import talib
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, ExtraTreesClassifier
import numpy as np
import pandas
def initialize(context):
set_symbol_lookup_date('2012-01-01')
# Parameters to be changed
context.model = ExtraTreesClassifier(n_estimators=300)
context.lookback = 14
context.history_range = 1000
context.beta_coefficient = 0.0
context.percentage_change = 0.025
context.maximum_leverage = 2.0
context.number_of_stocks = 150
context.maximum_pe_ratio = 8
context.maximum_market_cap = 0.1e9
context.starting_probability = 0.5
# End of parameters
schedule_function(create_model, date_rules.month_start(), time_rules.market_open())
schedule_function(rebalance, date_rules.month_start(), time_rules.market_open())
schedule_function(trade, date_rules.every_day(), time_rules.market_open())
context.algorithm_returns = []
context.longs = []
context.shorts = []
context.training_stocks = symbols('SPY')
context.trading_stocks = []
context.beta = 1.0
context.beta_list = []
context.completed = False
def before_trading_start(context):
if context.completed: return
fundamental_df = get_fundamentals(query(fundamentals.valuation.market_cap)
.filter(fundamentals.company_reference.primary_exchange_id == 'NAS' or
fundamentals.company_reference.primary_exchange_id == 'NYSE')
.filter(fundamentals.valuation_ratios.pe_ratio < context.maximum_pe_ratio)
.filter(fundamentals.valuation.market_cap < context.maximum_market_cap)
.order_by(fundamentals.valuation.market_cap.desc())
.limit(context.number_of_stocks))
update_universe(fundamental_df.columns.values)
context.trading_stocks = [stock for stock in fundamental_df]
context.completed = True
def create_model(context, data):
X = []
Y = []
for S in context.training_stocks:
recent_prices = history(context.history_range, '1d', 'price')[S].values
recent_lows = history(context.history_range, '1d', 'low')[S].values
recent_highs = history(context.history_range, '1d', 'high')[S].values
recent_closes = history(context.history_range, '1d', 'close_price')[S].values
atr = talib.ATR(recent_highs, recent_lows, recent_closes, timeperiod=14)
prev_close = np.roll(recent_closes, 2)
upside_signal = (recent_prices - (prev_close + atr)).tolist()
downside_signal = (prev_close - (recent_prices + atr)).tolist()
price_changes = np.diff(recent_prices).tolist()
upper, middle, lower = talib.BBANDS(recent_prices,timeperiod=10,nbdevup=2,nbdevdn=2,matype=1)
upper = upper.tolist()
middle = middle.tolist()
lower = lower.tolist()
for i in range(15, context.history_range-context.lookback-1):
Z = price_changes[i:i+context.lookback] + upside_signal[i:i+context.lookback] + downside_signal[i:i+context.lookback] +\
upper[i:i+context.lookback] + middle[i:i+context.lookback] + lower[i:i+context.lookback]
if (np.any(np.isnan(Z)) or not np.all(np.isfinite(Z))): continue
X.append(Z)
if abs(price_changes[i+context.lookback]) > abs(price_changes[i]*(1+context.percentage_change)):
if price_changes[i+context.lookback] > 0:
Y.append(+1)
else:
Y.append(-1)
else:
Y.append(0)
context.model.fit(X, Y)
def rebalance(context, data):
context.completed = False
def trade(context, data):
if (context.account.leverage > context.maximum_leverage): return
if not context.model: return
for stock in context.trading_stocks:
if stock not in data:
context.trading_stocks.remove(stock)
for stock in context.trading_stocks:
if stock.security_end_date < get_datetime():
context.trading_stocks.remove(stock)
if stock in security_lists.leveraged_etf_list:
context.trading_stocks.remove(stock)
for one_stock in context.trading_stocks:
if get_open_orders(one_stock): continue
recent_prices = history(context.lookback+30, '1d', 'price')[one_stock].values
recent_lows = history(context.lookback+30, '1d', 'low')[one_stock].values
recent_highs = history(context.lookback+30, '1d', 'high')[one_stock].values
recent_closes = history(context.lookback+30, '1d', 'close_price')[one_stock].values
if (np.any(np.isnan(recent_prices)) or not np.all(np.isfinite(recent_prices))): continue
if (np.any(np.isnan(recent_lows)) or not np.all(np.isfinite(recent_lows))): continue
if (np.any(np.isnan(recent_highs)) or not np.all(np.isfinite(recent_highs))): continue
if (np.any(np.isnan(recent_closes)) or not np.all(np.isfinite(recent_closes))): continue
atr = talib.ATR(recent_highs, recent_lows, recent_closes, timeperiod=14)
prev_close = np.roll(recent_closes, 2)
upside_signal = (recent_prices - (prev_close + atr)).tolist()
downside_signal = (prev_close - (recent_prices + atr)).tolist()
price_changes = np.diff(recent_prices).tolist()
upper, middle, lower = talib.BBANDS(recent_prices,timeperiod=10,nbdevup=2,nbdevdn=2,matype=1)
upper = upper.tolist()
middle = middle.tolist()
lower = lower.tolist()
L = context.lookback
Z = price_changes[-L:] + upside_signal[-L:] + downside_signal[-L:] + upper[-L:] + middle[-L:] + lower[-L:]
if (np.any(np.isnan(Z)) or not np.all(np.isfinite(Z))): continue
prediction = context.model.predict(Z)
predict_proba = context.model.predict_proba(Z)
probability = predict_proba[0][prediction+1]
p_desired = context.starting_probability + 0.1*context.portfolio.returns
if probability > p_desired:
if prediction > 0:
if one_stock in context.shorts:
order_target_percent(one_stock, 0)
context.shorts.remove(one_stock)
elif not one_stock in context.longs:
context.longs.append(one_stock)
elif prediction < 0:
if one_stock in context.longs:
order_target_percent(one_stock, 0)
context.longs.remove(one_stock)
elif not one_stock in context.shorts:
context.shorts.append(one_stock)
else:
order_target_percent(one_stock, 0)
if one_stock in context.longs: context.longs.remove(one_stock)
elif one_stock in context.shorts: context.shorts.remove(one_stock)
if get_open_orders(): return
for one_stock in context.longs:
if not one_stock in context.trading_stocks:
context.longs.remove(one_stock)
else:
order_target_percent(one_stock, context.maximum_leverage/(len(context.longs)+len(context.shorts)))
for one_stock in context.shorts:
if not one_stock in context.trading_stocks:
context.shorts.remove(one_stock)
else:
order_target_percent(one_stock, (-1.0)*context.maximum_leverage/(len(context.longs)+len(context.shorts)))
order_target_percent(symbol('SPY'), (-1.0)*context.maximum_leverage*(context.beta*context.beta_coefficient))
def estimateBeta(priceY,priceX):
algorithm_returns = priceY
benchmark_returns = (priceX/np.roll(priceX,1)-1).dropna().values
if len(algorithm_returns) <> len(benchmark_returns):
minlen = min(len(algorithm_returns), len(benchmark_returns))
if minlen > 2:
algorithm_returns = algorithm_returns[-minlen:]
benchmark_returns = benchmark_returns[-minlen:]
else:
return 1.00
returns_matrix = np.vstack([algorithm_returns, benchmark_returns])
C = np.cov(returns_matrix, ddof=1)
algorithm_covariance = C[0][1]
benchmark_variance = C[1][1]
beta = algorithm_covariance / benchmark_variance
return beta
def handle_data(context, data):
record(cash = context.portfolio.cash/(1000000))
record(lev = context.account.leverage)
context.algorithm_returns.append(context.portfolio.returns)
if len(context.algorithm_returns) > 30:
recent_prices = history(len(context.algorithm_returns), '1d', 'price')[symbol('SPY')]
context.beta_list.append(estimateBeta(pandas.Series(context.algorithm_returns[-30:]), recent_prices))
if len(context.beta_list) > 7: context.beta_list.pop(0)
context.beta = np.mean(context.beta_list)
record(Beta=context.beta)
