[hard]41. First Missing Positive

本文介绍了一种在未排序整数数组中找到第一个缺失正整数的算法,该算法运行时间为O(n),并使用常量空间。通过将非正数设置为特殊值并遍历数组来标记出现过的正整数位置,最终确定第一个未被标记的位置即为答案。

41. First Missing Positive

Given an unsorted integer array, find the first missing positive integer.

For example,
Given [1,2,0] return 3,
and [3,4,-1,1] return 2.

Your algorithm should run in O(n) time and uses constant space.


挺简单的,先把所有非正数的绝对值设为无限大

然后,从头枚举,若该数的绝对值小于vector大小,将其绝对值位的数设为负

枚举,第一个非负位的下标为答案

class Solution {
public:
    int firstMissingPositive(vector<int>& nums) {
        nums.push_back(-1);
        for (int i = 0; i < nums.size(); ++ i)
            if (nums[i] <= 0)
                nums[i] = nums.size();

        for (int i = 0; i < nums.size(); ++ i) {
            int x = (nums[i] > 0) ? (nums[i]) : (-nums[i]);

            if (x >= nums.size())
                continue;
            if (nums[x] > 0)
                nums[x] *= -1;
        }

        for (int i = 1; i < nums.size(); ++ i)
            if(nums[i] > 0)
                return i;

        return (int)nums.size();
    }
};


Quickstart Note The data files used in the quickstart guide are updated from time to time, which means that the adjusted close changes and with it the close (and the other components). That means that the actual output may be different to what was put in the documentation at the time of writing. Using the platform Let’s run through a series of examples (from almost an empty one to a fully fledged strategy) but not without before roughly explaining 2 basic concepts when working with backtrader Lines Data Feeds, Indicators and Strategies have lines. A line is a succession of points that when joined together form this line. When talking about the markets, a Data Feed has usually the following set of points per day: Open, High, Low, Close, Volume, OpenInterest The series of “Open”s along time is a Line. And therefore a Data Feed has usually 6 lines. If we also consider “DateTime” (which is the actual reference for a single point), we could count 7 lines. Index 0 Approach When accessing the values in a line, the current value is accessed with index: 0 And the “last” output value is accessed with -1. This in line with Python conventions for iterables (and a line can be iterated and is therefore an iterable) where index -1 is used to access the “last” item of the iterable/array. In our case is the last output value what’s getting accessed. As such and being index 0 right after -1, it is used to access the current moment in line. With that in mind and if we imagine a Strategy featuring a Simple Moving average created during initialization: self.sma = SimpleMovingAverage(.....) The easiest and simplest way to access the current value of this moving average: av = self.sma[0] There is no need to know how many bars/minutes/days/months have been processed, because “0” uniquely identifies the current instant. Following pythonic tradition, the “last” output value is accessed using -1: previous_value = self.sma[-1] Of course earlier output values can be accessed with -2, -3, … From 0 to 100: the samples Basic Setup Let’s get running. from __future__ import (absolute_import, division, print_function, unicode_literals) import backtrader as bt if __name__ == '__main__': cerebro = bt.Cerebro() print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue()) cerebro.run() print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue()) After the execution the output is: Starting Portfolio Value: 10000.00 Final Portfolio Value: 10000.00 In this example: backtrader was imported The Cerebro engine was instantiated The resulting cerebro instance was told to run (loop over data) And the resulting outcome was printed out Although it doesn’t seem much, let’s point out something explicitly shown: The Cerebro engine has created a broker instance in the background The instance already has some cash to start with This behind the scenes broker instantiation is a constant trait in the platform to simplify the life of the user. If no broker is set by the user, a default one is put in place. And 10K monetary units is a usual value with some brokers to begin with. Setting the Cash In the world of finance, for sure only “losers” start with 10k. Let’s change the cash and run the example again. from __future__ import (absolute_import, division, print_function, unicode_literals) import backtrader as bt if __name__ == '__main__': cerebro = bt.Cerebro() cerebro.broker.setcash(100000.0) print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue()) cerebro.run() print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue()) After the execution the output is: Starting Portfolio Value: 1000000.00 Final Portfolio Value: 1000000.00 Mission accomplished. Let’s move to tempestuous waters. Adding a Data Feed Having cash is fun, but the purpose behind all this is to let an automated strategy multiply the cash without moving a finger by operating on an asset which we see as a Data Feed Ergo … No Data Feed -> No Fun. Let’s add one to the ever growing example. from __future__ import (absolute_import, division, print_function, unicode_literals) import datetime # For datetime objects import os.path # To manage paths import sys # To find out the script name (in argv[0]) # Import the backtrader platform import backtrader as bt if __name__ == '__main__': # Create a cerebro entity cerebro = bt.Cerebro() # Datas are in a subfolder of the samples. Need to find where the script is # because it could have been called from anywhere modpath = os.path.dirname(os.path.abspath(sys.argv[0])) datapath = os.path.join(modpath, '../../datas/orcl-1995-2014.txt') # Create a Data Feed data = bt.feeds.YahooFinanceCSVData( dataname=datapath, # Do not pass values before this date fromdate=datetime.datetime(2000, 1, 1), # Do not pass values after this date todate=datetime.datetime(2000, 12, 31), reverse=False) # Add the Data Feed to Cerebro cerebro.adddata(data) # Set our desired cash start cerebro.broker.setcash(100000.0) # Print out the starting conditions print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue()) # Run over everything cerebro.run() # Print out the final result print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue()) After the execution the output is: Starting Portfolio Value: 1000000.00 Final Portfolio Value: 1000000.00 The amount of boilerplate has grown slightly, because we added: Finding out where our example script is to be able to locate the sample Data Feed file Having datetime objects to filter on which data from the Data Feed we will be operating Aside from that, the Data Feed is created and added to cerebro. The output has not changed and it would be a miracle if it had. Note Yahoo Online sends the CSV data in date descending order, which is not the standard convention. The reversed=True prameter takes into account that the CSV data in the file has already been reversed and has the standard expected date ascending order. Our First Strategy The cash is in the broker and the Data Feed is there. It seems like risky business is just around the corner. Let’s put a Strategy into the equation and print the “Close” price of each day (bar). DataSeries (the underlying class in Data Feeds) objects have aliases to access the well known OHLC (Open High Low Close) daily values. This should ease up the creation of our printing logic. from __future__ import (absolute_import, division, print_function, unicode_literals) import datetime # For datetime objects import os.path # To manage paths import sys # To find out the script name (in argv[0]) # Import the backtrader platform import backtrader as bt # Create a Stratey class TestStrategy(bt.Strategy): def log(self, txt, dt=None): ''' Logging function for this strategy''' dt = dt or self.datas[0].datetime.date(0) print('%s, %s' % (dt.isoformat(), txt)) def __init__(self): # Keep a reference to the "close" line in the data[0] dataseries self.dataclose = self.datas[0].close def next(self): # Simply log the closing price of the series from the reference self.log('Close, %.2f' % self.dataclose[0]) if __name__ == '__main__': # Create a cerebro entity cerebro = bt.Cerebro() # Add a strategy cerebro.addstrategy(TestStrategy) # Datas are in a subfolder of the samples. Need to find where the script is # because it could have been called from anywhere modpath = os.path.dirname(os.path.abspath(sys.argv[0])) datapath = os.path.join(modpath, '../../datas/orcl-1995-2014.txt') # Create a Data Feed data = bt.feeds.YahooFinanceCSVData( dataname=datapath, # Do not pass values before this date fromdate=datetime.datetime(2000, 1, 1), # Do not pass values before this date todate=datetime.datetime(2000, 12, 31), # Do not pass values after this date reverse=False) # Add the Data Feed to Cerebro cerebro.adddata(data) # Set our desired cash start cerebro.broker.setcash(100000.0) # Print out the starting conditions print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue()) # Run over everything cerebro.run() # Print out the final result print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue()) After the execution the output is: Starting Portfolio Value: 100000.00 2000-01-03T00:00:00, Close, 27.85 2000-01-04T00:00:00, Close, 25.39 2000-01-05T00:00:00, Close, 24.05 ... ... ... 2000-12-26T00:00:00, Close, 29.17 2000-12-27T00:00:00, Close, 28.94 2000-12-28T00:00:00, Close, 29.29 2000-12-29T00:00:00, Close, 27.41 Final Portfolio Value: 100000.00 Someone said the stockmarket was risky business, but it doesn’t seem so. Let’s explain some of the magic: Upon init being called the strategy already has a list of datas that are present in the platform This is a standard Python list and datas can be accessed in the order they were inserted. The first data in the list self.datas[0] is the default data for trading operations and to keep all strategy elements synchronized (it’s the system clock) self.dataclose = self.datas[0].close keeps a reference to the close line. Only one level of indirection is later needed to access the close values. The strategy next method will be called on each bar of the system clock (self.datas[0]). This is true until other things come into play like indicators, which need some bars to start producing an output. More on that later. Adding some Logic to the Strategy Let’s try some crazy idea we had by looking at some charts If the price has been falling 3 sessions in a row … BUY BUY BUY!!! from __future__ import (absolute_import, division, print_function, unicode_literals) import datetime # For datetime objects import os.path # To manage paths import sys # To find out the script name (in argv[0]) # Import the backtrader platform import backtrader as bt # Create a Stratey class TestStrategy(bt.Strategy): def log(self, txt, dt=None): ''' Logging function fot this strategy''' dt = dt or self.datas[0].datetime.date(0) print('%s, %s' % (dt.isoformat(), txt)) def __init__(self): # Keep a reference to the "close" line in the data[0] dataseries self.dataclose = self.datas[0].close def next(self): # Simply log the closing price of the series from the reference self.log('Close, %.2f' % self.dataclose[0]) if self.dataclose[0] < self.dataclose[-1]: # current close less than previous close if self.dataclose[-1] < self.dataclose[-2]: # previous close less than the previous close # BUY, BUY, BUY!!! (with all possible default parameters) self.log('BUY CREATE, %.2f' % self.dataclose[0]) self.buy() if __name__ == '__main__': # Create a cerebro entity cerebro = bt.Cerebro() # Add a strategy cerebro.addstrategy(TestStrategy) # Datas are in a subfolder of the samples. Need to find where the script is # because it could have been called from anywhere modpath = os.path.dirname(os.path.abspath(sys.argv[0])) datapath = os.path.join(modpath, '../../datas/orcl-1995-2014.txt') # Create a Data Feed data = bt.feeds.YahooFinanceCSVData( dataname=datapath, # Do not pass values before this date fromdate=datetime.datetime(2000, 1, 1), # Do not pass values before this date todate=datetime.datetime(2000, 12, 31), # Do not pass values after this date reverse=False) # Add the Data Feed to Cerebro cerebro.adddata(data) # Set our desired cash start cerebro.broker.setcash(100000.0) # Print out the starting conditions print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue()) # Run over everything cerebro.run() # Print out the final result print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue()) After the execution the output is: Starting Portfolio Value: 100000.00 2000-01-03, Close, 27.85 2000-01-04, Close, 25.39 2000-01-05, Close, 24.05 2000-01-05, BUY CREATE, 24.05 2000-01-06, Close, 22.63 2000-01-06, BUY CREATE, 22.63 2000-01-07, Close, 24.37 ... ... ... 2000-12-20, BUY CREATE, 26.88 2000-12-21, Close, 27.82 2000-12-22, Close, 30.06 2000-12-26, Close, 29.17 2000-12-27, Close, 28.94 2000-12-27, BUY CREATE, 28.94 2000-12-28, Close, 29.29 2000-12-29, Close, 27.41 Final Portfolio Value: 99725.08 Several “BUY” creation orders were issued, our porftolio value was decremented. A couple of important things are clearly missing. The order was created but it is unknown if it was executed, when and at what price. The next example will build upon that by listening to notifications of order status. The curious reader may ask how many shares are being bought, what asset is being bought and how are orders being executed. Where possible (and in this case it is) the platform fills in the gaps: self.datas[0] (the main data aka system clock) is the target asset if no other one is specified The stake is provided behind the scenes by a position sizer which uses a fixed stake, being the default “1”. It will be modified in a later example The order is executed “At Market”. The broker (shown in previous examples) executes this using the opening price of the next bar, because that’s the 1st tick after the current under examination bar. The order is executed so far without any commission (more on that later) Do not only buy … but SELL After knowing how to enter the market (long), an “exit concept” is needed and also understanding whether the strategy is in the market. Luckily a Strategy object offers access to a position attribute for the default data feed Methods buy and sell return the created (not yet executed) order Changes in orders’ status will be notified to the strategy via a notify method The “exit concept” will be an easy one: Exit after 5 bars (on the 6th bar) have elapsed for good or for worse Please notice that there is no “time” or “timeframe” implied: number of bars. The bars can represent 1 minute, 1 hour, 1 day, 1 week or any other time period. Although we know the data source is a daily one, the strategy makes no assumption about that. Additionally and to simplify: Do only allow a Buy order if not yet in the market Note The next method gets no “bar index” passed and therefore it seems obscure how to understand when 5 bars may have elapsed, but this has been modeled in pythonic way: call len on an object and it will tell you the length of its lines. Just write down (save in a variable) at which length in an operation took place and see if the current length is 5 bars away. from __future__ import (absolute_import, division, print_function, unicode_literals) import datetime # For datetime objects import os.path # To manage paths import sys # To find out the script name (in argv[0]) # Import the backtrader platform import backtrader as bt # Create a Stratey class TestStrategy(bt.Strategy): def log(self, txt, dt=None): ''' Logging function fot this strategy''' dt = dt or self.datas[0].datetime.date(0) print('%s, %s' % (dt.isoformat(), txt)) def __init__(self): # Keep a reference to the "close" line in the data[0] dataseries self.dataclose = self.datas[0].close # To keep track of pending orders self.order = None def notify_order(self, order): if order.status in [order.Submitted, order.Accepted]: # Buy/Sell order submitted/accepted to/by broker - Nothing to do return # Check if an order has been completed # Attention: broker could reject order if not enough cash if order.status in [order.Completed]: if order.isbuy(): self.log('BUY EXECUTED, %.2f' % order.executed.price) elif order.issell(): self.log('SELL EXECUTED, %.2f' % order.executed.price) self.bar_executed = len(self) elif order.status in [order.Canceled, order.Margin, order.Rejected]: self.log('Order Canceled/Margin/Rejected') # Write down: no pending order self.order = None def next(self): # Simply log the closing price of the series from the reference self.log('Close, %.2f' % self.dataclose[0]) # Check if an order is pending ... if yes, we cannot send a 2nd one if self.order: return # Check if we are in the market if not self.position: # Not yet ... we MIGHT BUY if ... if self.dataclose[0] < self.dataclose[-1]: # current close less than previous close if self.dataclose[-1] < self.dataclose[-2]: # previous close less than the previous close # BUY, BUY, BUY!!! (with default parameters) self.log('BUY CREATE, %.2f' % self.dataclose[0]) # Keep track of the created order to avoid a 2nd order self.order = self.buy() else: # Already in the market ... we might sell if len(self) >= (self.bar_executed + 5): # SELL, SELL, SELL!!! (with all possible default parameters) self.log('SELL CREATE, %.2f' % self.dataclose[0]) # Keep track of the created order to avoid a 2nd order self.order = self.sell() if __name__ == '__main__': # Create a cerebro entity cerebro = bt.Cerebro() # Add a strategy cerebro.addstrategy(TestStrategy) # Datas are in a subfolder of the samples. Need to find where the script is # because it could have been called from anywhere modpath = os.path.dirname(os.path.abspath(sys.argv[0])) datapath = os.path.join(modpath, '../../datas/orcl-1995-2014.txt') # Create a Data Feed data = bt.feeds.YahooFinanceCSVData( dataname=datapath, # Do not pass values before this date fromdate=datetime.datetime(2000, 1, 1), # Do not pass values before this date todate=datetime.datetime(2000, 12, 31), # Do not pass values after this date reverse=False) # Add the Data Feed to Cerebro cerebro.adddata(data) # Set our desired cash start cerebro.broker.setcash(100000.0) # Print out the starting conditions print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue()) # Run over everything cerebro.run() # Print out the final result print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue()) After the execution the output is: Starting Portfolio Value: 100000.00 2000-01-03T00:00:00, Close, 27.85 2000-01-04T00:00:00, Close, 25.39 2000-01-05T00:00:00, Close, 24.05 2000-01-05T00:00:00, BUY CREATE, 24.05 2000-01-06T00:00:00, BUY EXECUTED, 23.61 2000-01-06T00:00:00, Close, 22.63 2000-01-07T00:00:00, Close, 24.37 2000-01-10T00:00:00, Close, 27.29 2000-01-11T00:00:00, Close, 26.49 2000-01-12T00:00:00, Close, 24.90 2000-01-13T00:00:00, Close, 24.77 2000-01-13T00:00:00, SELL CREATE, 24.77 2000-01-14T00:00:00, SELL EXECUTED, 25.70 2000-01-14T00:00:00, Close, 25.18 ... ... ... 2000-12-15T00:00:00, SELL CREATE, 26.93 2000-12-18T00:00:00, SELL EXECUTED, 28.29 2000-12-18T00:00:00, Close, 30.18 2000-12-19T00:00:00, Close, 28.88 2000-12-20T00:00:00, Close, 26.88 2000-12-20T00:00:00, BUY CREATE, 26.88 2000-12-21T00:00:00, BUY EXECUTED, 26.23 2000-12-21T00:00:00, Close, 27.82 2000-12-22T00:00:00, Close, 30.06 2000-12-26T00:00:00, Close, 29.17 2000-12-27T00:00:00, Close, 28.94 2000-12-28T00:00:00, Close, 29.29 2000-12-29T00:00:00, Close, 27.41 2000-12-29T00:00:00, SELL CREATE, 27.41 Final Portfolio Value: 100018.53 Blistering Barnacles!!! The system made money … something must be wrong The broker says: Show me the money! And the money is called “commission”. Let’s add a reasonable 0.1% commision rate per operation (both for buying and selling … yes the broker is avid …) A single line will suffice for it: # 0.1% ... divide by 100 to remove the % cerebro.broker.setcommission(commission=0.001) Being experienced with the platform we want to see the profit or loss after a buy/sell cycle, with and without commission. from __future__ import (absolute_import, division, print_function, unicode_literals) import datetime # For datetime objects import os.path # To manage paths import sys # To find out the script name (in argv[0]) # Import the backtrader platform import backtrader as bt # Create a Stratey class TestStrategy(bt.Strategy): def log(self, txt, dt=None): ''' Logging function fot this strategy''' dt = dt or self.datas[0].datetime.date(0) print('%s, %s' % (dt.isoformat(), txt)) def __init__(self): # Keep a reference to the "close" line in the data[0] dataseries self.dataclose = self.datas[0].close # To keep track of pending orders and buy price/commission self.order = None self.buyprice = None self.buycomm = None def notify_order(self, order): if order.status in [order.Submitted, order.Accepted]: # Buy/Sell order submitted/accepted to/by broker - Nothing to do return # Check if an order has been completed # Attention: broker could reject order if not enough cash if order.status in [order.Completed]: if order.isbuy(): self.log( 'BUY EXECUTED, Price: %.2f, Cost: %.2f, Comm %.2f' % (order.executed.price, order.executed.value, order.executed.comm)) self.buyprice = order.executed.price self.buycomm = order.executed.comm else: # Sell self.log('SELL EXECUTED, Price: %.2f, Cost: %.2f, Comm %.2f' % (order.executed.price, order.executed.value, order.executed.comm)) self.bar_executed = len(self) elif order.status in [order.Canceled, order.Margin, order.Rejected]: self.log('Order Canceled/Margin/Rejected') self.order = None def notify_trade(self, trade): if not trade.isclosed: return self.log('OPERATION PROFIT, GROSS %.2f, NET %.2f' % (trade.pnl, trade.pnlcomm)) def next(self): # Simply log the closing price of the series from the reference self.log('Close, %.2f' % self.dataclose[0]) # Check if an order is pending ... if yes, we cannot send a 2nd one if self.order: return # Check if we are in the market if not self.position: # Not yet ... we MIGHT BUY if ... if self.dataclose[0] < self.dataclose[-1]: # current close less than previous close if self.dataclose[-1] < self.dataclose[-2]: # previous close less than the previous close # BUY, BUY, BUY!!! (with default parameters) self.log('BUY CREATE, %.2f' % self.dataclose[0]) # Keep track of the created order to avoid a 2nd order self.order = self.buy() else: # Already in the market ... we might sell if len(self) >= (self.bar_executed + 5): # SELL, SELL, SELL!!! (with all possible default parameters) self.log('SELL CREATE, %.2f' % self.dataclose[0]) # Keep track of the created order to avoid a 2nd order self.order = self.sell() if __name__ == '__main__': # Create a cerebro entity cerebro = bt.Cerebro() # Add a strategy cerebro.addstrategy(TestStrategy) # Datas are in a subfolder of the samples. Need to find where the script is # because it could have been called from anywhere modpath = os.path.dirname(os.path.abspath(sys.argv[0])) datapath = os.path.join(modpath, '../../datas/orcl-1995-2014.txt') # Create a Data Feed data = bt.feeds.YahooFinanceCSVData( dataname=datapath, # Do not pass values before this date fromdate=datetime.datetime(2000, 1, 1), # Do not pass values before this date todate=datetime.datetime(2000, 12, 31), # Do not pass values after this date reverse=False) # Add the Data Feed to Cerebro cerebro.adddata(data) # Set our desired cash start cerebro.broker.setcash(100000.0) # Set the commission - 0.1% ... divide by 100 to remove the % cerebro.broker.setcommission(commission=0.001) # Print out the starting conditions print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue()) # Run over everything cerebro.run() # Print out the final result print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue()) After the execution the output is: Starting Portfolio Value: 100000.00 2000-01-03T00:00:00, Close, 27.85 2000-01-04T00:00:00, Close, 25.39 2000-01-05T00:00:00, Close, 24.05 2000-01-05T00:00:00, BUY CREATE, 24.05 2000-01-06T00:00:00, BUY EXECUTED, Price: 23.61, Cost: 23.61, Commission 0.02 2000-01-06T00:00:00, Close, 22.63 2000-01-07T00:00:00, Close, 24.37 2000-01-10T00:00:00, Close, 27.29 2000-01-11T00:00:00, Close, 26.49 2000-01-12T00:00:00, Close, 24.90 2000-01-13T00:00:00, Close, 24.77 2000-01-13T00:00:00, SELL CREATE, 24.77 2000-01-14T00:00:00, SELL EXECUTED, Price: 25.70, Cost: 25.70, Commission 0.03 2000-01-14T00:00:00, OPERATION PROFIT, GROSS 2.09, NET 2.04 2000-01-14T00:00:00, Close, 25.18 ... ... ... 2000-12-15T00:00:00, SELL CREATE, 26.93 2000-12-18T00:00:00, SELL EXECUTED, Price: 28.29, Cost: 28.29, Commission 0.03 2000-12-18T00:00:00, OPERATION PROFIT, GROSS -0.06, NET -0.12 2000-12-18T00:00:00, Close, 30.18 2000-12-19T00:00:00, Close, 28.88 2000-12-20T00:00:00, Close, 26.88 2000-12-20T00:00:00, BUY CREATE, 26.88 2000-12-21T00:00:00, BUY EXECUTED, Price: 26.23, Cost: 26.23, Commission 0.03 2000-12-21T00:00:00, Close, 27.82 2000-12-22T00:00:00, Close, 30.06 2000-12-26T00:00:00, Close, 29.17 2000-12-27T00:00:00, Close, 28.94 2000-12-28T00:00:00, Close, 29.29 2000-12-29T00:00:00, Close, 27.41 2000-12-29T00:00:00, SELL CREATE, 27.41 Final Portfolio Value: 100016.98 God Save the Queen!!! The system still made money. Before moving on, let’s notice something by filtering the “OPERATION PROFIT” lines: 2000-01-14T00:00:00, OPERATION PROFIT, GROSS 2.09, NET 2.04 2000-02-07T00:00:00, OPERATION PROFIT, GROSS 3.68, NET 3.63 2000-02-28T00:00:00, OPERATION PROFIT, GROSS 4.48, NET 4.42 2000-03-13T00:00:00, OPERATION PROFIT, GROSS 3.48, NET 3.41 2000-03-22T00:00:00, OPERATION PROFIT, GROSS -0.41, NET -0.49 2000-04-07T00:00:00, OPERATION PROFIT, GROSS 2.45, NET 2.37 2000-04-20T00:00:00, OPERATION PROFIT, GROSS -1.95, NET -2.02 2000-05-02T00:00:00, OPERATION PROFIT, GROSS 5.46, NET 5.39 2000-05-11T00:00:00, OPERATION PROFIT, GROSS -3.74, NET -3.81 2000-05-30T00:00:00, OPERATION PROFIT, GROSS -1.46, NET -1.53 2000-07-05T00:00:00, OPERATION PROFIT, GROSS -1.62, NET -1.69 2000-07-14T00:00:00, OPERATION PROFIT, GROSS 2.08, NET 2.01 2000-07-28T00:00:00, OPERATION PROFIT, GROSS 0.14, NET 0.07 2000-08-08T00:00:00, OPERATION PROFIT, GROSS 4.36, NET 4.29 2000-08-21T00:00:00, OPERATION PROFIT, GROSS 1.03, NET 0.95 2000-09-15T00:00:00, OPERATION PROFIT, GROSS -4.26, NET -4.34 2000-09-27T00:00:00, OPERATION PROFIT, GROSS 1.29, NET 1.22 2000-10-13T00:00:00, OPERATION PROFIT, GROSS -2.98, NET -3.04 2000-10-26T00:00:00, OPERATION PROFIT, GROSS 3.01, NET 2.95 2000-11-06T00:00:00, OPERATION PROFIT, GROSS -3.59, NET -3.65 2000-11-16T00:00:00, OPERATION PROFIT, GROSS 1.28, NET 1.23 2000-12-01T00:00:00, OPERATION PROFIT, GROSS 2.59, NET 2.54 2000-12-18T00:00:00, OPERATION PROFIT, GROSS -0.06, NET -0.12 Adding up the “NET” profits the final figure is: 15.83 But the system said the following at the end: 2000-12-29T00:00:00, SELL CREATE, 27.41 Final Portfolio Value: 100016.98 And obviously 15.83 is not 16.98. There is no error whatsoever. The “NET” profit of 15.83 is already cash in the bag. Unfortunately (or fortunately to better understand the platform) there is an open position on the last day of the Data Feed. Even if a SELL operation has been sent … IT HAS NOT YET BEEN EXECUTED. The “Final Portfolio Value” calculated by the broker takes into account the “Close” price on 2000-12-29. The actual execution price would have been set on the next trading day which happened to be 2001-01-02. Extending the Data Feed” to take into account this day the output is: 2001-01-02T00:00:00, SELL EXECUTED, Price: 27.87, Cost: 27.87, Commission 0.03 2001-01-02T00:00:00, OPERATION PROFIT, GROSS 1.64, NET 1.59 2001-01-02T00:00:00, Close, 24.87 2001-01-02T00:00:00, BUY CREATE, 24.87 Final Portfolio Value: 100017.41 Now adding the previous NET profit to the completed operation’s net profit: 15.83 + 1.59 = 17.42 Which (discarding rounding errors in the “print” statements) is the extra Portfolio above the initial 100000 monetary units the strategy started with. Customizing the Strategy: Parameters It would a bit unpractical to hardcode some of the values in the strategy and have no chance to change them easily. Parameters come in handy to help. Definition of parameters is easy and looks like: params = (('myparam', 27), ('exitbars', 5),) Being this a standard Python tuple with some tuples inside it, the following may look more appealling to some: params = ( ('myparam', 27), ('exitbars', 5), ) With either formatting parametrization of the strategy is allowed when adding the strategy to the Cerebro engine: # Add a strategy cerebro.addstrategy(TestStrategy, myparam=20, exitbars=7) Note The setsizing method below is deprecated. This content is kept here for anyone looking at old samples of the sources. The sources have been update to use: cerebro.addsizer(bt.sizers.FixedSize, stake=10)`` Please read the section about sizers Using the parameters in the strategy is easy, as they are stored in a “params” attribute. If we for example want to set the stake fix, we can pass the stake parameter to the position sizer like this durint init: # Set the sizer stake from the params self.sizer.setsizing(self.params.stake) We could have also called buy and sell with a stake parameter and self.params.stake as the value. The logic to exit gets modified: # Already in the market ... we might sell if len(self) >= (self.bar_executed + self.params.exitbars): With all this in mind the example evolves to look like: from __future__ import (absolute_import, division, print_function, unicode_literals) import datetime # For datetime objects import os.path # To manage paths import sys # To find out the script name (in argv[0]) # Import the backtrader platform import backtrader as bt # Create a Stratey class TestStrategy(bt.Strategy): params = ( ('exitbars', 5), ) def log(self, txt, dt=None): ''' Logging function fot this strategy''' dt = dt or self.datas[0].datetime.date(0) print('%s, %s' % (dt.isoformat(), txt)) def __init__(self): # Keep a reference to the "close" line in the data[0] dataseries self.dataclose = self.datas[0].close # To keep track of pending orders and buy price/commission self.order = None self.buyprice = None self.buycomm = None def notify_order(self, order): if order.status in [order.Submitted, order.Accepted]: # Buy/Sell order submitted/accepted to/by broker - Nothing to do return # Check if an order has been completed # Attention: broker could reject order if not enough cash if order.status in [order.Completed]: if order.isbuy(): self.log( 'BUY EXECUTED, Price: %.2f, Cost: %.2f, Comm %.2f' % (order.executed.price, order.executed.value, order.executed.comm)) self.buyprice = order.executed.price self.buycomm = order.executed.comm else: # Sell self.log('SELL EXECUTED, Price: %.2f, Cost: %.2f, Comm %.2f' % (order.executed.price, order.executed.value, order.executed.comm)) self.bar_executed = len(self) elif order.status in [order.Canceled, order.Margin, order.Rejected]: self.log('Order Canceled/Margin/Rejected') self.order = None def notify_trade(self, trade): if not trade.isclosed: return self.log('OPERATION PROFIT, GROSS %.2f, NET %.2f' % (trade.pnl, trade.pnlcomm)) def next(self): # Simply log the closing price of the series from the reference self.log('Close, %.2f' % self.dataclose[0]) # Check if an order is pending ... if yes, we cannot send a 2nd one if self.order: return # Check if we are in the market if not self.position: # Not yet ... we MIGHT BUY if ... if self.dataclose[0] < self.dataclose[-1]: # current close less than previous close if self.dataclose[-1] < self.dataclose[-2]: # previous close less than the previous close # BUY, BUY, BUY!!! (with default parameters) self.log('BUY CREATE, %.2f' % self.dataclose[0]) # Keep track of the created order to avoid a 2nd order self.order = self.buy() else: # Already in the market ... we might sell if len(self) >= (self.bar_executed + self.params.exitbars): # SELL, SELL, SELL!!! (with all possible default parameters) self.log('SELL CREATE, %.2f' % self.dataclose[0]) # Keep track of the created order to avoid a 2nd order self.order = self.sell() if __name__ == '__main__': # Create a cerebro entity cerebro = bt.Cerebro() # Add a strategy cerebro.addstrategy(TestStrategy) # Datas are in a subfolder of the samples. Need to find where the script is # because it could have been called from anywhere modpath = os.path.dirname(os.path.abspath(sys.argv[0])) datapath = os.path.join(modpath, '../../datas/orcl-1995-2014.txt') # Create a Data Feed data = bt.feeds.YahooFinanceCSVData( dataname=datapath, # Do not pass values before this date fromdate=datetime.datetime(2000, 1, 1), # Do not pass values before this date todate=datetime.datetime(2000, 12, 31), # Do not pass values after this date reverse=False) # Add the Data Feed to Cerebro cerebro.adddata(data) # Set our desired cash start cerebro.broker.setcash(100000.0) # Add a FixedSize sizer according to the stake cerebro.addsizer(bt.sizers.FixedSize, stake=10) # Set the commission - 0.1% ... divide by 100 to remove the % cerebro.broker.setcommission(commission=0.001) # Print out the starting conditions print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue()) # Run over everything cerebro.run() # Print out the final result print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue()) After the execution the output is: Starting Portfolio Value: 100000.00 2000-01-03T00:00:00, Close, 27.85 2000-01-04T00:00:00, Close, 25.39 2000-01-05T00:00:00, Close, 24.05 2000-01-05T00:00:00, BUY CREATE, 24.05 2000-01-06T00:00:00, BUY EXECUTED, Size 10, Price: 23.61, Cost: 236.10, Commission 0.24 2000-01-06T00:00:00, Close, 22.63 ... ... ... 2000-12-20T00:00:00, BUY CREATE, 26.88 2000-12-21T00:00:00, BUY EXECUTED, Size 10, Price: 26.23, Cost: 262.30, Commission 0.26 2000-12-21T00:00:00, Close, 27.82 2000-12-22T00:00:00, Close, 30.06 2000-12-26T00:00:00, Close, 29.17 2000-12-27T00:00:00, Close, 28.94 2000-12-28T00:00:00, Close, 29.29 2000-12-29T00:00:00, Close, 27.41 2000-12-29T00:00:00, SELL CREATE, 27.41 Final Portfolio Value: 100169.80 In order to see the difference, the print outputs have also been extended to show the execution size. Having multiplied the stake by 10, the obvious has happened: the profit and loss has been multiplied by 10. Instead of 16.98, the surplus is now 169.80 Adding an indicator Having heard of indicators, the next thing anyone would add to the strategy is one of them. For sure they must be much better than a simple “3 lower closes” strategy. Inspired in one of the examples from PyAlgoTrade a strategy using a Simple Moving Average. Buy “AtMarket” if the close is greater than the Average If in the market, sell if the close is smaller than the Average Only 1 active operation is allowed in the market Most of the existing code can be kept in place. Let’s add the average during init and keep a reference to it: self.sma = bt.indicators.MovingAverageSimple(self.datas[0], period=self.params.maperiod) And of course the logic to enter and exit the market will rely on the Average values. Look in the code for the logic. Note The starting cash will be 1000 monetary units to be in line with the PyAlgoTrade example and no commission will be applied from __future__ import (absolute_import, division, print_function, unicode_literals) import datetime # For datetime objects import os.path # To manage paths import sys # To find out the script name (in argv[0]) # Import the backtrader platform import backtrader as bt # Create a Stratey class TestStrategy(bt.Strategy): params = ( ('maperiod', 15), ) def log(self, txt, dt=None): ''' Logging function fot this strategy''' dt = dt or self.datas[0].datetime.date(0) print('%s, %s' % (dt.isoformat(), txt)) def __init__(self): # Keep a reference to the "close" line in the data[0] dataseries self.dataclose = self.datas[0].close # To keep track of pending orders and buy price/commission self.order = None self.buyprice = None self.buycomm = None # Add a MovingAverageSimple indicator self.sma = bt.indicators.SimpleMovingAverage( self.datas[0], period=self.params.maperiod) def notify_order(self, order): if order.status in [order.Submitted, order.Accepted]: # Buy/Sell order submitted/accepted to/by broker - Nothing to do return # Check if an order has been completed # Attention: broker could reject order if not enough cash if order.status in [order.Completed]: if order.isbuy(): self.log( 'BUY EXECUTED, Price: %.2f, Cost: %.2f, Comm %.2f' % (order.executed.price, order.executed.value, order.executed.comm)) self.buyprice = order.executed.price self.buycomm = order.executed.comm else: # Sell self.log('SELL EXECUTED, Price: %.2f, Cost: %.2f, Comm %.2f' % (order.executed.price, order.executed.value, order.executed.comm)) self.bar_executed = len(self) elif order.status in [order.Canceled, order.Margin, order.Rejected]: self.log('Order Canceled/Margin/Rejected') self.order = None def notify_trade(self, trade): if not trade.isclosed: return self.log('OPERATION PROFIT, GROSS %.2f, NET %.2f' % (trade.pnl, trade.pnlcomm)) def next(self): # Simply log the closing price of the series from the reference self.log('Close, %.2f' % self.dataclose[0]) # Check if an order is pending ... if yes, we cannot send a 2nd one if self.order: return # Check if we are in the market if not self.position: # Not yet ... we MIGHT BUY if ... if self.dataclose[0] > self.sma[0]: # BUY, BUY, BUY!!! (with all possible default parameters) self.log('BUY CREATE, %.2f' % self.dataclose[0]) # Keep track of the created order to avoid a 2nd order self.order = self.buy() else: if self.dataclose[0] < self.sma[0]: # SELL, SELL, SELL!!! (with all possible default parameters) self.log('SELL CREATE, %.2f' % self.dataclose[0]) # Keep track of the created order to avoid a 2nd order self.order = self.sell() if __name__ == '__main__': # Create a cerebro entity cerebro = bt.Cerebro() # Add a strategy cerebro.addstrategy(TestStrategy) # Datas are in a subfolder of the samples. Need to find where the script is # because it could have been called from anywhere modpath = os.path.dirname(os.path.abspath(sys.argv[0])) datapath = os.path.join(modpath, '../../datas/orcl-1995-2014.txt') # Create a Data Feed data = bt.feeds.YahooFinanceCSVData( dataname=datapath, # Do not pass values before this date fromdate=datetime.datetime(2000, 1, 1), # Do not pass values before this date todate=datetime.datetime(2000, 12, 31), # Do not pass values after this date reverse=False) # Add the Data Feed to Cerebro cerebro.adddata(data) # Set our desired cash start cerebro.broker.setcash(1000.0) # Add a FixedSize sizer according to the stake cerebro.addsizer(bt.sizers.FixedSize, stake=10) # Set the commission cerebro.broker.setcommission(commission=0.0) # Print out the starting conditions print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue()) # Run over everything cerebro.run() # Print out the final result print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue()) Now, before skipping to the next section LOOK CAREFULLY to the first date which is shown in the log: It’ no longer 2000-01-03, the first trading day in the year 2K. It’s 2000-01-24 … Who has stolen my cheese? The missing days are not missing. The platform has adapted to the new circumstances: An indicator (SimpleMovingAverage) has been added to the Strategy. This indicator needs X bars to produce an output: in the example: 15 2000-01-24 is the day in which the 15th bar occurs The backtrader platform assumes that the Strategy has the indicator in place for a good reason, to use it in the decision making process. And it makes no sense to try to make decisions if the indicator is not yet ready and producing values. next will be 1st called when all indicators have already reached the minimum needed period to produce a value In the example there is a single indicator, but the strategy could have any number of them. After the execution the output is: Starting Portfolio Value: 1000.00 2000-01-24T00:00:00, Close, 25.55 2000-01-25T00:00:00, Close, 26.61 2000-01-25T00:00:00, BUY CREATE, 26.61 2000-01-26T00:00:00, BUY EXECUTED, Size 10, Price: 26.76, Cost: 267.60, Commission 0.00 2000-01-26T00:00:00, Close, 25.96 2000-01-27T00:00:00, Close, 24.43 2000-01-27T00:00:00, SELL CREATE, 24.43 2000-01-28T00:00:00, SELL EXECUTED, Size 10, Price: 24.28, Cost: 242.80, Commission 0.00 2000-01-28T00:00:00, OPERATION PROFIT, GROSS -24.80, NET -24.80 2000-01-28T00:00:00, Close, 22.34 2000-01-31T00:00:00, Close, 23.55 2000-02-01T00:00:00, Close, 25.46 2000-02-02T00:00:00, Close, 25.61 2000-02-02T00:00:00, BUY CREATE, 25.61 2000-02-03T00:00:00, BUY EXECUTED, Size 10, Price: 26.11, Cost: 261.10, Commission 0.00 ... ... ... 2000-12-20T00:00:00, SELL CREATE, 26.88 2000-12-21T00:00:00, SELL EXECUTED, Size 10, Price: 26.23, Cost: 262.30, Commission 0.00 2000-12-21T00:00:00, OPERATION PROFIT, GROSS -20.60, NET -20.60 2000-12-21T00:00:00, Close, 27.82 2000-12-21T00:00:00, BUY CREATE, 27.82 2000-12-22T00:00:00, BUY EXECUTED, Size 10, Price: 28.65, Cost: 286.50, Commission 0.00 2000-12-22T00:00:00, Close, 30.06 2000-12-26T00:00:00, Close, 29.17 2000-12-27T00:00:00, Close, 28.94 2000-12-28T00:00:00, Close, 29.29 2000-12-29T00:00:00, Close, 27.41 2000-12-29T00:00:00, SELL CREATE, 27.41 Final Portfolio Value: 973.90 In the name of the King!!! A winning system turned into a losing one … and that with no commission. It may well be that simply adding an indicator is not the universal panacea. Note The same logic and data with PyAlgoTrade yields a slightly different result (slightly off). Looking at the entire printout reveals that some operations are not exactly the same. Being the culprit again the usual suspect: rounding. PyAlgoTrade does not round the datafeed values when applying the divided “adjusted close” to the data feed values. The Yahoo Data Feed provided by backtrader rounds the values down to 2 decimals after applying the adjusted close. Upon printing the values everything seems the same, but it’s obvious that sometimes that 5th place decimal plays a role. Rounding down to 2 decimals seems more realistic, because Market Exchanges do only allow a number of decimals per asset (being that 2 decimals usually for stocks) Note The Yahoo Data Feed (starting with version 1.8.11.99 allows to specify if rounding has to happen and how many decimals) Visual Inspection: Plotting A printout or log of the actual whereabouts of the system at each bar-instant is good but humans tend to be visual and therefore it seems right to offer a view of the same whereabouts as chart. Note To plot you need to have matplotlib installed Once again defaults for plotting are there to assist the platform user. Plotting is incredibly a 1 line operation: cerebro.plot() Being the location for sure after cerebro.run() has been called. In order to display the automatic plotting capabilities and a couple of easy customizations, the following will be done: A 2nd MovingAverage (Exponential) will be added. The defaults will plot it (just like the 1st) with the data. A 3rd MovingAverage (Weighted) will be added. Customized to plot in an own plot (even if not sensible) A Stochastic (Slow) will be added. No change to the defaults. A MACD will be added. No change to the defaults. A RSI will be added. No change to the defaults. A MovingAverage (Simple) will be applied to the RSI. No change to the defaults (it will be plotted with the RSI) An AverageTrueRange will be added. Changed defaults to avoid it being plotted. The entire set of additions to the init method of the Strategy: # Indicators for the plotting show bt.indicators.ExponentialMovingAverage(self.datas[0], period=25) bt.indicators.WeightedMovingAverage(self.datas[0], period=25).subplot = True bt.indicators.StochasticSlow(self.datas[0]) bt.indicators.MACDHisto(self.datas[0]) rsi = bt.indicators.RSI(self.datas[0]) bt.indicators.SmoothedMovingAverage(rsi, period=10) bt.indicators.ATR(self.datas[0]).plot = False Note Even if indicators are not explicitly added to a member variable of the strategy (like self.sma = MovingAverageSimple…), they will autoregister with the strategy and will influence the minimum period for next and will be part of the plotting. In the example only RSI is added to a temporary variable rsi with the only intention to create a MovingAverageSmoothed on it. The example now: from __future__ import (absolute_import, division, print_function, unicode_literals) import datetime # For datetime objects import os.path # To manage paths import sys # To find out the script name (in argv[0]) # Import the backtrader platform import backtrader as bt # Create a Stratey class TestStrategy(bt.Strategy): params = ( ('maperiod', 15), ) def log(self, txt, dt=None): ''' Logging function fot this strategy''' dt = dt or self.datas[0].datetime.date(0) print('%s, %s' % (dt.isoformat(), txt)) def __init__(self): # Keep a reference to the "close" line in the data[0] dataseries self.dataclose = self.datas[0].close # To keep track of pending orders and buy price/commission self.order = None self.buyprice = None self.buycomm = None # Add a MovingAverageSimple indicator self.sma = bt.indicators.SimpleMovingAverage( self.datas[0], period=self.params.maperiod) # Indicators for the plotting show bt.indicators.ExponentialMovingAverage(self.datas[0], period=25) bt.indicators.WeightedMovingAverage(self.datas[0], period=25, subplot=True) bt.indicators.StochasticSlow(self.datas[0]) bt.indicators.MACDHisto(self.datas[0]) rsi = bt.indicators.RSI(self.datas[0]) bt.indicators.SmoothedMovingAverage(rsi, period=10) bt.indicators.ATR(self.datas[0], plot=False) def notify_order(self, order): if order.status in [order.Submitted, order.Accepted]: # Buy/Sell order submitted/accepted to/by broker - Nothing to do return # Check if an order has been completed # Attention: broker could reject order if not enough cash if order.status in [order.Completed]: if order.isbuy(): self.log( 'BUY EXECUTED, Price: %.2f, Cost: %.2f, Comm %.2f' % (order.executed.price, order.executed.value, order.executed.comm)) self.buyprice = order.executed.price self.buycomm = order.executed.comm else: # Sell self.log('SELL EXECUTED, Price: %.2f, Cost: %.2f, Comm %.2f' % (order.executed.price, order.executed.value, order.executed.comm)) self.bar_executed = len(self) elif order.status in [order.Canceled, order.Margin, order.Rejected]: self.log('Order Canceled/Margin/Rejected') # Write down: no pending order self.order = None def notify_trade(self, trade): if not trade.isclosed: return self.log('OPERATION PROFIT, GROSS %.2f, NET %.2f' % (trade.pnl, trade.pnlcomm)) def next(self): # Simply log the closing price of the series from the reference self.log('Close, %.2f' % self.dataclose[0]) # Check if an order is pending ... if yes, we cannot send a 2nd one if self.order: return # Check if we are in the market if not self.position: # Not yet ... we MIGHT BUY if ... if self.dataclose[0] > self.sma[0]: # BUY, BUY, BUY!!! (with all possible default parameters) self.log('BUY CREATE, %.2f' % self.dataclose[0]) # Keep track of the created order to avoid a 2nd order self.order = self.buy() else: if self.dataclose[0] < self.sma[0]: # SELL, SELL, SELL!!! (with all possible default parameters) self.log('SELL CREATE, %.2f' % self.dataclose[0]) # Keep track of the created order to avoid a 2nd order self.order = self.sell() if __name__ == '__main__': # Create a cerebro entity cerebro = bt.Cerebro() # Add a strategy cerebro.addstrategy(TestStrategy) # Datas are in a subfolder of the samples. Need to find where the script is # because it could have been called from anywhere modpath = os.path.dirname(os.path.abspath(sys.argv[0])) datapath = os.path.join(modpath, '../../datas/orcl-1995-2014.txt') # Create a Data Feed data = bt.feeds.YahooFinanceCSVData( dataname=datapath, # Do not pass values before this date fromdate=datetime.datetime(2000, 1, 1), # Do not pass values before this date todate=datetime.datetime(2000, 12, 31), # Do not pass values after this date reverse=False) # Add the Data Feed to Cerebro cerebro.adddata(data) # Set our desired cash start cerebro.broker.setcash(1000.0) # Add a FixedSize sizer according to the stake cerebro.addsizer(bt.sizers.FixedSize, stake=10) # Set the commission cerebro.broker.setcommission(commission=0.0) # Print out the starting conditions print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue()) # Run over everything cerebro.run() # Print out the final result print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue()) # Plot the result cerebro.plot() After the execution the output is: Starting Portfolio Value: 1000.00 2000-02-18T00:00:00, Close, 27.61 2000-02-22T00:00:00, Close, 27.97 2000-02-22T00:00:00, BUY CREATE, 27.97 2000-02-23T00:00:00, BUY EXECUTED, Size 10, Price: 28.38, Cost: 283.80, Commission 0.00 2000-02-23T00:00:00, Close, 29.73 ... ... ... 2000-12-21T00:00:00, BUY CREATE, 27.82 2000-12-22T00:00:00, BUY EXECUTED, Size 10, Price: 28.65, Cost: 286.50, Commission 0.00 2000-12-22T00:00:00, Close, 30.06 2000-12-26T00:00:00, Close, 29.17 2000-12-27T00:00:00, Close, 28.94 2000-12-28T00:00:00, Close, 29.29 2000-12-29T00:00:00, Close, 27.41 2000-12-29T00:00:00, SELL CREATE, 27.41 Final Portfolio Value: 981.00 The final result has changed even if the logic hasn’t. This is true but the logic has not been applied to the same number of bars. Note As explained before, the platform will first call next when all indicators are ready to produce a value. In this plotting example (very clear in the chart) the MACD is the last indicator to be fully ready (all 3 lines producing an output). The 1st BUY order is no longer scheduled during Jan 2000 but close to the end of Feb 2000. The chart: image Let’s Optimize Many trading books say each market and each traded stock (or commodity or ..) have different rythms. That there is no such thing as a one size fits all. Before the plotting sample, when the strategy started using an indicator the period default value was 15 bars. It’s a strategy parameter and this can be used in an optimization to change the value of the parameter and see which one better fits the market. Note There is plenty of literature about Optimization and associated pros and cons. But the advice will always point in the same direction: do not overoptimize. If a trading idea is not sound, optimizing may end producing a positive result which is only valid for the backtested dataset. The sample is modified to optimize the period of the Simple Moving Average. For the sake of clarity any output with regards to Buy/Sell orders has been removed The example now: from __future__ import (absolute_import, division, print_function, unicode_literals) import datetime # For datetime objects import os.path # To manage paths import sys # To find out the script name (in argv[0]) # Import the backtrader platform import backtrader as bt # Create a Stratey class TestStrategy(bt.Strategy): params = ( ('maperiod', 15), ('printlog', False), ) def log(self, txt, dt=None, doprint=False): ''' Logging function fot this strategy''' if self.params.printlog or doprint: dt = dt or self.datas[0].datetime.date(0) print('%s, %s' % (dt.isoformat(), txt)) def __init__(self): # Keep a reference to the "close" line in the data[0] dataseries self.dataclose = self.datas[0].close # To keep track of pending orders and buy price/commission self.order = None self.buyprice = None self.buycomm = None # Add a MovingAverageSimple indicator self.sma = bt.indicators.SimpleMovingAverage( self.datas[0], period=self.params.maperiod) def notify_order(self, order): if order.status in [order.Submitted, order.Accepted]: # Buy/Sell order submitted/accepted to/by broker - Nothing to do return # Check if an order has been completed # Attention: broker could reject order if not enough cash if order.status in [order.Completed]: if order.isbuy(): self.log( 'BUY EXECUTED, Price: %.2f, Cost: %.2f, Comm %.2f' % (order.executed.price, order.executed.value, order.executed.comm)) self.buyprice = order.executed.price self.buycomm = order.executed.comm else: # Sell self.log('SELL EXECUTED, Price: %.2f, Cost: %.2f, Comm %.2f' % (order.executed.price, order.executed.value, order.executed.comm)) self.bar_executed = len(self) elif order.status in [order.Canceled, order.Margin, order.Rejected]: self.log('Order Canceled/Margin/Rejected') # Write down: no pending order self.order = None def notify_trade(self, trade): if not trade.isclosed: return self.log('OPERATION PROFIT, GROSS %.2f, NET %.2f' % (trade.pnl, trade.pnlcomm)) def next(self): # Simply log the closing price of the series from the reference self.log('Close, %.2f' % self.dataclose[0]) # Check if an order is pending ... if yes, we cannot send a 2nd one if self.order: return # Check if we are in the market if not self.position: # Not yet ... we MIGHT BUY if ... if self.dataclose[0] > self.sma[0]: # BUY, BUY, BUY!!! (with all possible default parameters) self.log('BUY CREATE, %.2f' % self.dataclose[0]) # Keep track of the created order to avoid a 2nd order self.order = self.buy() else: if self.dataclose[0] < self.sma[0]: # SELL, SELL, SELL!!! (with all possible default parameters) self.log('SELL CREATE, %.2f' % self.dataclose[0]) # Keep track of the created order to avoid a 2nd order self.order = self.sell() def stop(self): self.log('(MA Period %2d) Ending Value %.2f' % (self.params.maperiod, self.broker.getvalue()), doprint=True) if __name__ == '__main__': # Create a cerebro entity cerebro = bt.Cerebro() # Add a strategy strats = cerebro.optstrategy( TestStrategy, maperiod=range(10, 31)) # Datas are in a subfolder of the samples. Need to find where the script is # because it could have been called from anywhere modpath = os.path.dirname(os.path.abspath(sys.argv[0])) datapath = os.path.join(modpath, '../../datas/orcl-1995-2014.txt') # Create a Data Feed data = bt.feeds.YahooFinanceCSVData( dataname=datapath, # Do not pass values before this date fromdate=datetime.datetime(2000, 1, 1), # Do not pass values before this date todate=datetime.datetime(2000, 12, 31), # Do not pass values after this date reverse=False) # Add the Data Feed to Cerebro cerebro.adddata(data) # Set our desired cash start cerebro.broker.setcash(1000.0) # Add a FixedSize sizer according to the stake cerebro.addsizer(bt.sizers.FixedSize, stake=10) # Set the commission cerebro.broker.setcommission(commission=0.0) # Run over everything cerebro.run(maxcpus=1) Instead of calling addstrategy to add a stratey class to Cerebro, the call is made to optstrategy. And instead of passing a value a range of values is passed. One of the “Strategy” hooks is added, the stop method, which will be called when the data has been exhausted and backtesting is over. It’s used to print the final net value of the portfolio in the broker (it was done in Cerebro previously) The system will execute the strategy for each value of the range. The following will be output: 2000-12-29, (MA Period 10) Ending Value 880.30 2000-12-29, (MA Period 11) Ending Value 880.00 2000-12-29, (MA Period 12) Ending Value 830.30 2000-12-29, (MA Period 13) Ending Value 893.90 2000-12-29, (MA Period 14) Ending Value 896.90 2000-12-29, (MA Period 15) Ending Value 973.90 2000-12-29, (MA Period 16) Ending Value 959.40 2000-12-29, (MA Period 17) Ending Value 949.80 2000-12-29, (MA Period 18) Ending Value 1011.90 2000-12-29, (MA Period 19) Ending Value 1041.90 2000-12-29, (MA Period 20) Ending Value 1078.00 2000-12-29, (MA Period 21) Ending Value 1058.80 2000-12-29, (MA Period 22) Ending Value 1061.50 2000-12-29, (MA Period 23) Ending Value 1023.00 2000-12-29, (MA Period 24) Ending Value 1020.10 2000-12-29, (MA Period 25) Ending Value 1013.30 2000-12-29, (MA Period 26) Ending Value 998.30 2000-12-29, (MA Period 27) Ending Value 982.20 2000-12-29, (MA Period 28) Ending Value 975.70 2000-12-29, (MA Period 29) Ending Value 983.30 2000-12-29, (MA Period 30) Ending Value 979.80 Results: For periods below 18 the strategy (commissionless) loses money. For periods between 18 and 26 (both included) the strategy makes money. Above 26 money is lost again. And the winning period for this strategy and the given data set is: 20 bars, which wins 78.00 units over 1000 $/€ (a 7.8%) Note The extra indicators from the plotting example have been removed and the start of operations is only influenced by the Simple Moving Average which is being optimized. Hence the slightly different results for period 15 Conclusion The incremental samples have shown how to go from a barebones script to a fully working trading system which even plots the results and can be optimized. A lot more can be done to try to improve the chances of winning: Self defined Indicators Creating an indicator is easy (and even plotting them is easy) Sizers Money Management is for many the key to success Order Types (limit, stop, stoplimit) Some others To ensure all the above items can be fully utilized the documentation provides an insight into them (and other topics) Look in the table of contents and keep on reading … and developing. Best of luck
07-08
# Ultralytics YOLO 🚀, AGPL-3.0 license import json import random from collections import defaultdict from itertools import repeat from multiprocessing.pool import ThreadPool from pathlib import Path import cv2 import numpy as np import torch from PIL import Image from torch.utils.data import ConcatDataset from ultralytics.utils import LOCAL_RANK, NUM_THREADS, TQDM, colorstr from ultralytics.utils.ops import resample_segments from ultralytics.utils.torch_utils import TORCHVISION_0_18 from .augment import ( Compose, Format, LetterBox, RandomLoadText, classify_augmentations, classify_transforms, v8_transforms, ) from .base import BaseDataset from .utils import ( HELP_URL, LOGGER, get_hash, img2label_paths, load_dataset_cache_file, save_dataset_cache_file, verify_image, verify_image_label, ) # Ultralytics dataset *.cache version, >= 1.0.0 for YOLOv8 DATASET_CACHE_VERSION = "1.0.3" # 修复点1: 添加完整的Instances类定义(包含convert_bbox和denormalize方法) class Instances: """Instances class for handling bounding boxes, segments, and keypoints in object detection.""" def __init__(self, bboxes, segments=None, keypoints=None, bbox_format="xywh", normalized=True): """ Initialize Instances. Args: bboxes (np.ndarray): Bounding boxes array segments (np.ndarray, optional): Segmentation masks keypoints (np.ndarray, optional): Keypoints bbox_format (str): Bounding box format ('xywh', 'xyxy', etc.) normalized (bool): Whether coordinates are normalized """ self.bboxes = bboxes self.segments = segments self.keypoints = keypoints self.bbox_format = bbox_format self.normalized = normalized self.cls = None # 添加cls属性占位 def __len__(self): """Return the number of instances.""" return len(self.bboxes) @classmethod def empty(cls): """Return an empty Instances object.""" return cls(np.zeros((0, 4), dtype=np.float32)) @classmethod def cat(cls, instances_list): """Concatenate multiple Instances objects into one.""" bboxes = np.concatenate([inst.bboxes for inst in instances_list], axis=0) segments = np.concatenate([inst.segments for inst in instances_list], axis=0) if instances_list[0].segments is not None else None keypoints = np.concatenate([inst.keypoints for inst in instances_list], axis=0) if instances_list[0].keypoints is not None else None return cls(bboxes, segments, keypoints, bbox_format=instances_list[0].bbox_format, normalized=instances_list[0].normalized) def convert_bbox(self, format): """Convert bounding box format. Args: format (str): Target format, either 'xyxy' or 'xywh'. """ if self.bbox_format == format: return if self.bbox_format == "xywh" and format == "xyxy": # Convert from xywh to xyxy x, y, w, h = self.bboxes.T xyxy = np.array([x - w/2, y - h/2, x + w/2, y + h/2]).T self.bboxes = xyxy self.bbox_format = "xyxy" elif self.bbox_format == "xyxy" and format == "xywh": # Convert from xyxy to xywh x1, y1, x2, y2 = self.bboxes.T xywh = np.array([(x1+x2)/2, (y1+y2)/2, x2-x1, y2-y1]).T self.bboxes = xywh self.bbox_format = "xywh" else: raise ValueError(f"Conversion from {self.bbox_format} to {format} not supported") # 添加缺失的denormalize方法 def denormalize(self, w, h): """ Denormalize bounding boxes from normalized coordinates to pixel coordinates. Args: w (int): Image width h (int): Image height """ if not self.normalized: return if self.bboxes is not None and len(self.bboxes) > 0: if self.bbox_format == "xywh": # Denormalize xywh format self.bboxes[:, 0] *= w self.bboxes[:, 1] *= h self.bboxes[:, 2] *= w self.bboxes[:, 3] *= h elif self.bbox_format == "xyxy": # Denormalize xyxy format self.bboxes[:, [0, 2]] *= w self.bboxes[:, [1, 3]] *= h # 处理segments(如果存在) if self.segments is not None and len(self.segments) > 0: # segments shape: (n, num_points, 2) self.segments[..., 0] *= w self.segments[..., 1] *= h # 处理keypoints(如果存在) if self.keypoints is not None and len(self.keypoints) > 0: # keypoints shape: (n, num_keypoints, 2 or 3) self.keypoints[..., 0] *= w self.keypoints[..., 1] *= h self.normalized = False class Mosaic: """Mosaic data augmentation for object detection datasets. This class combines 4 images into a single mosaic image, adjusting labels accordingly. """ def __init__(self, dataset, imgsz=640, p=0.5, border=[-320, -320]): """ Initialize Mosaic augmentation. Args: dataset (YOLODataset): The dataset object imgsz (int): Output image size (height and width) p (float): Probability of applying mosaic augmentation border (list): Border values for random center placement """ self.dataset = dataset self.imgsz = imgsz self.p = p self.border = border self.mosaic_border = [-imgsz // 2, -imgsz // 2] def __call__(self, data): """Apply mosaic augmentation to a batch of data.""" # Only apply mosaic with given probability if random.random() > self.p: return data # Check if data contains necessary components if 'img' not in data or 'instances' not in data: return data # Get current image and instances img = data['img'] instances = data['instances'] h0, w0 = img.shape[:2] # original height and width # Create mosaic image mosaic_img = np.full((self.imgsz * 2, self.imgsz * 2, img.shape[2]), 114, dtype=np.uint8) # Random center placement yc, xc = [int(random.uniform(-x, 2 * self.imgsz + x)) for x in self.mosaic_border] # Get 3 additional random indices indices = [random.randint(0, len(self.dataset) - 1) for _ in range(3)] mosaic_instances = [] # Place 4 images in mosaic for i, index in enumerate([0] + indices): if i == 0: # current image img_i, instances_i = img, instances else: # Get other image and instances from dataset data_i = self.dataset[index] img_i = data_i['img'] instances_i = data_i['instances'] # Resize image r = self.imgsz / max(img_i.shape[:2]) img_i = cv2.resize(img_i, (int(w0 * r), int(h0 * r)), interpolation=cv2.INTER_LINEAR) h, w = img_i.shape[:2] # Place image in mosaic if i == 0: # top left x1a, y1a, x2a, y2a = max(xc - w, 0), max(yc - h, 0), xc, yc x1b, y1b, x2b, y2b = w - (x2a - x1a), h - (y2a - y1a), w, h elif i == 1: # top right x1a, y1a, x2a, y2a = xc, max(yc - h, 0), min(xc + w, self.imgsz * 2), yc x1b, y1b, x2b, y2b = 0, h - (y2a - y1a), min(w, x2a - x1a), h elif i == 2: # bottom left x1a, y1a, x2a, y2a = max(xc - w, 0), yc, xc, min(self.imgsz * 2, yc + h) x1b, y1b, x2b, y2b = w - (x2a - x1a), 0, w, min(h, y2a - y1a) elif i == 3: # bottom right x1a, y1a, x2a, y2a = xc, yc, min(xc + w, self.imgsz * 2), min(self.imgsz * 2, yc + h) x1b, y1b, x2b, y2b = 0, 0, min(w, x2a - x1a), min(h, y2a - y1a) # Place image segment in mosaic mosaic_img[y1a:y2a, x1a:x2a] = img_i[y1b:y2b, x1b:x2b] padw, padh = x1a - x1b, y1a - y1b # Adjust instances if they exist if instances_i is not None and len(instances_i) > 0: # 确保使用xyxy格式进行处理 if instances_i.bbox_format != "xyxy": instances_i.convert_bbox("xyxy") # 修复点2: 使用copy()代替clone()处理NumPy数组 bboxes_copy = instances_i.bboxes.copy() segments_copy = instances_i.segments.copy() if instances_i.segments is not None else None keypoints_copy = instances_i.keypoints.copy() if instances_i.keypoints is not None else None # Create a copy of instances to avoid modifying original new_instances = Instances( bboxes_copy, segments_copy, keypoints_copy, bbox_format=instances_i.bbox_format, normalized=instances_i.normalized ) # Adjust bboxes if new_instances.bboxes is not None and len(new_instances.bboxes) > 0: bboxes = new_instances.bboxes if new_instances.normalized: # Convert normalized coordinates to pixels bboxes[:, [0, 2]] = bboxes[:, [0, 2]] * w bboxes[:, [1, 3]] = bboxes[:, [1, 3]] * h # Adjust coordinates bboxes[:, [0, 2]] = bboxes[:, [0, 2]] * r + padw bboxes[:, [1, 3]] = bboxes[:, [1, 3]] * r + padh # Convert back to normalized coordinates bboxes[:, [0, 2]] = bboxes[:, [0, 2]] / (self.imgsz * 2) bboxes[:, [1, 3]] = bboxes[:, [1, 3]] / (self.imgsz * 2) # Filter boxes that are completely outside the mosaic valid = ( (bboxes[:, 0] < 1) & (bboxes[:, 1] < 1) & (bboxes[:, 2] > 0) & (bboxes[:, 3] > 0)) new_instances.bboxes = bboxes[valid] # Adjust class labels if present if new_instances.cls is not None: new_instances.cls = new_instances.cls[valid] # Add adjusted instances to mosaic mosaic_instances.append(new_instances) # Combine all instances if mosaic_instances: updated_instances = Instances.cat(mosaic_instances) else: updated_instances = Instances.empty() # Update data dictionary data['img'] = mosaic_img data['instances'] = updated_instances data['mosaic_border'] = self.mosaic_border return data class YOLODataset(BaseDataset): """ Dataset class for loading object detection and/or segmentation labels in YOLO format. Args: data (dict, optional): A dataset YAML dictionary. Defaults to None. task (str): An explicit arg to point current task, Defaults to 'detect'. Returns: (torch.utils.data.Dataset): A PyTorch dataset object that can be used for training an object detection model. """ def __init__(self, *args, data=None, task="detect", **kwargs): """Initializes the YOLODataset with optional configurations for segments and keypoints.""" self.use_segments = task == "segment" self.use_keypoints = task == "pose" self.use_obb = task == "obb" self.data = data self.mosaic_enabled = False # Will be enabled in build_transforms if conditions met assert not (self.use_segments and self.use_keypoints), "Can not use both segments and keypoints." super().__init__(*args, **kwargs) def cache_labels(self, path=Path("./labels.cache")): """ Cache dataset labels, check images and read shapes. Args: path (Path): Path where to save the cache file. Default is Path('./labels.cache'). Returns: (dict): labels. """ x = {"labels": []} nm, nf, ne, nc, msgs = 0, 0, 0, 0, [] # number missing, found, empty, corrupt, messages desc = f"{self.prefix}Scanning {path.parent / path.stem}..." total = len(self.im_files) nkpt, ndim = self.data.get("kpt_shape", (0, 0)) if self.use_keypoints and (nkpt <= 0 or ndim not in {2, 3}): raise ValueError( "'kpt_shape' in data.yaml missing or incorrect. Should be a list with [number of " "keypoints, number of dims (2 for x,y or 3 for x,y,visible)], i.e. 'kpt_shape: [17, 3]'" ) with ThreadPool(NUM_THREADS) as pool: results = pool.imap( func=verify_image_label, iterable=zip( self.im_files, self.label_files, repeat(self.prefix), repeat(self.use_keypoints), repeat(len(self.data["names"])), repeat(nkpt), repeat(ndim), ), ) pbar = TQDM(results, desc=desc, total=total) for im_file, lb, shape, segments, keypoint, nm_f, nf_f, ne_f, nc_f, msg in pbar: nm += nm_f nf += nf_f ne += ne_f nc += nc_f if im_file: x["labels"].append( { "im_file": im_file, "shape": shape, "cls": lb[:, 0:1], # n, 1 "bboxes": lb[:, 1:], # n, 4 "segments": segments, "keypoints": keypoint, "normalized": True, "bbox_format": "xywh", } ) if msg: msgs.append(msg) pbar.desc = f"{desc} {nf} images, {nm + ne} backgrounds, {nc} corrupt" pbar.close() if msgs: LOGGER.info("\n".join(msgs)) if nf == 0: LOGGER.warning(f"{self.prefix}WARNING ⚠️ No labels found in {path}. {HELP_URL}") x["hash"] = get_hash(self.label_files + self.im_files) x["results"] = nf, nm, ne, nc, len(self.im_files) x["msgs"] = msgs # warnings save_dataset_cache_file(self.prefix, path, x, DATASET_CACHE_VERSION) return x def get_labels(self): """Returns dictionary of labels for YOLO training.""" self.label_files = img2label_paths(self.im_files) cache_path = Path(self.label_files[0]).parent.with_suffix(".cache") try: cache, exists = load_dataset_cache_file(cache_path), True # attempt to load a *.cache file assert cache["version"] == DATASET_CACHE_VERSION # matches current version assert cache["hash"] == get_hash(self.label_files + self.im_files) # identical hash except (FileNotFoundError, AssertionError, AttributeError): cache, exists = self.cache_labels(cache_path), False # run cache ops # Display cache nf, nm, ne, nc, n = cache.pop("results") # found, missing, empty, corrupt, total if exists and LOCAL_RANK in {-1, 0}: d = f"Scanning {cache_path}... {nf} images, {nm + ne} backgrounds, {nc} corrupt" TQDM(None, desc=self.prefix + d, total=n, initial=n) # display results if cache["msgs"]: LOGGER.info("\n".join(cache["msgs"])) # display warnings # Read cache [cache.pop(k) for k in ("hash", "version", "msgs")] # remove items labels = cache["labels"] if not labels: LOGGER.warning(f"WARNING ⚠️ No images found in {cache_path}, training may not work correctly. {HELP_URL}") self.im_files = [lb["im_file"] for lb in labels] # update im_files # Check if the dataset is all boxes or all segments lengths = ((len(lb["cls"]), len(lb["bboxes"]), len(lb["segments"])) for lb in labels) len_cls, len_boxes, len_segments = (sum(x) for x in zip(*lengths)) if len_segments and len_boxes != len_segments: LOGGER.warning( f"WARNING ⚠️ Box and segment counts should be equal, but got len(segments) = {len_segments}, " f"len(boxes) = {len_boxes}. To resolve this only boxes will be used and all segments will be removed. " "To avoid this please supply either a detect or segment dataset, not a detect-segment mixed dataset." ) for lb in labels: lb["segments"] = [] if len_cls == 0: LOGGER.warning(f"WARNING ⚠️ No labels found in {cache_path}, training may not work correctly. {HELP_URL}") return labels def build_transforms(self, hyp=None): """Builds and appends transforms to the list.""" if self.augment: # Enable mosaic if specified in hyperparameters self.mosaic_enabled = hyp.mosaic > 0 if self.augment and not self.rect else False hyp.mosaic = hyp.mosaic if self.augment and not self.rect else 0.0 hyp.mixup = hyp.mixup if self.augment and not self.rect else 0.0 # Create transforms list transforms = [] # Add Mosaic transform if enabled if self.mosaic_enabled: transforms.append(Mosaic(self, self.imgsz, p=hyp.mosaic)) # Add other standard transforms transforms.extend(v8_transforms(self, self.imgsz, hyp)) else: transforms = [Compose(LetterBox(new_shape=(self.imgsz, self.imgsz), scaleup=False))] # Add format transform transforms.append( Format( bbox_format="xywh", normalize=True, return_mask=self.use_segments, return_keypoint=self.use_keypoints, return_obb=self.use_obb, batch_idx=True, mask_ratio=hyp.mask_ratio, mask_overlap=hyp.overlap_mask, bgr=hyp.bgr if self.augment else 0.0, # only affect training. ) ) # Return as Compose object return Compose(transforms) def close_mosaic(self, hyp): """Sets mosaic, copy_paste and mixup options to 0.0 and builds transformations.""" hyp.mosaic = 0.0 # set mosaic ratio=0.0 hyp.copy_paste = 0.0 # keep the same behavior as previous v8 close-mosaic hyp.mixup = 0.0 # keep the same behavior as previous v8 close-mosaic self.transforms = self.build_transforms(hyp) def update_labels_info(self, label): """ Custom your label format here. Note: cls is not with bboxes now, classification and semantic segmentation need an independent cls label Can also support classification and semantic segmentation by adding or removing dict keys there. """ bboxes = label.pop("bboxes") segments = label.pop("segments", []) keypoints = label.pop("keypoints", None) bbox_format = label.pop("bbox_format") normalized = label.pop("normalized") # NOTE: do NOT resample oriented boxes segment_resamples = 100 if self.use_obb else 1000 if len(segments) > 0: # list[np.array(1000, 2)] * num_samples # (N, 1000, 2) segments = np.stack(resample_segments(segments, n=segment_resamples), axis=0) else: segments = np.zeros((0, segment_resamples, 2), dtype=np.float32) label["instances"] = Instances(bboxes, segments, keypoints, bbox_format=bbox_format, normalized=normalized) return label @staticmethod def collate_fn(batch): """Collates data samples into batches.""" new_batch = {} keys = batch[0].keys() values = list(zip(*[list(b.values()) for b in batch])) for i, k in enumerate(keys): value = values[i] if k == "img": value = torch.stack(value, 0) if k in {"masks", "keypoints", "bboxes", "cls", "segments", "obb"}: value = torch.cat(value, 0) new_batch[k] = value new_batch["batch_idx"] = list(new_batch["batch_idx"]) for i in range(len(new_batch["batch_idx"])): new_batch["batch_idx"][i] += i # add target image index for build_targets() new_batch["batch_idx"] = torch.cat(new_batch["batch_idx"], 0) return new_batch class YOLOMultiModalDataset(YOLODataset): """ Dataset class for loading object detection and/or segmentation labels in YOLO format. Args: data (dict, optional): A dataset YAML dictionary. Defaults to None. task (str): An explicit arg to point current task, Defaults to 'detect'. Returns: (torch.utils.data.Dataset): A PyTorch dataset object that can be used for training an object detection model. """ def __init__(self, *args, data=None, task="detect", **kwargs): """Initializes a dataset object for object detection tasks with optional specifications.""" super().__init__(*args, data=data, task=task, **kwargs) def update_labels_info(self, label): """Add texts information for multi-modal model training.""" labels = super().update_labels_info(label) # NOTE: some categories are concatenated with its synonyms by `/`. labels["texts"] = [v.split("/") for _, v in self.data["names"].items()] return labels def build_transforms(self, hyp=None): """Enhances data transformations with optional text augmentation for multi-modal training.""" transforms = super().build_transforms(hyp) if self.augment: # NOTE: hard-coded the args for now. transforms.insert(-1, RandomLoadText(max_samples=min(self.data["nc"], 80), padding=True)) return transforms class GroundingDataset(YOLODataset): """Handles object detection tasks by loading annotations from a specified JSON file, supporting YOLO format.""" def __init__(self, *args, task="detect", json_file, **kwargs): """Initializes a GroundingDataset for object detection, loading annotations from a specified JSON file.""" assert task == "detect", "`GroundingDataset` only support `detect` task for now!" self.json_file = json_file super().__init__(*args, task=task, data={}, **kwargs) def get_img_files(self, img_path): """The image files would be read in `get_labels` function, return empty list here.""" return [] def get_labels(self): """Loads annotations from a JSON file, filters, and normalizes bounding boxes for each image.""" labels = [] LOGGER.info("Loading annotation file...") with open(self.json_file) as f: annotations = json.load(f) images = {f'{x["id"]:d}': x for x in annotations["images"]} img_to_anns = defaultdict(list) for ann in annotations["annotations"]: img_to_anns[ann["image_id"]].append(ann) for img_id, anns in TQDM(img_to_anns.items(), desc=f"Reading annotations {self.json_file}"): img = images[f"{img_id:d}"] h, w, f = img["height"], img["width"], img["file_name"] im_file = Path(self.img_path) / f if not im_file.exists(): continue self.im_files.append(str(im_file)) bboxes = [] cat2id = {} texts = [] for ann in anns: if ann["iscrowd"]: continue box = np.array(ann["bbox"], dtype=np.float32) box[:2] += box[2:] / 2 box[[0, 2]] /= float(w) box[[1, 3]] /= float(h) if box[2] <= 0 or box[3] <= 0: continue cat_name = " ".join([img["caption"][t[0] : t[1]] for t in ann["tokens_positive"]]) if cat_name not in cat2id: cat2id[cat_name] = len(cat2id) texts.append([cat_name]) cls = cat2id[cat_name] # class box = [cls] + box.tolist() if box not in bboxes: bboxes.append(box) lb = np.array(bboxes, dtype=np.float32) if len(bboxes) else np.zeros((0, 5), dtype=np.float32) labels.append( { "im_file": im_file, "shape": (h, w), "cls": lb[:, 0:1], # n, 1 "bboxes": lb[:, 1:], # n, 4 "normalized": True, "bbox_format": "xywh", "texts": texts, } ) return labels def build_transforms(self, hyp=None): """Configures augmentations for training with optional text loading; `hyp` adjusts augmentation intensity.""" transforms = super().build_transforms(hyp) if self.augment: # NOTE: hard-coded the args for now. transforms.insert(-1, RandomLoadText(max_samples=80, padding=True)) return transforms class YOLOConcatDataset(ConcatDataset): """ Dataset as a concatenation of multiple datasets. This class is useful to assemble different existing datasets. """ @staticmethod def collate_fn(batch): """Collates data samples into batches.""" return YOLODataset.collate_fn(batch) # TODO: support semantic segmentation class SemanticDataset(BaseDataset): """ Semantic Segmentation Dataset. This class is responsible for handling datasets used for semantic segmentation tasks. It inherits functionalities from the BaseDataset class. Note: This class is currently a placeholder and needs to be populated with methods and attributes for supporting semantic segmentation tasks. """ def __init__(self): """Initialize a SemanticDataset object.""" super().__init__() class ClassificationDataset: """ Extends torchvision ImageFolder to support YOLO classification tasks, offering functionalities like image augmentation, caching, and verification. It's designed to efficiently handle large datasets for training deep learning models, with optional image transformations and caching mechanisms to speed up training. This class allows for augmentations using both torchvision and Albumentations libraries, and supports caching images in RAM or on disk to reduce IO overhead during training. Additionally, it implements a robust verification process to ensure data integrity and consistency. Attributes: cache_ram (bool): Indicates if caching in RAM is enabled. cache_disk (bool): Indicates if caching on disk is enabled. samples (list): A list of tuples, each containing the path to an image, its class index, path to its .npy cache file (if caching on disk), and optionally the loaded image array (if caching in RAM). torch_transforms (callable): PyTorch transforms to be applied to the images. """ def __init__(self, root, args, augment=False, prefix=""): """ Initialize YOLO object with root, image size, augmentations, and cache settings. Args: root (str): Path to the dataset directory where images are stored in a class-specific folder structure. args (Namespace): Configuration containing dataset-related settings such as image size, augmentation parameters, and cache settings. It includes attributes like `imgsz` (image size), `fraction` (fraction of data to use), `scale`, `fliplr`, `flipud`, `cache` (disk or RAM caching for faster training), `auto_augment`, `hsv_h`, `hsv_s`, `hsv_v`, and `crop_fraction`. augment (bool, optional): Whether to apply augmentations to the dataset. Default is False. prefix (str, optional): Prefix for logging and cache filenames, aiding in dataset identification and debugging. Default is an empty string. """ import torchvision # scope for faster 'import ultralytics' # Base class assigned as attribute rather than used as base class to allow for scoping slow torchvision import if TORCHVISION_0_18: # 'allow_empty' argument first introduced in torchvision 0.18 self.base = torchvision.datasets.ImageFolder(root=root, allow_empty=True) else: self.base = torchvision.datasets.ImageFolder(root=root) self.samples = self.base.samples self.root = self.base.root # Initialize attributes if augment and args.fraction < 1.0: # reduce training fraction self.samples = self.samples[: round(len(self.samples) * args.fraction)] self.prefix = colorstr(f"{prefix}: ") if prefix else "" self.cache_ram = args.cache is True or str(args.cache).lower() == "ram" # cache images into RAM if self.cache_ram: LOGGER.warning( "WARNING ⚠️ Classification `cache_ram` training has known memory leak in " "https://github.com/ultralytics/ultralytics/issues/9824, setting `cache_ram=False`." ) self.cache_ram = False self.cache_disk = str(args.cache).lower() == "disk" # cache images on hard drive as uncompressed *.npy files self.samples = self.verify_images() # filter out bad images self.samples = [list(x) + [Path(x[0]).with_suffix(".npy"), None] for x in self.samples] # file, index, npy, im scale = (1.0 - args.scale, 1.0) # (0.08, 1.0) self.torch_transforms = ( classify_augmentations( size=args.imgsz, scale=scale, hflip=args.fliplr, vflip=args.flipud, erasing=args.erasing, auto_augment=args.auto_augment, hsv_h=args.hsv_h, hsv_s=args.hsv_s, hsv_v=args.hsv_v, ) if augment else classify_transforms(size=args.imgsz, crop_fraction=args.crop_fraction) ) def __getitem__(self, i): """Returns subset of data and targets corresponding to given indices.""" f, j, fn, im = self.samples[i] # filename, index, filename.with_suffix('.npy'), image if self.cache_ram: if im is None: # Warning: two separate if statements required here, do not combine this with previous line im = self.samples[i][3] = cv2.imread(f) elif self.cache_disk: if not fn.exists(): # load npy np.save(fn.as_posix(), cv2.imread(f), allow_pickle=False) im = np.load(fn) else: # read image im = cv2.imread(f) # BGR # Convert NumPy array to PIL image im = Image.fromarray(cv2.cvtColor(im, cv2.COLOR_BGR2RGB)) sample = self.torch_transforms(im) return {"img": sample, "cls": j} def __len__(self) -> int: """Return the total number of samples in the dataset.""" return len(self.samples) def verify_images(self): """Verify all images in dataset.""" desc = f"{self.prefix}Scanning {self.root}..." path = Path(self.root).with_suffix(".cache") # *.cache file path try: cache = load_dataset_cache_file(path) # attempt to load a *.cache file assert cache["version"] == DATASET_CACHE_VERSION # matches current version assert cache["hash"] == get_hash([x[0] for x in self.samples]) # identical hash nf, nc, n, samples = cache.pop("results") # found, missing, empty, corrupt, total if LOCAL_RANK in {-1, 0}: d = f"{desc} {nf} images, {nc} corrupt" TQDM(None, desc=d, total=n, initial=n) if cache["msgs"]: LOGGER.info("\n".join(cache["msgs"])) # display warnings return samples except (FileNotFoundError, AssertionError, AttributeError): # Run scan if *.cache retrieval failed nf, nc, msgs, samples, x = 0, 0, [], [], {} with ThreadPool(NUM_THREADS) as pool: results = pool.imap(func=verify_image, iterable=zip(self.samples, repeat(self.prefix))) pbar = TQDM(results, desc=desc, total=len(self.samples)) for sample, nf_f, nc_f, msg in pbar: if nf_f: samples.append(sample) if msg: msgs.append(msg) nf += nf_f nc += nc_f pbar.desc = f"{desc} {nf} images, {nc} corrupt" pbar.close() if msgs: LOGGER.info("\n".join(msgs)) x["hash"] = get_hash([x[0] for x in self.samples]) x["results"] = nf, nc, len(samples), samples x["msgs"] = msgs # warnings save_dataset_cache_file(self.prefix, path, x, DATASET_CACHE_VERSION) return samples(这是dataset.py代码)
09-25
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