This algo is a trend follow for major asset class ETFs. Since beta can be high when the stock is in clear up-trend of down-trend, this algo may not be suitable for the contest. I am sharing it because it may be useful for individual accounts. For that reason, the leverage is 0.9 to be safe.

Clone Algorithm

6565

Loading...

There was an error loading this backtest.

Backtest from
to
with
initial capital

Cumulative performance:

Algorithm
Benchmark

Custom data:

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 |

##################################################################### # Trend following algo # Naoki Nagai, 2015 ##################################################################### # This is a trend following algo for varieties of uncorrelated assets. # Entry signal: Regression line slope exceeds + or - 1% per day and cross the regression line # Profit take : 1.96 standard deviation (95% bollinger band) # Stop loss : Trailing stop with percentage = regression line slope * look back period from numpy import isnan, matrix, array, zeros, empty, sqrt, round, ones, dot, append, mean, cov, transpose, linspace import numpy as np import talib import pandas as pd import scipy.optimize import operator from pytz import timezone from zipline.utils.tradingcalendar import get_early_closes import statsmodels.api as sm import statsmodels.tsa.stattools as ts # Initialization def initialize(context): set_symbol_lookup_date('2015-01-01') context.lookback = 252/2 context.maxlever = 0.9 # Always hold 10% cash context.multiple = 5.0 # 1% of annual return translate to what weight? e.g. 5% context.profittake = 1.96 # 95% bollinger band load_symbols(context) context.weights = dict.fromkeys(context.secs, 0) context.stopprice = dict.fromkeys(context.secs, None) schedule_function(trail_stop, date_rules.every_day(), time_rules.market_open(minutes=10)) schedule_function(regression, date_rules.every_day(), time_rules.market_open(minutes=28)) schedule_function(trade, date_rules.every_day(), time_rules.market_open(minutes=30)) # Calculate the slopes for different assetes def regression(context, data): prices = history(context.lookback, '1d', 'open_price') X=range(len(prices)) # Add column of ones so we get intercept A=sm.add_constant(X) for s in context.secs: if s not in data: continue # Price movement sd = prices[s].std() # Price points to run regression Y = prices[s].values # If all empty, skip if isnan(Y).any(): continue # Run regression y = ax + b results = sm.OLS(Y,A).fit() (b, a) =results.params # Normalized slope slope = a / b * 252.0 # Daily return regression * 1 year # Currently how far away from regression line? delta = Y - (dot(a,X) + b) # Don't trade if the slope is near flat slope_min = 0.252 # At least %7 growth per year to trade # Current gain if trading gain = get_gain(context, s) * 100 # Long but slope turns down, then exit if context.weights[s] > 0 and slope < 0: context.weights[s] = 0 loggain('v %+2d%% Slope turn bull %3s - %s' %(gain, s.symbol, s.security_name),gain) # Short but slope turns upward, then exit if context.weights[s] < 0 and 0 < slope: context.weights[s] = 0 loggain('^ %+2d%% Slope turn bear %3s - %s' %(gain, s.symbol, s.security_name),gain) # Trend is up if slope > slope_min: # Price crosses the regression line if delta[-1] > 0 and delta[-2] < 0 and context.weights[s] == 0: context.stopprice[s] = None context.weights[s] = slope loggain('/ Long a = %+.2f%% %3s - %s' %(slope*100, s.symbol, s.security_name),gain) # Profit take, reaches the top of 95% bollinger band if delta[-1] > context.profittake * sd and context.weights[s] > 0: context.weights[s] = 0 loggain('//%+2d%% Long exit %3s - %s'%(gain, s.symbol, s.security_name) ,gain) # Trend is down if slope < -slope_min: # Price crosses the regression line if delta[-1] < 0 and delta[-2] > 0 and context.weights[s] == 0: context.stopprice[s] = None context.weights[s] = slope loggain('\ Short a = %+.2f%% %3s - %s' %(slope*100, s.symbol, s.security_name)) # Profit take, reaches the top of 95% bollinger band if delta[-1] < - context.profittake * sd and context.weights[s] < 0: context.weights[s] = 0 loggain('\\%+2d%% Short exit %3s - %s' %(gain, s.symbol, s.security_name),gain) return context.weights def get_gain(context, s): if s in context.portfolio.positions: cost = context.portfolio.positions[s].cost_basis amount = context.portfolio.positions[s].amount price = context.portfolio.positions[s].last_sale_price if amount > 0: gain = price/cost - 1 if amount < 0: gain = 1 - price/cost else: gain = 0 return gain def trade(context, data): w = context.weights record(leverage = context.account.leverage) record(equities = sum(w[s] for s in context.equities)) record(fixedincome = sum(w[s] for s in context.fixedincome )) record(alternative = sum(w[s] for s in context.alternative)) record(cash = max(0,context.portfolio.cash) / context.portfolio.portfolio_value) no_positions = 0 for s in context.secs: if w[s] != 0: no_positions += 1 for s in context.secs: if s in data and s not in get_open_orders(): if w[s] == 0: order_target_percent(s, 0) if w[s] > 0: order_target_percent(s, min(w[s] * context.multiple, context.maxlever)/no_positions) if w[s] < 0: order_target_percent(s, max(w[s] * context.multiple, -context.maxlever)/no_positions) def trail_stop(context, data): for s in context.secs: if s not in data: continue price = data[s].mavg(3) gain = get_gain(context, s) * 100 # Stop loss percentage is the return over the lookback period stoploss = abs(context.weights[s] * context.lookback / 252) + 1 # percent change per period if context.weights[s] > 0: if context.stopprice[s] < 0: context.stopprice[s] = price / stoploss else: context.stopprice[s] = max(price / stoploss, context.stopprice[s]) if price < context.stopprice[s] : loggain('x %+2d%% Long stop loss %3s - %s' %(gain, s.symbol, s.security_name,),gain) context.weights[s] = 0 order_target_percent(s,0) elif context.weights[s] < 0: if context.stopprice[s] < 0: context.stopprice[s] = price * stoploss else: context.stopprice[s] = min(price * stoploss, context.stopprice[s]) if price > context.stopprice[s]: loggain('x %+2d%% Short stop loss %3s - %s' %(gain, s.symbol, s.security_name,),gain) context.weights[s] = 0 order_target_percent(s,0) else: context.stopprice[s] = None #record(stoploss = context.stopprice[s]) def handle_data(context, data): exchange_time = pd.Timestamp(get_datetime()).tz_convert('US/Eastern') if exchange_time.minute % 5 == 0: # Check trailing stop every 5 minutes trail_stop(context,data) def loggain(text, gain=0): # Loss settle is WARN and gain settle is INFO if gain < 0: log.warn(text) else: log.info(text) def load_symbols(context) : context.equities = symbols( # Equity 'DIA', # Dow 'QQQ', # NASDAQ ) context.fixedincome = symbols( # Fixed income 'LQD', # Corporate bond 'HYG', # High yield ) context.alternative = symbols( 'USO', # Oil 'GLD', # Gold 'VNQ', # US Real Estate 'RWX', # Dow Jones® Global ex-U.S. Select Real Estate Securities Index 'UNG', # Natual gas 'DBA', # Agriculture ) context.secs = context.equities + context.fixedincome + context.alternative

We have migrated this algorithm to work with a new version of the Quantopian API. The code is different than the original version, but the investment rationale of the algorithm has not changed. We've put everything you need to know here on one page.