some changes to the average stat module, use '/aux clear average' in case of problems
This commit is contained in:
Manuel Simon Hirsig
+97
-94
@@ -1,8 +1,8 @@
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local private, public = {}, {}
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Aux.stat_average = public
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private.PUSH_INTERVAL = 3
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private.NEW_RECORD = '0:0:0:0:0:0:0:0'
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private.PUSH_INTERVAL = 20
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private.NEW_RECORD = '0:0:0:0:0:0:0:0:0'
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function private.load_data()
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local dataset = Aux.persistence.load_dataset()
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@@ -41,93 +41,94 @@ function public.process_auction(auction_info)
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item_record[1] = item_record[1] + 1 -- auction count
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item_record[2] = item_record[2] + 1 -- daily auction count
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item_record[3] = item_record[3] + buyout -- daily accumulated buyout
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item_record[4] = item_record[4] ~= 0 and min(item_record[4], buyout) or buyout -- daily min buyout
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private.write_record(auction_info.item_key, item_record)
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end
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function public.get_price_data(item_key)
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local auction_count, daily_auction_count, daily_accumulated_buyout, seen_days, EMA3, EMA7, EMA14 = unpack(public.read_record(item_key))
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local auction_count, daily_auction_count, daily_accumulated_buyout, daily_min_buyout, seen_days, avg_EMA5, avg_EMA30, min_EMA5, min_EMA30 = unpack(public.read_record(item_key))
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local daily_average = daily_accumulated_buyout / daily_auction_count
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return auction_count, seen_days, daily_average, EMA3, EMA7, EMA14
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return auction_count, seen_days, daily_average, daily_min_buyout, avg_EMA5, avg_EMA30, min_EMA5, min_EMA30
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end
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function public.get_mean(item_key)
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local _, daily_auction_count, daily_accumulated_buyout, seen_days, EMA3, EMA7, EMA14 = unpack(public.read_record(item_key))
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local mean = 0
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local daily_average = daily_accumulated_buyout / daily_auction_count
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if seen_days == 0 then
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if daily_auction_count > 0 then
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mean = daily_average
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end
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elseif seen_days <= 3 then -- No EMAs before day 4
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mean = EMA3
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if daily_auction_count > 0 then
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mean = (mean * seen_days + daily_average) / (seen_days + 1)
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end
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else
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-- we have 4 or more days of data, potentially enough to perform mean and stddev calculations
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local count = 0
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local valueset, weightset = {}, {}
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-- include daily data if available
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if daily_auction_count > 0 then
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count = 1
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valueset[count] = daily_average
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weightset[count] = 1
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end
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-- EMA3: standard weight 3, reduced if seenDays < 6, reduced if there was daily data, but never less than 1
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local weight = 3 - count
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if seen_days < 6 then
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weight = seen_days - 3
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if weight > 1 then
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weight = weight - count
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end
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end
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count = count + 1
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valueset[count] = EMA3
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weightset[count] = weight
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-- EMA7: standard weight 4, reduced if seenDays < 10
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if seen_days > 6 then
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count = count + 1
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valueset[count] = EMA7
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if seen_days < 10 then
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weightset[count] = seen_days - 6
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else
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weightset[count] = 4
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end
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end
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-- EMA14: standard weight 7, reduced if seenDays < 17
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if seen_days > 10 then
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count = count + 1
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valueset[count] = EMA14
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if seen_days < 17 then
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weightset[count] = seen_days - 10
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else
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weightset[count] = 7
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end
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end
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-- we will use a weighted incremental algorithm, based on sample code by West and Knuth http://en.wikipedia.org/wiki/Algorithms_for_calculating_variance
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local sumWeight, sumSquares = 0, 0 -- actually "sum of squares of differences from the (current) mean", but that's rather long for a variable name.
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for i=1,count do
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local value, weight = valueset[i], weightset[i]
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local nextweight = weight + sumWeight
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local valuediff = value - mean
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local meanadjust = valuediff * weight / nextweight
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mean = mean + meanadjust
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sumSquares = sumSquares + sumWeight * valuediff * meanadjust
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sumWeight = nextweight
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end
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-- stddev = sqrt(sumSquares / sumWeight * count / (count - 1))
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end
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return mean
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local _, daily_auction_count, daily_accumulated_buyout, daily_min_buyout, seen_days, avg_EMA5, avg_EMA30, min_EMA5, min_EMA30 = unpack(public.read_record(item_key))
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--
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-- local mean = 0
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-- local daily_average = daily_accumulated_buyout / daily_auction_count
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--
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-- if seen_days == 0 then
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-- if daily_auction_count > 0 then
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-- mean = daily_average
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-- end
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-- elseif seen_days <= 3 then -- No EMAs before day 4
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-- mean = EMA3
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-- if daily_auction_count > 0 then
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-- mean = (mean * seen_days + daily_average) / (seen_days + 1)
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-- end
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-- else
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-- -- we have 4 or more days of data, potentially enough to perform mean and stddev calculations
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-- local count = 0
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-- local valueset, weightset = {}, {}
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--
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-- -- include daily data if available
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-- if daily_auction_count > 0 then
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-- count = 1
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-- valueset[count] = daily_average
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-- weightset[count] = 1
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-- end
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--
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-- -- EMA3: standard weight 3, reduced if seenDays < 6, reduced if there was daily data, but never less than 1
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-- local weight = 3 - count
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-- if seen_days < 6 then
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-- weight = seen_days - 3
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-- if weight > 1 then
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-- weight = weight - count
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-- end
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-- end
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-- count = count + 1
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-- valueset[count] = EMA3
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-- weightset[count] = weight
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--
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-- -- EMA7: standard weight 4, reduced if seenDays < 10
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-- if seen_days > 6 then
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-- count = count + 1
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-- valueset[count] = EMA7
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-- if seen_days < 10 then
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-- weightset[count] = seen_days - 6
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-- else
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-- weightset[count] = 4
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-- end
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-- end
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--
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-- -- EMA14: standard weight 7, reduced if seenDays < 17
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-- if seen_days > 10 then
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-- count = count + 1
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-- valueset[count] = EMA14
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-- if seen_days < 17 then
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-- weightset[count] = seen_days - 10
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-- else
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-- weightset[count] = 7
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-- end
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-- end
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--
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-- -- we will use a weighted incremental algorithm, based on sample code by West and Knuth http://en.wikipedia.org/wiki/Algorithms_for_calculating_variance
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-- local sumWeight, sumSquares = 0, 0 -- actually "sum of squares of differences from the (current) mean", but that's rather long for a variable name.
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-- for i=1,count do
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-- local value, weight = valueset[i], weightset[i]
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-- local nextweight = weight + sumWeight
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-- local valuediff = value - mean
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-- local meanadjust = valuediff * weight / nextweight
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-- mean = mean + meanadjust
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-- sumSquares = sumSquares + sumWeight * valuediff * meanadjust
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-- sumWeight = nextweight
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-- end
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--
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-- -- stddev = sqrt(sumSquares / sumWeight * count / (count - 1))
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-- end
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--
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-- return mean
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end
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function private.push_data()
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@@ -137,30 +138,32 @@ function private.push_data()
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for item_key, _ in pairs(item_data) do
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local item_record = public.read_record(item_key)
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local _, daily_auction_count, daily_accumulated_buyout, seen_days, EMA3, EMA7, EMA14 = unpack(item_record)
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local _, daily_auction_count, daily_accumulated_buyout, daily_min_buyout, seen_days, avg_EMA5, avg_EMA30, min_EMA5, min_EMA30 = unpack(item_record)
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if daily_auction_count > 0 then
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local daily_average = daily_accumulated_buyout / daily_auction_count
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if seen_days < 3 then
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-- for first 3 days perform plain average instead of EMAs
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EMA3 = (EMA3 * seen_days + daily_average) / (seen_days + 1)
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EMA7 = EMA3
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EMA14 = EMA3
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if seen_days == 0 then
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avg_EMA5 = daily_average
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avg_EMA30 = daily_average
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min_EMA5 = daily_min_buyout
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min_EMA30 = daily_min_buyout
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else
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-- do normal EMA calculations
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EMA3 = (EMA3 * 2 + daily_average) / 3
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EMA7 = (EMA7 * 6 + daily_average) / 7
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EMA14 = (EMA14 * 13 + daily_average) / 14
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avg_EMA5 = 2/3 * avg_EMA5 + 1/3 * daily_average
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avg_EMA30 = 13/14 * avg_EMA30 + 1/14 * daily_average
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min_EMA5 = 2/3 * min_EMA5 + 1/3 * daily_min_buyout
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min_EMA30 = 13/14 * min_EMA30 + 1/14 * daily_min_buyout
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end
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item_record[2] = 0 -- daily auction count
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item_record[3] = 0 -- daily accumulated buyout
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item_record[4] = seen_days + 1 -- seen days
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item_record[5] = EMA3 -- EMA3
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item_record[6] = EMA7 -- EMA7
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item_record[7] = EMA14 -- EMA14
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item_record[4] = 0 -- daily min buyout
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item_record[5] = seen_days + 1 -- seen days
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item_record[6] = avg_EMA5 -- avg_EMA5
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item_record[7] = avg_EMA30 -- avg_EMA30
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item_record[8] = min_EMA5 -- min_EMA5
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item_record[9] = min_EMA30 -- min_EMA30
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private.write_record(item_key, item_record)
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end
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