def initialize(context):
#Schedule a function'rebalance', to run once a month
schedule_function(schedule,
date_rules.month_start(),
time_rules.market_open(hours=0, minutes=3))
attach_pipeline(make_pipeline(), 'my_pipeline')
attach_pipeline(risk_loading_pipeline(), 'risk_factors')
def initialize(context):
context.day_count = 0
context.daily_message = "Day {}"
context.weekly_message = "Time to place some trades"
context.max_leverage = 1.0
context.max_position_size = 0.015
context.max_turnover = 0.95
algo.attach_pipeline(
make_pipeline(),
'data_pipe'
)
algo.attach_pipeline(
risk_loading_pipeline(),
'risk_pipe'
)
algo.schedule_function(
rebalance,
date_rule = algo.date_rules.week_start(),
time_rule = algo.time_rules.market_open()
)
def initialize(context):
"""
Called once at the start of the algorithm.
"""
# Rebalance every Monday when the market opens
algo.schedule_function(
rebalance,
algo.date_rules.week_start(),
algo.time_rules.market_open(),
)
# Record tracking variables at the end of each day.
algo.schedule_function(
record_vars,
algo.date_rules.every_day(),
algo.time_rules.market_close(),
)
# Set constraints
context.max_leverage = 1.0
context.max_pos_size = 0.015
context.max_turnover = 0.95
# Create our dynamic stock selector.
algo.attach_pipeline(make_pipeline(), 'pipeline')
algo.attach_pipeline(risk_loading_pipeline(), 'risk_pipeline')
def initialize(context):
# Here we set our slippage and commisions. Set slippage
# and commission to zero to evaulate the signal-generating
# ability of the algorithm independent of these additional
# costs.
set_commission(commission.PerShare(cost=0.0, min_trade_cost=0))
set_slippage(slippage.VolumeShareSlippage(volume_limit=1, price_impact=0))
context.spy = sid(8554)
algo.attach_pipeline(make_pipeline(), 'long_short_equity_template')
# attach the pipeline for the risk model factors that we
# want to neutralize in the optimization step
algo.attach_pipeline(risk_loading_pipeline(), 'risk_factors')
# Schedule my rebalance function
schedule_function(func=rebalance,
date_rule=date_rules.every_day(),
time_rule=time_rules.market_open(hours=0, minutes=30),
half_days=True)
# record my portfolio variables at the end of day
schedule_function(func=recording_statements,
date_rule=date_rules.every_day(),
time_rule=time_rules.market_close(),
half_days=True)
def initialize(context):
"""
Called once at the start of the algorithm.
"""
set_slippage(slippage.FixedSlippage(spread=0.00))
set_commission(commission.PerShare(cost=0, min_trade_cost=0))
schedule_function(rebalance_portfolio, TRADE_FREQ,
time_rules.market_open(minutes=1))
# Record tracking variables at the end of each day.
schedule_function(log_metrics, date_rules.every_day(),
time_rules.market_close())
# Set up universe
# base_universe = AverageDollarVolume(window_length=63, mask=QTradableStocksUS()).percentile_between(80, 100)
universe = AverageDollarVolume(
window_length=63, mask=QTradableStocksUS()).percentile_between(40, 60)
# create alpha factors and machine learning pipline
ml_pipeline = make_ml_pipeline(alpha_factors=make_alpha_factors(),
universe=universe,
lookback=TRAINING_PERIOD,
lookahead=HOLDING_PERIOD)
attach_pipeline(ml_pipeline, 'alpha_model')
attach_pipeline(risk_loading_pipeline(), 'risk_loading_pipeline')
context.past_predictions = {}
context.realized_rmse = 0
context.realized_ic = 0
context.long_short_spread = 0
def initialize(context):
"""
バックテスト開始時に呼び出される、アルゴリズムの中心となるメイン関数を、Quantopianでは
initialize関数と呼びます。
パラメータ
----------
context : アルゴリズムコンテキスト
バックテストの状態を保存するために用いられるオブジェクト。
contextは、initialize, before_trading_start, handle_data, scedule_functionから呼び
出される全ての関数に渡されます。
contextは現在のポジションに関する情報を回収ために用いることのできるポートフォリオ特性を提供します。
"""
# パイプラインをアルゴリズムに取り込む（notebookでいうところのrun_pipelineに相当）
# run_pipelineと違うのは、start_dateとend_dateが引数に存在しない点！
algo.attach_pipeline(make_pipeline(), 'long_short_equity_template')
# リスクモデルのファクターを構成するパイプラインをアルゴリズムに取り込む
algo.attach_pipeline(risk_loading_pipeline(), 'risk_factors')
# アルゴリズムの動作スケジュールを決定（ポートフォリオのリバランスを【週次】実行している）
algo.schedule_function(func=rebalance,
date_rule=algo.date_rules.week_start(),
time_rule=algo.time_rules.market_open(hours=0,
minutes=30),
half_days=True)
# アルゴリズムの動作スケジュールを決定（ポートフォリオの状態を営業日終了時点で評価）
algo.schedule_function(func=record_vars,
date_rule=algo.date_rules.every_day(),
time_rule=algo.time_rules.market_close(),
half_days=True)
def initialize(context):
# SPY capital, EFA fondos, TIP bonos, GSG indince.
context.security_list = symbols('SPY', 'EFA','TIP','GSG')
context.classifier = KNeighborsClassifier(n_neighbors=7, weights='uniform', algorithm='kd_tree') # Usar un clasificador KNN
#Parametros de restriccion
# Attach de pileine datos
attach_pipeline(
make_pipeline(),
'data_pipe'
)
attach_pipeline(
risk_loading_pipeline(),
'risk_pipe'
)
# Rebalance 2 horas de abrir el mercado
algo.schedule_function(
rebalance,
algo.date_rules.month_end(),
algo.time_rules.market_open(hours=2),
)
# guardar al cierre todos los dias
algo.schedule_function(
record_vars,
algo.date_rules.every_day(),
algo.time_rules.market_close()
)
# crea el conjunto seleccionado
algo.attach_pipeline(make_pipeline(), 'pipeline')
def initialize(context):
"""
A core function called automatically once at the beginning of a backtest.
Use this function for initializing state or other bookkeeping.
Parameters
----------
context : AlgorithmContext
An object that can be used to store state that you want to maintain in
your algorithm. context is automatically passed to initialize,
before_trading_start, handle_data, and any functions run via schedule_function.
context provides the portfolio attribute, which can be used to retrieve information
about current positions.
"""
algo.attach_pipeline(make_pipeline(), 'long_short_equity_template')
# Attach the pipeline for the risk model factors that we
# want to neutralize in the optimization step. The 'risk_factors' string is
# used to retrieve the output of the pipeline in before_trading_start below.
algo.attach_pipeline(risk_loading_pipeline(), 'risk_factors')
# Schedule our rebalance function
algo.schedule_function(func=rebalance,
date_rule=algo.date_rules.week_start(),
time_rule=algo.time_rules.market_open(hours=0,
minutes=30),
half_days=True)
# Record our portfolio variables at the end of day
algo.schedule_function(func=record_vars,
date_rule=algo.date_rules.every_day(),
time_rule=algo.time_rules.market_close(),
half_days=True)
def initialize(context):
universe = QTradableStocksUS()
sector = Sector()
momentum = Momentum()
pipe = Pipeline()
pipe.add(sector, 'sector')
# ALPHA
asset_turnover = Fundamentals.assets_turnover.latest.winsorize(.05, .95).zscore()
ciwc = Fundamentals.change_in_working_capital.latest.winsorize(.05, .95).zscore()
alpha = (asset_turnover + ciwc).rank().demean()
pipe.add(alpha, 'alpha')
# BETA
beta = RollingLinearRegressionOfReturns(target=sid(8554),
returns_length=5,
regression_length=252,
mask=alpha.notnull() & Sector().notnull()
).beta
pipe.add(beta, 'beta')
# REGISTER PIPELINES
# ------------------
attach_pipeline(pipe, 'pipe')
attach_pipeline(risk_loading_pipeline(), 'risk_loading_pipeline')
pipe.set_screen(alpha.notnull() & Sector().notnull() & beta.notnull() & universe & (momentum>0))
# SCHEDULE FUNCTIONS
# ------------------
schedule_function(rebalance,
date_rule=date_rules.every_day(),
time_rule=time_rules.market_open(minutes=10))
def initialize(context):
"""
Called once at the start of the algorithm.
"""
set_benchmark(symbol('SPY'))
set_commission(commission.PerTrade(cost=0.0))
set_slippage(slippage.VolumeShareSlippage(volume_limit=1, price_impact=0))
set_long_only()
# set_max_leverage(1.1)
# Rebalance every day, 1 hour after market open.
algo.schedule_function(
rebalance,
algo.date_rules.every_day(),
algo.time_rules.market_open(hours=1),
)
# Record tracking variables at the end of each day.
algo.schedule_function(
record_vars,
algo.date_rules.every_day(),
algo.time_rules.market_close(),
)
# Create our dynamic stock selector.
algo.attach_pipeline(make_pipeline(), 'piotroski')
algo.attach_pipeline(risk_loading_pipeline(), 'risk_factors')
def initialize(context):
algo.schedule_function(rebalance, algo.date_rules.week_start(),
time_rules.market_open())
algo.schedule_function(recordo, date_rules.every_day(),
time_rules.market_close())
algo.attach_pipeline(make_pipeline(), 'my_pipeline')
algo.attach_pipeline(risk_loading_pipeline(), 'risk_factors')
def initialize(context):
# DEFINE UNIVERSE
# ------------------
universe = QTradableStocksUS()
# ALPHA GENERATION
# ----------------
# COMPUTE Z SCORES: ASSET TURNOVER AND CHANGE IN WORKING CAPITAL
# BOTH ARE FUNDAMENTAL VALUE MEASURES
asset_turnover = Fundamentals.assets_turnover.latest.winsorize(
.05, .95).zscore()
ciwc = Fundamentals.change_in_working_capital.latest.winsorize(
.05, .95).zscore()
# ALPHA COMBINATION
# -----------------
# ASSIGN EVERY ASSET AN ALPHA RANK AND CENTER VALUES AT 0 (DEMEAN).
alpha = (asset_turnover + 2 * ciwc).rank().demean()
# BETA DEFINITION
beta = 0.66 * RollingLinearRegressionOfReturns(
target=sid(8554),
returns_length=5,
regression_length=252,
mask=alpha.notnull() & Sector().notnull()).beta + 0.33 * 1.0
# CREATE AND REGISTER PIPELINE
#-------------------------------
# COMPUTES COMBINES ALPHA AND SECTOR CODE FOR UNIVERSE OF STOCKS TO BE USED IN OPTIMIZATION
pipe = Pipeline(
columns={
'alpha': alpha,
'sector': Sector(),
'beta': beta,
},
# RETURN ONLY "NOT NULL" VALUES IN OUR PIPELINE
screen=alpha.notnull() & Sector().notnull() & beta.notnull()
& universe,
)
# LOAD ALL PIPELINES
algo.attach_pipeline(pipe, 'pipe')
algo.attach_pipeline(risk_loading_pipeline(), 'risk_loading_pipeline')
# SCHEDULE FUNCTIONS
#--------------------
algo.schedule_function(
do_portfolio_construction,
date_rule=algo.date_rules.week_start(),
time_rule=algo.time_rules.market_open(minutes=10),
half_days=False,
)
def initialize(context):
algo.attach_pipeline(make_pipeline(), 'long_short_equity_template')
algo.attach_pipeline(risk_loading_pipeline(), 'risk_factors')
algo.schedule_function(func=rebalance,
date_rule=algo.date_rules.week_start(),
time_rule=algo.time_rules.market_open(hours=0,
minutes=30),
half_days=True)
algo.schedule_function(func=record_vars,
date_rule=algo.date_rules.every_day(),
time_rule=algo.time_rules.market_close(),
half_days=True)
def initialize(context):
# Constraint parameters
context.max_leverage = 1.0
context.max_short_pos_size = 0.03
context.max_long_pos_size = 0.03
context.max_turnover = 0.95
# Attach data pipelines
attach_pipeline(make_pipeline(), 'data_pipe')
attach_pipeline(risk_loading_pipeline(), 'risk_pipe')
# Schedule rebalance function
schedule_function(rebalance, date_rules.week_start(),
time_rules.market_open())
def initialize(context):
algo.attach_pipeline(make_pipeline(), 'long_short_equity_template')
algo.attach_pipeline(risk_loading_pipeline(), 'risk_factors')
algo.schedule_function(func=rebalance,
date_rule=algo.date_rules.week_start(),
time_rule=algo.time_rules.market_close(),
half_days=False)
# Record our portfolio variables at the end of day
algo.schedule_function(func=record_vars,
date_rule=algo.date_rules.every_day(),
time_rule=algo.time_rules.market_close(),
half_days=True)
def initialize(context):
context.max_leverage = 1.0
context.max_pos_size = 0.015
context.max_turnover = 0.95
algo.attach_pipeline(
make_pipeline(), 'data_pipe')
algo.attach_pipeline(
risk_loading_pipeline(), 'risk_pipe'
)
algo.schedule_function(
rebalance,
date_rule = algo.date_rules.week_start(),
time_rule = algo.time_rules.market_open()
)
def initialize(context):
attach_pipeline(make_pipeline(), 'long_short_equity_template')
attach_pipeline(risk_loading_pipeline(), 'risk_loading_pipeline')
# Schedule my rebalance function
schedule_function(func=rebalance,
date_rule=date_rules.every_day(),
time_rule=time_rules.market_open(minutes=60),
half_days=True)
# record my portfolio variables at the end of day
schedule_function(func=recording_statements,
date_rule=date_rules.every_day(),
time_rule=time_rules.market_close(),
half_days=True)
set_commission(commission.PerShare(cost=0, min_trade_cost=0))
set_slippage(slippage.FixedSlippage(spread=0))
def initialize(context):
# Restricciones &&
context.max_lever = 1.2
context.max_posTam = 0.025
# Volumen de Negocios
context.max_Volum = 0.85
# adjunta la informacion de pipelines
attach_pipeline(make_pipeline(),'data_pipe')
attach_pipeline(risk_loading_pipeline(),'risk_pipe')
# llama la funcion rebalance cada dia, 15 minutos despues que el mercado abre
schedule_function(
rebalance,
date_rules.week_start(),
time_rules.market_open(minutes=15),
)
def initialize(context):
# 制約パラメータ
context.max_leverage = 1.0
context.max_pos_size = 0.015
context.max_turnover = 0.95
# data pipelines を取り付ける
algo.attach_pipeline(make_pipeline(), 'data_pipe')
algo.attach_pipeline(risk_loading_pipeline(), 'risk_pipe')
# rebalance 関数をスケジュール
algo.schedule_function(
rebalance,
algo.date_rules.week_start(),
algo.time_rules.market_open(),
)
def initialize(context):
context.spy = sid(8554)
attach_pipeline(make_pipeline(), 'long_short_equity_template')
# Attach the risk loading pipeline to our algorithm.
algo.attach_pipeline(risk_loading_pipeline(), 'risk_loading_pipeline')
# Schedule my rebalance function
schedule_function(func=rebalance,
date_rule=date_rules.every_day(),
time_rule=time_rules.market_open(hours=0, minutes=30),
half_days=True)
# record my portfolio variables at the end of day
schedule_function(func=recording_statements,
date_rule=date_rules.every_day(),
time_rule=time_rules.market_close(),
half_days=True)
def initialize(context):
"""
Called once at the start of the algorithm.
"""
#This is for the trend following filter
context.spy = sid(8554)
context.TF_filter = False
context.TF_lookback = 126
# Attach the risk loading pipeline to our algorithm.
algo.attach_pipeline(risk_loading_pipeline(), 'risk_loading_pipeline')
algo.attach_pipeline(make_pipeline(), 'pipe')
# Schedule a function, 'do_portfolio_construction', to run once a week
# ten minutes after market open.
algo.schedule_function(
do_portfolio_construction,
date_rule=algo.date_rules.week_start(),
time_rule=algo.time_rules.market_open(minutes=MINUTES_AFTER_OPEN_TO_TRADE),
half_days=False,
)
def initialize(context):
algo.attach_pipeline(make_pipeline(), 'long_short_equity_template')
# Attach the pipeline for the risk model factors that we
# want to neutralize in the optimization step. The 'risk_factors' string is
# used to retrieve the output of the pipeline in before_trading_start below.
algo.attach_pipeline(risk_loading_pipeline(), 'risk_factors')
# Schedule our rebalance function
algo.schedule_function(func=rebalance,
date_rule=algo.date_rules.every_day(),
time_rule=algo.time_rules.market_open(hours=0, minutes=30),
half_days=True)
# Record our portfolio variables at the end of day
algo.schedule_function(func=record_vars,
date_rule=algo.date_rules.every_day(),
time_rule=algo.time_rules.market_close(),
half_days=True)
def initialize(context):
attach_pipeline(risk_loading_pipeline(), 'risk_loading_pipeline')
attach_pipeline(beta_pipeline(), 'beta_pipeline')
attach_pipeline(factor_pipeline(), 'factor_pipeline')
# Schedule my rebalance function
schedule_function(func=rebalance,
date_rule=date_rules.every_day(),
time_rule=time_rules.market_open(minutes=60),
half_days=True)
# record my portfolio variables at the end of day
schedule_function(func=recording_statements,
date_rule=date_rules.every_day(),
time_rule=time_rules.market_close(),
half_days=True)
context.init = True
context.combined_alpha = pd.Series()
def initialize(context):
context.max_pos_size = 0.01
context.max_turnover = 0.95
context.holdings = 10
context.profit_taking = 20
context.max_loss = -20
context.max_leverage = 1
set_slippage(slippage.FixedSlippage(spread=0.00))
schedule_function(monthly_rebalance, date_rules.month_end(),
time_rules.market_open(hours=5)) #5 is 8%
pipe = Pipeline()
attach_pipeline(pipe, 'ranked_stocks')
attach_pipeline(risk_loading_pipeline(), 'risk_pipe')
market_cap_filter = market_cap()
stocks = market_cap_filter.top(3000)
total_filter = (stocks)
pipe.set_screen(total_filter)
m_factor1 = momentum_factor_1()
pipe.add(m_factor1, 'factor_1')
m_factor2 = momentum_factor_2()
pipe.add(m_factor2, 'factor_2')
m_factor_3 = stddev_factor()
pipe.add(m_factor_3, 'factor_3')
m_factor1_rank = m_factor1.rank(mask=total_filter, ascending=False)
m_factor2_rank = m_factor2.rank(mask=total_filter, ascending=False)
m_factor_3_rank = m_factor_3.rank(mask=total_filter, ascending=True)
pipe.add(m_factor1_rank, 'f1_rank')
pipe.add(m_factor2_rank, 'f2_rank')
pipe.add(m_factor_3_rank, 'f3_rank')
#should put weighted adding instead
combo_raw = (m_factor1_rank + m_factor2_rank + m_factor_3_rank) / 3
pipe.add(combo_raw, 'combo_raw')
pipe.add(combo_raw.rank(mask=total_filter), 'combo_rank')
def initialize(context):
# Agregar tuberia
algo.attach_pipeline(make_pipeline(), 'long_short_equity_template')
# Agregar tuberia de riesgo
algo.attach_pipeline(risk_loading_pipeline(), 'risk_factors')
# Agregar función programada de rebalanceo
algo.schedule_function(func=rebalance,
date_rule=algo.date_rules.week_start(),
time_rule=algo.time_rules.market_open(hours=0,
minutes=30),
half_days=True)
# Agregar función programada de registro de variables
algo.schedule_function(func=record_vars,
date_rule=algo.date_rules.every_day(),
time_rule=algo.time_rules.market_close(),
half_days=True)
def initialize(context):
set_slippage(slippage.FixedSlippage(spread=0.05))
algo.attach_pipeline(make_pipeline(), 'pipeline')
algo.attach_pipeline(risk_loading_pipeline(), 'risk_factors')
#Schedule Functions
schedule_function(trade, date_rules.week_start(2),
time_rules.market_close(minutes=30))
schedule_function(trade_bonds, date_rules.month_end(),
time_rules.market_close(minutes=20))
#This is for the trend following filter
context.spy = sid(8554)
context.TF_filter = False
context.TF_lookback = 126
#Set number of securities to buy and bonds fund (when we are out of stocks)
context.Target_securities_to_buy = 20.0
context.bonds = sid(23870)
def initialize(context):
"""
Called once at the start of the algorithm.
"""
set_slippage(slippage.FixedSlippage(spread=0.00))
set_commission(commission.PerShare(cost=0, min_trade_cost=0))
schedule_function(
rebalance,
TRADE_FREQ,
time_rules.market_open(minutes=1),
)
# Record tracking variables at the end of each day.
schedule_function(
record_vars,
date_rules.every_day(),
time_rules.market_close(),
)
# Set up universe, alphas and ML pipline
context.universe = QTradableStocksUS()
# if you are using IsAnnouncedAcqTarget, uncomment the next line
# context.universe &= IsAnnouncedAcqTarget()
ml_pipeline = make_ml_pipeline(
context.universe,
n_forward_days=PRED_N_FORWARD_DAYS,
window_length=ML_TRAINING_WINDOW,
)
# Create our dynamic stock selector.
attach_pipeline(ml_pipeline, 'alpha_model')
# Add the risk pipeline
attach_pipeline(risk_loading_pipeline(), 'risk_factors')
context.past_predictions = {}
context.hold_out_accuracy = 0
context.hold_out_log_loss = 0
context.hold_out_returns_spread_bps = 0
def initialize(context):
# Rebalance every day, after market close
algo.schedule_function(rebalance, algo.date_rules.every_day(),
algo.time_rules.market_close(hours=1))
if NO_COST:
set_commission(commission.PerShare(cost=0, min_trade_cost=0))
set_slippage(
slippage.FixedBasisPointsSlippage(basis_points=0,
volume_limit=0.1))
# Record tracking variables at the end of each day.
algo.schedule_function(
record_vars,
algo.date_rules.every_day(),
algo.time_rules.market_close(),
)
# Create our dynamic stock selector.
algo.attach_pipeline(make_pipeline(), 'pipeline')
algo.attach_pipeline(risk_loading_pipeline(), 'risk_loading_pipeline')
def initialize(context):
# Rebalance every day, 1 hour after market open.
algo.schedule_function(
rebalance,
algo.date_rules.every_day(),
algo.time_rules.market_open(hours=1),
)
# contrains to the contest
context.max_posTam = 0.045
context.max_lever = 0.97
# Record tracking variables at the end of each day.
algo.schedule_function(
record_vars,
algo.date_rules.every_day(),
algo.time_rules.market_close(),
)
# Create our dynamic stock selector.
algo.attach_pipeline(risk_loading_pipeline(), 'risk_loading_pipeline')
algo.attach_pipeline(make_pipeline(), 'pipeline')
Parameters
----------
context : AlgorithmContext
An object that can be used to store state that you want to maintain in
your algorithm. context is automatically passed to initialize,
before_trading_start, handle_data, and any functions run via schedule_function.
context provides the portfolio attribute, which can be used to retrieve information
about current positions.
"""
algo.attach_pipeline(make_pipeline(), 'long_short_equity_template')
# Attach the pipeline for the risk model factors that we
# want to neutralize in the optimization step. The 'risk_factors' string is
# used to retrieve the output of the pipeline in before_trading_start below.
algo.attach_pipeline(risk_loading_pipeline(), 'risk_factors')
# Schedule our rebalance function
algo.schedule_function(func=rebalance,
date_rule=algo.date_rules.month_start(days_offset = 3),
time_rule=algo.time_rules.market_close(hours=0, minutes=30),
half_days=True)
# Record our portfolio variables at the end of day
algo.schedule_function(func=record_vars,
date_rule=algo.date_rules.every_day(),
time_rule=algo.time_rules.market_close(),
half_days=True)
def make_pipeline():
"""
