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BasicTradeStats for backtrader
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| ####################################### | |
| # Code: Rich O'Regan (London) Sep 2017 | |
| ####################################### | |
| import math | |
| import numpy as np | |
| from backtrader import Analyzer | |
| from backtrader.utils import AutoOrderedDict, AutoDict | |
| class BasicTradeStats(Analyzer): | |
| ''' | |
| Summary: | |
| Calculate popular statistics on all closed trades. | |
| Also calculates statistics on winning and losing trades seperately. | |
| Statistics include, percentage of winning trades, reward risk ratio | |
| and basic streak analysis. | |
| Params: | |
| - ``calcStatsAfterEveryTrade``: (default: ``False``) | |
| ### COMMENTS NEEDED #### | |
| - ``useStandardPrint``: (default: ``False``) | |
| ### COMMENTS NEEDED #### | |
| Methods: | |
| - get_analysis | |
| Returns a dictionary holding the statistics. | |
| Statistics calculated: | |
| From ALL trades: | |
| total - number of all trades closed and open. | |
| open - number of open trades. | |
| NOTE: Obvious point but worth reminding.If a trade is open, | |
| it is not yet known if it will be a winner or loser. | |
| closed - number of closed trades. | |
| - pnl | |
| total - p&l of all trades combined. | |
| average - average p&l from every trade taken, won or lost. | |
| - stats | |
| profitFactor - total of all profit / total of all losses. | |
| winFactor - number of trades won / number of trades lost. | |
| winRate - percentage of winning trades (also called strike rate). | |
| rewardRiskRatio - average profit of winning trades | |
| / average loss from losing trades. | |
| From WON trades: | |
| closed - number of closed trades. | |
| percent - winning trades as a percentage of all closed trades. | |
| - pnl | |
| total - total p&l of only the winning trades. | |
| average - average p&l of winning trades. | |
| median - median p&l of winning trades. | |
| max - maximun p&l generated on a trade, | |
| from set of all winning trades. | |
| - streak | |
| current - current length of winning streak (if any). | |
| max - maximum length of winning streak that occurred. | |
| average - average length of winning streaks. | |
| median - median length of winning streaks. | |
| From LOST trades: | |
| Includes stats from WON above but applied to losing trades. | |
| [This 'basictradedtats.py' was coded by Richard O'Regan (London) October 2017] | |
| ''' | |
| # Declare parameters user can pass to this Analyzer.. | |
| params = ( | |
| # Run calculations once at end (fast) OR after every trade (slower) | |
| ('calcStatsAfterEveryTrade', False ), | |
| # Filter stats on long or short trades only.. | |
| # 'all' = all trades | |
| # 'long' = long trades only | |
| # 'short' = short trades only | |
| ('filter', 'all'), | |
| # When .print() called | |
| # If True -> use standard BackTrader print() [this is verbose] | |
| # If False -> instead use my clearer table format.. | |
| ('useStandardPrint', False), | |
| ) | |
| def nextstart(self): | |
| # Called once by Backtrader first valid bar of data.. | |
| o = self.rets # User returned object.. | |
| o.all.firstStrategyTradingDate = self.datas[0].datetime.datetime(0) | |
| self.next() # Run next() method.. | |
| def next(self): | |
| # Called every bar of data strategy runs. | |
| # Get last trading date strategy runs used to calc annual statistics. | |
| # Last trading date so far, is simply the current date of this bar. | |
| # As more bars pass by, this last date is updated to reflect latest | |
| # last date.. | |
| self.rets.all.lastStrategyTradingDate = self.datas[0].datetime.datetime(0) | |
| def create_analysis(self): | |
| # Set up variables.. | |
| # Variables hidden from user.. | |
| # Depending on the input parameter user provides for 'filter', | |
| # append 'LONG' or 'SHORT' in table heading output to user. | |
| # Helps user identify the types of trades stats calculated on, | |
| # either long, short or all (i.e. both).. | |
| if self.p.filter == 'long': | |
| self._tableLongShort = 'LONG' # Append 'LONG' to table heading.. | |
| elif self.p.filter == 'short': | |
| self._tableLongShort = 'SHORT' # Append 'SHORT' to table heading.. | |
| elif self.p.filter == 'all': | |
| self._tableLongShort = 'TRADES' # Blank char appended to our table.. | |
| else: | |
| raise Exception("Parameter 'filter' must be 'long', 'short', or" + | |
| " 'all' not '%s'." % str(self.p.filter)) | |
| self._all_pnl_list=[] # hidden from user - all trades pnl. | |
| self._won_pnl_list=[] # hidden from user - win trades pnl. | |
| self._lost_pnl_list=[] # hidden from user - lost trades pnl. | |
| self._curStreak = None # Current streak type [None, 'Won', 'Lost'] | |
| self._wonStreak_list=[] # Store each won streak in list.. | |
| self._lostStreak_list=[] # Store each loss streak in list.. | |
| # Variables output to user.. | |
| o = self.rets = AutoOrderedDict() # Return user object.. | |
| # Stats applied to all trades (winners and losers).. | |
| o.all.firstStrategyTradingDate = None | |
| o.all.lastStrategyTradingDate = None | |
| o.all.trades.total = 0 | |
| o.all.trades.open = 0 | |
| o.all.trades.closed = 0 | |
| o.all.pnl.total = None | |
| o.all.pnl.average = None | |
| o.all.streak.zScore = None | |
| o.all.stats.profitFactor = None | |
| o.all.stats.winFactor = None | |
| o.all.stats.winRate = None | |
| o.all.stats.rewardRiskRatio = None | |
| o.all.stats.expectancyPercentEstimated = None | |
| o.all.stats.kellyPercent = None | |
| o.all.stats.tradesPerYear = None | |
| o.all.stats.perTradeOpportunityPercent = None | |
| o.all.stats.annualOpportunityPercent = None | |
| o.all.stats.annualOpportunityCompoundedPercent = None | |
| #o.all.stats.stake1PercentAnnualOpportunityCompoundedPercent = None | |
| for each in ['won', 'lost']: | |
| oWL=self.rets[each] | |
| oWL.trades.closed = 0 | |
| oWL.trades.percent = None | |
| oWL.pnl.total = None | |
| oWL.pnl.average = None | |
| oWL.pnl.median = None | |
| oWL.pnl.max = None | |
| oWL.streak.current = 0 | |
| oWL.streak.max = None | |
| oWL.streak.average = None | |
| oWL.streak.median = None | |
| def calculate_statistics(self): | |
| # Calculate various statistics.. | |
| # Applied to three groups; | |
| # 1) All trades | |
| # 2) Winning trades | |
| # 3) Losing trades | |
| # NOTE: To see for different types of trades, e.g. Long or Short, | |
| # simply run strategy with Long trades only, gather stats, | |
| # and then re-run with short trades only and gather stats. | |
| # A system that goes long and short can be simplified to two different | |
| # systems. likely parameters will be different also.. | |
| # This method can be called either; | |
| # 1) After every completed trade, i.e. ran multiple times. | |
| # 2) Once at end after all trades completed. | |
| # Option 1) occurs if calcStatsAfterEveryTrade is set to True. | |
| # Option 2) occurs if calcStatsAfterEveryTrade is set to False. | |
| # NOTE: Final output after last trade will always be the same. | |
| # regardless of which option you use. The difference is that option 1) | |
| # allows your strategy to access these statistics whilst it is running. | |
| # Option 1) | |
| # Running after every trade is obviously slower but needed if your | |
| # strategy needs to know these statstics whilst running. i.e. may adapt | |
| # itself as profit and loss statistics change. | |
| # example1: as winning streak increases, bet more. | |
| # example2: if win rate drops to less than 50% (0.5) stop trading until | |
| # win rate picks back up to 70% (0.7).. | |
| # Option 2) | |
| # If strategy does not need to know these information, it will be | |
| # quicker to run statistics just once at the end. In which case | |
| # option 2 is quicker and more efficient. | |
| # Must be at least 1 trade to proceed.. | |
| if self._all_pnl_list!=[]: | |
| # Set up 'pointers' to save typing long lines.. | |
| oA=self.rets.all | |
| oW=self.rets.won | |
| oL=self.rets.lost | |
| oA.pnl.total = np.sum(self._all_pnl_list) | |
| oA.pnl.average = np.mean(self._all_pnl_list) | |
| # Calc stats seperately for winning and losing trades.. | |
| for each in ['won', 'lost']: | |
| # Get our list. Either _won_pnl_list or _lost_pnl_list.. | |
| pnlList = eval('self._' + str(each) + '_pnl_list') | |
| # Check list not empty, else can't calculate median e.t.c. | |
| if pnlList!=[]: | |
| oWL=self.rets[each] | |
| oWL.trades.closed = np.size(pnlList) | |
| oWL.trades.percent = len(pnlList)/len(self._all_pnl_list)*100 | |
| oWL.pnl.total = np.sum(pnlList) | |
| # Note: Max win calculated with max() function, | |
| # but Max loss calculated using min() function.. | |
| oWL.pnl.max = (np.max(pnlList) if each=='won' else np.min(pnlList)) | |
| oWL.pnl.average = np.mean(pnlList) | |
| oWL.pnl.median = np.median(pnlList) | |
| # Streak calculations.. | |
| streak = eval('self._' + str(each) + 'Streak_list') | |
| if streak!=[]: | |
| oWL.streak.max = np.max(streak) | |
| oWL.streak.average = np.mean(streak) | |
| # Can only be integer. Cast from double/float to integer | |
| oWL.streak.median = int(np.median(streak)) | |
| # Calc key stats on ALL trades.. | |
| oA.stats.winRate = oW.trades.percent | |
| # Can only calc following if at least 1 winner and 1 loser.. | |
| if self._won_pnl_list!=[] and self._lost_pnl_list!=[]: | |
| oA.streak.zScore = self.zScore(oW.trades.closed, | |
| oL.trades.closed, | |
| len(self._wonStreak_list)) | |
| oA.stats.profitFactor = (oW.pnl.total | |
| / (-1 * oL.pnl.total)) | |
| oA.stats.winFactor = (oW.trades.closed | |
| / oL.trades.closed) | |
| if oW.pnl.average != 0: # Check for division by zero | |
| oA.stats.kellyPercent = oA.pnl.average / oW.pnl.average*100 | |
| if oL.pnl.average != 0: # Check for division by zero | |
| oA.stats.rewardRiskRatio = (oW.pnl.average | |
| / (-1 * oL.pnl.average)) | |
| oA.stats.expectancyPercentEstimated = (oA.pnl.average | |
| / (-1 * oL.pnl.average) * 100) | |
| if (oA.stats.kellyPercent is not None | |
| and oA.stats.expectancyPercentEstimated is not None): | |
| # This is my (Rich O'Regan) own idea for important system | |
| # measures.. simple but I believe essence. | |
| # You will know how works by looking at the simple calculation.. | |
| # 'perTradeOpportunityPercent' | |
| # a system may have 3 trades a year, will get a shitty AOP & | |
| # AOCP compared to a system with 200.. but don't ignore as | |
| # could still be great system and combined with other similar | |
| # systems to increase trades.. | |
| # | |
| # 'annualOpportunityPercent' | |
| # Equalises for effect of time. Take into account number of | |
| # trading days system backtested over and calulate the | |
| # 'opportunity' for whole year. | |
| # 'annualOpportunityCompoundedPercent' | |
| # Same as above, just assume after each trade we are able to | |
| # compound profits. | |
| # Note: in reality may not be possible to compound after each | |
| # trade coz amount of contracts allowed with margin or risk.. | |
| # MORE IMPORTANTLY, may have several trades on at the same time, | |
| # some may close at same time (e.g. a shared stop), and | |
| # outcome wasn't known, so new trades made whilst old positions | |
| # not yet closed, would not have had benefits of knowing we | |
| # could compound the profits (or allow for losses).. | |
| # WHAT DOES THE ABOVE SHIT MEAN?: | |
| # It's a useful measure, sometimes it is precise, though in some | |
| # system cases, perhaps with muliple overlapping trades, | |
| # it's more of an helpful estimation.. | |
| # TO % - 'perTradeOpportunityPercent' | |
| oA.stats.perTradeOpportunityPercent = ( | |
| (oA.stats.kellyPercent / 100) * | |
| (oA.stats.expectancyPercentEstimated / 100) * 100) | |
| # AO % - 'annualOpportunityPercent' | |
| _daysStrategyRan = (oA.lastStrategyTradingDate - | |
| oA.firstStrategyTradingDate).days | |
| oA.stats.tradesPerYear = oA.trades.closed * 365 / _daysStrategyRan | |
| oA.stats.annualOpportunityPercent = (oA.stats.tradesPerYear * | |
| oA.stats.perTradeOpportunityPercent) | |
| # AOC % - 'annualOpportunityCompoundedPercent' | |
| _power = (oA.stats.tradesPerYear) | |
| _value = ((oA.stats.perTradeOpportunityPercent / 100) + 1) | |
| oA.stats.annualOpportunityCompoundedPercent = ( | |
| (np.power(_value, _power) - 1) * 100 ) | |
| # 1% stake AOC % - 'stake1PercentAnnualOpportunityCompoundedPercent' | |
| #_1pctTradeOp = 0.01 * (oA.stats.expectancyPercentEstimated / 100) | |
| #_1pctValue = _1pctTradeOp + 1 | |
| #oA.stats.stake1PercentAnnualOpportunityCompoundedPercent = ( | |
| # (np.power(_1pctValue, _power) - 1) * 100 ) | |
| def preparation_pre_calculation(self, trade): | |
| # This code does the basic steps of sorting each trade into a winner or | |
| # loser list which is then used later by 'calculate_statistics()'. | |
| # It also sets up the lists for winner and losing streak analysis. | |
| # NOTE: the code here runs in linear n time. There should be little | |
| # reason to optimise. Better to optimise 'calculate_statistics()' as | |
| # this may be running every trade in exponential O^n time. | |
| # [If you don't know what n time or O^n times means, don't stress :) ] | |
| if trade.justopened: | |
| # Trade just opened, update number of trades.. | |
| self.rets.all.trades.total += 1 | |
| self.rets.all.trades.open += 1 | |
| elif trade.status == trade.Closed: | |
| # Trade closed, updated number of trades closed.. | |
| self.rets.all.trades.open += -1 | |
| self.rets.all.trades.closed += 1 | |
| # Put each trade pnl into different buckets (lists) depending if | |
| # they are winning or losing trades.. | |
| pnl = trade.pnlcomm | |
| self._all_pnl_list.append(pnl) # List of all win & losing trades. | |
| if pnl >= 0: | |
| # Current trade is a winner.. | |
| self._won_pnl_list.append(pnl) # List of all win trades | |
| # Update winning streak list.. | |
| if self._curStreak=='Won': | |
| # Previous trade was also a winner.. | |
| self.rets.won.streak.current+=1 | |
| else: | |
| # Previous trade was a loser.. | |
| self._curStreak='Won' | |
| self._lostStreak_list.append(self.rets.lost.streak.current) | |
| self.rets.lost.streak.current=0 | |
| self.rets.won.streak.current+=1 | |
| else: | |
| # Current trade is a loser.. | |
| self._lost_pnl_list.append(pnl) # List of all losing trades | |
| # Update losing streak list.. | |
| if self._curStreak=='Lost': | |
| # Previous trade was also a loser.. | |
| self.rets.lost.streak.current+=1 | |
| else: | |
| # Previous trade was a winner.. | |
| self._curStreak='Lost' | |
| self._wonStreak_list.append(self.rets.won.streak.current) | |
| self.rets.won.streak.current=0 | |
| self.rets.lost.streak.current+=1 | |
| def notify_trade(self, trade): | |
| longMatch = trade.long and self.p.filter == 'long' | |
| shortMatch = not trade.long and self.p.filter == 'short' | |
| allMatch = self.p.filter == 'all' | |
| if True in [longMatch, shortMatch, allMatch]: | |
| self.preparation_pre_calculation(trade) | |
| if self.p.calcStatsAfterEveryTrade: | |
| self.calculate_statistics() | |
| def stop(self): | |
| # Get last trading date strategy runs.. used to calc annual statistics.. | |
| #o = self.rets # User returned object.. | |
| #o.all.lastStrategyTradingDate = self.datas[0].datetime.datetime(0) | |
| # REMOVED: if not self.p.calcStatsAfterEveryTrade: self.calculate_statistics() | |
| # Removed line above, if we are using dates strategy ran for, then | |
| # notify_trade() different than next() | |
| # I.e. most statistics identical, but to check how many trading days passed | |
| # we may have a trade then many days till another.. because of this | |
| # any statistics like 'annualOpportunityPercent' that rely on number of | |
| # trading days have occured, may be out.. | |
| # By running calculate_statistics() at end regardless if we calculated | |
| # per trade, may mean for most statistics, we ran one time extra | |
| # unecessary, but for trading day calculations, we need to run for | |
| # accuracy.. | |
| self.calculate_statistics() # Run every time.. | |
| # Delete all lists we created to perform calculations.. | |
| # (to save memory) | |
| self._all_pnl_list=[] # hidden from user - all trades pnl. | |
| self._won_pnl_list=[] # hidden from user - win trades pnl. | |
| self._lost_pnl_list=[] # hidden from user - lost trades pnl. | |
| self._curStreak = None # Current streak type [None, 'Won', 'Lost'] | |
| self._wonStreak_list=[] # Store each won streak in list.. | |
| self._lostStreak_list=[] # Store each loss streak in list.. | |
| self.rets._close() # Check if we need this.. £££££££££#### | |
| def zScore(self, wins, losses, streaks): | |
| ''' | |
| Calculates the Z-Score of streaks of wins and losses from a trading system. | |
| If system has a significant Z score then it is potentially possible to | |
| exploit the system for extra profit. | |
| A negative Z score means that there are fewer streaks in the trading | |
| system than would be expected statistically. This means that winning | |
| trades tend to follow winning trades and that losing trades tend to | |
| follower losers. | |
| A positive Z score means that there are more streaks in the trading | |
| system than would be expected. This means that winners tend to follow | |
| losers and vice versa. | |
| A confidence level of 95% or above is generally regarded as significant | |
| enough to exploit the apparent non-randomness of streaks in a system. | |
| Z scores of above 1.96 and below -1.96 represent 95% confidence. | |
| i.e. Z scores less than 1.96 and greater than -1.96 e.g. 0.87 or -1.2, | |
| suggest that outcome of previous trade cannot be used successfully to | |
| predict (and therfore profit) from outcome of following trade. | |
| A score of 0.0 suggests trade outcome totally independent of previous | |
| trade outcome. | |
| THE CALCULATION: | |
| Z-Score = (n*(s - 0.5) - x) / ((x*(x - n))/(n - 1))^(1/2) | |
| Where: | |
| s = The total number of streaks in the sequence. | |
| w = The total number of winning trades in the sequence. | |
| L = The total number of losing trades in the sequence. | |
| n = w + L [i.e. total number of trades in the sequence.] | |
| x = 2*w*L | |
| CONFIDENCE LEVELS: | |
| z Score of 3.0 = 99.73% | |
| Z Score of 2.58 = 99.0% | |
| Z Score of 2.17 = 97.0% | |
| Z Score of 1.96 = 95.0% | |
| Z Score of 1.64 = 90.0% | |
| Z Score of 1.44 = 85.0% | |
| Z Score of 1.28 = 80.0% | |
| Z Score of 1.04 = 70.0% | |
| LIMITATIONS: | |
| The Z-Score does not take into account size of wins and losses in | |
| each trade of streak. Only binary outcomes considered, i.e. the | |
| trade either won or lost. | |
| Apparently Serial Correlation methods can deal with magnitude and | |
| therefore provide a more useful statistic than Z-Score. | |
| SOURCE OF INFORMATION: | |
| http://www.mypivots.com/dictionary/definition/233/z-score | |
| Maths probably came from book; | |
| The Mathematics of Money Management: Risk Analysis Techniques for | |
| Traders by Ralph Vince. | |
| [This 'Z-Score' function was coded by Richard O'Regan (London) October 2017] | |
| ''' | |
| w = wins | |
| L = losses | |
| s = streaks | |
| n = w + L | |
| x = 2*w*L | |
| denominator = math.sqrt( (x*(x - n)) / (n - 1) ) | |
| if denominator != 0: # Avoid division by zero error.. | |
| numerator = n*(s - 0.5) - x | |
| z = numerator/denominator | |
| return z | |
| # Denominator was zero, therefore can't calculate.. | |
| return None | |
| def print(self, *args, **kwargs): | |
| ''' | |
| Overide print method to display statistics to user in a | |
| more visually pleasing and space efficient table format. | |
| ''' | |
| # NOTE: Since this code is probably just a one off for this Analyzer | |
| # It is not yet a flexible general purpose method to display any data | |
| # in any table. | |
| # It currently have half programmable and half hardwired functionality. | |
| # Should this nicer output need to be used in other modules, the | |
| # code could be modified to become general purpose. | |
| # For now time is short and I just need something specific that works:) | |
| # If user requests standard output, print using parent class.. | |
| if self.p.useStandardPrint: | |
| super().print(*args, **kwargs) | |
| return | |
| # ..else override and make look nicer..! | |
| # Set up 'pointers' to save typing long lines.. | |
| oAt=self.rets.all.trades | |
| oAp=self.rets.all.pnl | |
| oAs=self.rets.all.stats | |
| oAk=self.rets.all.streak | |
| oWt=self.rets.won.trades | |
| oWp=self.rets.won.pnl | |
| oWk=self.rets.won.streak | |
| oLt=self.rets.lost.trades | |
| oLp=self.rets.lost.pnl | |
| oLk=self.rets.lost.streak | |
| dpsf=self.dpsf # Decimal Place & Significant Figure formatting.. | |
| #oWt.percent=None ### ROR remove | |
| # Structure for output | |
| # List of dicts #### improve comenting | |
| d = [ | |
| {'rowType':'table-top'}, | |
| {'rowType':'row-title', 'data': | |
| ['' , 'ALL ' + self._tableLongShort, | |
| '', self._tableLongShort + ' WON', self._tableLongShort + ' LOST']}, | |
| {'rowType':'table-seperator'}, | |
| {'rowType':'row-data', 'data': | |
| ['TRADES open', dpsf(oAt.open), | |
| 'TRADES ', '', '']}, | |
| #'%.2f' % oWt.percent if oWt.percent!=None else oWt.percent, | |
| #('%s' if oLt.percent is None else '%.2f') % oLt.percent]}, | |
| {'rowType':'row-data', 'data': | |
| ['closed', dpsf(oAt.closed), | |
| 'closed', dpsf(oWt.closed), dpsf(oLt.closed)]}, | |
| {'rowType':'row-data', 'data': | |
| ['Win Factor', dpsf(oAs.winFactor, dp=2), | |
| '%', dpsf(oWt.percent, dp=2), dpsf(oLt.percent, dp=2)]}, | |
| #['Win Factor','%.2f'% oAs.winFactor, '%', | |
| #'%.2f' % oWt.percent if oWt.percent!=None else oWt.percent, | |
| #('%s' if oLt.percent is None else '%.2f') % oLt.percent]}, | |
| {'rowType':'row-data', 'data': | |
| ['Trades per year', dpsf(oAs.tradesPerYear, dp=1), | |
| '', '', '']}, | |
| {'rowType':'table-seperator'}, | |
| {'rowType':'row-data', 'data': | |
| ['PROFIT total', dpsf(oAp.total, dp=2), | |
| 'PROFIT total', dpsf(oWp.total, dp=2), dpsf(oLp.total, dp=2)]}, | |
| {'rowType':'row-data', 'data': | |
| ['average', dpsf(oAp.average, dp=2), 'average', | |
| dpsf(oWp.average, dp=2), dpsf(oLp.average, dp=2)]}, | |
| {'rowType':'row-data', 'data': | |
| ['Profit Factor', dpsf(oAs.profitFactor, dp=2), | |
| 'median', dpsf(oWp.median, dp=2), dpsf(oLp.median, dp=2)]}, | |
| {'rowType':'row-data', 'data': | |
| ['Reward : Risk', dpsf(oAs.rewardRiskRatio, dp=2), | |
| 'max', dpsf(oWp.max, dp=2), dpsf(oLp.max, dp=2)]}, | |
| {'rowType':'table-seperator'}, | |
| {'rowType':'row-data', 'data': | |
| ['Kelly %', dpsf(oAs.kellyPercent, dp=1), | |
| 'STREAK current', dpsf(oWk.current), dpsf(oLk.current)]}, | |
| {'rowType':'row-data', 'data': | |
| ['Expectancy %', dpsf(oAs.expectancyPercentEstimated, dp=1), | |
| 'max' , dpsf(oWk.max), dpsf(oLk.max)]}, | |
| {'rowType':'row-data', 'data': | |
| ['TO %', dpsf(oAs.perTradeOpportunityPercent, dp=2), | |
| 'average', dpsf(oWk.average, dp=2), dpsf(oLk.average, dp=2)]}, | |
| {'rowType':'row-data', 'data': | |
| ['AO %', dpsf(oAs.annualOpportunityPercent, dp=1), | |
| 'median', dpsf(oWk.median), dpsf(oLk.median)]}, | |
| #{'rowType':'row-data', 'data': | |
| #['Kelly %', dpsf(oAs.kellyPercent, dp=1), | |
| #'', '', '']}, | |
| #{'rowType':'row-data', 'data': | |
| #['TO %', dpsf(oAs.perTradeOpportunityPercent, dp=2), | |
| #'', '', '']}, | |
| {'rowType':'row-data', 'data': | |
| ['AOC %', dpsf(oAs.annualOpportunityCompoundedPercent, dp=1), | |
| 'Z-Score', dpsf(oAk.zScore, dp=1), dpsf(oAk.zScore, dp=1)]}, | |
| #{'rowType':'row-data', 'data': | |
| #['1% AOC %', | |
| # dpsf(oAs.stake1PercentAnnualOpportunityCompoundedPercent, dp=2), | |
| #'', '', '']}, | |
| {'rowType':'table-bottom'} | |
| ] | |
| s = self.displayTable(d) | |
| print(s) | |
| def fixedWidthText(self, string, nChars=15, align='centre'): # ,horzChar='═'): | |
| # Displayoutput string of exactly n chars, no more no less (for good formatting).. | |
| # Convert input to string incase it is not e.g. an int | |
| string = str(string) | |
| # Pad input string with space chars either side. | |
| # Enables us to easily justify 'left','right' e.t.c. by slicing.. | |
| _s=' '*nChars + string + ' '*nChars | |
| if align=='left' or align=='l': | |
| return _s[nChars : nChars + nChars] | |
| elif align=='right' or align=='r': | |
| return _s[len(string):nChars+len(string)] | |
| elif align=='centre' or align=='center' or align=='c': | |
| startIndex = nChars - (int((nChars - len(string))/2)) | |
| return _s[startIndex : startIndex + nChars] | |
| else: | |
| raise Exception("Parameter 'align' must be 'left', 'right', or 'center' not '%s'." % str(align)) | |
| def displayTable(self, i): | |
| # Input is a list of dictionaries, the 5 column format hardwired to | |
| # run specifically with this method.. | |
| fWT = self.fixedWidthText # Shortcut to text formatting function. | |
| # Find out max width need for each of the columns. | |
| # This enables us to customise size of table cell and ensure data fits.. | |
| cs=[0,0,0,0,0] # Store size of columm for each of 5 columns.. | |
| for d in i: | |
| # Check for data rows.. | |
| if d['rowType'] in ['row-title','row-data','row-data2']: | |
| # Go thro each data cell and keep track of the max text length needed to display.. | |
| for c in range(5): # There are always 5 columns (hardwired) | |
| _l = len(str(d['data'][c])) | |
| if _l > cs[c]: cs[c]= _l | |
| # Display each row by joining table cells together with these chars.. | |
| (x,rx,lx,v,h)=('╬','╣','╠','║','═') | |
| (sv, hx, srx)=(' '+v, h+x, h+'╣') | |
| s='' | |
| for d in i: | |
| # Check for table formating rows.. | |
| if d['rowType']=='table-top': | |
| s+='╔═'+'═'*cs[0]+'╦'+'═'*cs[1]+'═╗'+' ╔═'+'═'*cs[2]+'╦'+'═'*cs[3]+'═╦'+'═'*cs[4]+'═╗\n' | |
| if d['rowType']=='table-seperator': | |
| s+='╠═'+'═'*cs[0]+'╬'+'═'*cs[1]+'═╣'+' ╠═'+'═'*cs[2]+'╬'+'═'*cs[3]+'═╬'+'═'*cs[4]+'═╣\n' | |
| if d['rowType']=='table-bottom': | |
| s+='╚═'+'═'*cs[0]+'╩'+'═'*cs[1]+'═╝'+' ╚═'+'═'*cs[2]+'╩'+'═'*cs[3]+'═╩'+'═'*cs[4]+'═╝' | |
| # Check for data rows.. | |
| if d['rowType']=='row-title': | |
| l = d['data'] | |
| s+= (v + fWT(l[0],cs[0]) + sv + fWT(l[1],cs[1],'center') + sv + ' ' + v | |
| + fWT(l[2],cs[2]) + sv + fWT(l[3],cs[3],'center') | |
| + sv + fWT(l[4],cs[4],'center') + sv + '\n') | |
| if d['rowType']=='row-data': | |
| l = d['data'] | |
| s+= (v + fWT(l[0],cs[0],'right') + sv + fWT(l[1],cs[1],'left') + sv + ' ' + v | |
| + fWT(l[2],cs[2],'right') + sv + fWT(l[3],cs[3],'left') | |
| + sv + fWT(l[4],cs[4],'left') + sv + '\n') | |
| if d['rowType']=='row-data2': | |
| l = d['data'] | |
| s+= (v + fWT(l[0],cs[0],'center') + sv + fWT(l[1],cs[1],'left') + sv + ' ' + v | |
| + fWT(l[2],cs[2],'right') + sv + fWT(l[3],cs[3],'left') | |
| + sv + fWT(l[4],cs[4],'left') + sv + '\n') | |
| # Return a string representing nicely formated table.. | |
| return s | |
| def dpsf(self, n=None, dp=None, sf=None): | |
| # Decimal Place & Significant Figure formatting.. | |
| # Logic used to display numeric values neatly: | |
| # dp = decimal places | |
| # sf = significant figues | |
| # n value is None type, e.g. a variable passed that does | |
| # not have enough data to be initialised correctly.. | |
| # Do not try to format a None type, will cause an exception, | |
| # instead pass it straight back.. | |
| if n == None: | |
| return 'None' | |
| # If no dp or sf provided.. | |
| # Display but allow space for sign | |
| #if dp == None and sf == None: | |
| # if | |
| # If just dp | |
| # check i= None | |
| # If just sf | |
| # check s= None | |
| # If both, take the biggest | |
| # checks n= None | |
| # Keep alignment if positive or negative number | |
| # e.g. -1.23 -> '-1.23' | |
| # e.g. 1.45 -> ' 1.45' extra space added so stays aligned.. | |
| if dp != None: | |
| _st = f'{dp}' | |
| # Format decimal place e.g. '%.2f' for 2dp.. | |
| _st = ('%.'+ _st +'f') % n | |
| return _st | |
| else: | |
| return str(n) |
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This looks like great work. Can you show or describe how this is called?