12 +08:00
committed by GitHub
parent d2669fb0c2
commit 08a3de9e2d
33 changed files with 312 additions and 312 deletions
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Algorithm.Python/IndicatorWarmupAlgorithm.py
+12 -12
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@@ -37,10 +37,10 @@ class IndicatorWarmupAlgorithm(QCAlgorithm):
self.__sd = { }
for security in self.securities:
self.__sd[security.key] = self.symbol_data(security.key, self)
self.__sd[security.key] = self.SymbolData(security.key, self)
# we want to warm up our algorithm
self.set_warmup(self.symbol_data.required_bars_warmup)
self.set_warmup(self.SymbolData.REQUIRED_BARS_WARMUP)
def on_data(self, data):
'''on_data event is the primary entry point for your algorithm. Each new data point will be pumped in here.
@@ -68,10 +68,10 @@ class IndicatorWarmupAlgorithm(QCAlgorithm):
return time.second == 0
class SymbolData:
RequiredBarsWarmup = 40
PercentTolerance = 0.001
PercentGlobalStopLoss = 0.01
LotSize = 10
REQUIRED_BARS_WARMUP = 40
PERCENT_TOLERANCE = 0.001
PERCENT_GLOBAL_STOP_LOSS = 0.01
LOT_SIZE = 10
def __init__(self, symbol, algorithm):
self.symbol = symbol
@@ -92,7 +92,7 @@ class IndicatorWarmupAlgorithm(QCAlgorithm):
def update(self):
self.is_ready = self.close.is_ready and self._adx.is_ready and self._ema.is_ready and self._macd.is_ready
tolerance = 1 - self.percent_tolerance
tolerance = 1 - self.PERCENT_TOLERANCE
self.is_uptrend = self._macd.signal.current.value > self._macd.current.value * tolerance and\
self._ema.current.value > self.close.current.value * tolerance
@@ -111,10 +111,10 @@ class IndicatorWarmupAlgorithm(QCAlgorithm):
if self.is_uptrend:
# 100 order lots
qty = self.lot_size
qty = self.LOT_SIZE
limit = self.security.low
elif self.is_downtrend:
qty = -self.lot_size
qty = -self.LOT_SIZE
limit = self.security.high
if qty != 0:
@@ -126,7 +126,7 @@ class IndicatorWarmupAlgorithm(QCAlgorithm):
limit = 0
qty = self.security.holdings.quantity
exit_tolerance = 1 + 2 * self.percent_tolerance
exit_tolerance = 1 + 2 * self.PERCENT_TOLERANCE
if self.security.holdings.is_long and self.close.current.value * exit_tolerance < self._ema.current.value:
limit = self.security.high
elif self.security.holdings.is_short and self.close.current.value > self._ema.current.value * exit_tolerance:
@@ -142,8 +142,8 @@ class IndicatorWarmupAlgorithm(QCAlgorithm):
# if we just finished entering, place a stop loss as well
if self.security.invested:
stop = fill.fill_price*(1 - self.percent_global_stop_loss) if self.security.holdings.is_long \
else fill.fill_price*(1 + self.percent_global_stop_loss)
stop = fill.fill_price*(1 - self.PERCENT_GLOBAL_STOP_LOSS) if self.security.holdings.is_long \
else fill.fill_price*(1 + self.PERCENT_GLOBAL_STOP_LOSS)
self.__current_stop_loss = self.__algorithm.stop_market_order(self.symbol, -qty, stop, "StopLoss at: {0}".format(stop))