From e2cfc99eff7b20c9e97045a125ccc6cbd5ce11ba Mon Sep 17 00:00:00 2001 From: Junjie Jiang Date: Thu, 22 Feb 2018 14:46:09 -0500 Subject: [PATCH 1/7] addlinearregression --- scrape.py | 39 +- testing.csv | 875 ------------------------------------------- tradingStrategies.py | 77 +++- twitterAPI.py | 78 +++- 4 files changed, 164 insertions(+), 905 deletions(-) delete mode 100644 testing.csv diff --git a/scrape.py b/scrape.py index 0c2b2d8..cf786e3 100644 --- a/scrape.py +++ b/scrape.py @@ -1,4 +1,6 @@ #!/usr/bin/env python +import sys +import os import argparse import requests import time @@ -14,8 +16,10 @@ parser = argparse.ArgumentParser(description='Scraping Tokens and Coins') parser.add_argument('min_market_cap', metavar='min_cap', type=int, nargs='?', default=0, help='minimum market cap [usd] for currency to be scraped (default: scrape all)') +parser.add_argument('max_date', metavar='max_date', type=str, nargs='?', default="3M", + help='Get data from the request time between the request and the current day. (default 3M) Example: 9D = 9 day from the current time. (D= Day)') -args = parser.parse_args() +args = parser.parse_args([]) # Configuration timestamp_0 = 1367174841000 @@ -86,6 +90,31 @@ def LoopandFilterListData(html): data.append(datum) return data +def filterTimeFrom(df): + dataString = args.max_date + print(dataString) + number = args.max_date[:len(args.max_date)-1] + suffix = args.max_date[-1].upper() + if number.isdigit(): + number = int(num) + else: + logging.info("invalid max_date") + sys.exit() + if suffix.isdigit() + logging.info("invalid max_date") + sys.exit() + + if suffix == "D" + d = datetime.timedelta(day=number) + else: + logging.info("invalid string. Please retry with Y,M,D only.") + sys.exit() + + threeMonth = df['time'].iloc[-1] - d + df = df[df['time']>threeMonth] + + print(df.describe()) + def getDetailandGraphData(token): URL = "{0}/currencies/{1}/".format(graphBASE_URL, token['slug']) rawData = pd.read_json(URL) @@ -99,7 +128,7 @@ def getDetailandGraphData(token): rawData.to_csv("{0}.csv".format(token['slug']), sep=',',index=False) return rawData - + def main(): ##logging.info("Attempting to scrape token list.") tokens = scrapeTokenList() @@ -108,6 +137,8 @@ def main(): for token in tokens: logging.info("> Starting scrape of token {0}...".format(token['slug'])) df = getDetailandGraphData(token) + df = filterTimeFrom(df) + #TechnicalAnalysis(df) #rawData.to_csv("testing2.csv", sep=',',index=False) @@ -123,7 +154,9 @@ def main(): def testing(): df = pd.read_csv('eos.csv') - trading.technicalAnalysis(df) + + filterTimeFrom(df) + #trading.technicalAnalysis(df) #def main(): # d = get_historical_data(COIN) diff --git a/testing.csv b/testing.csv deleted file mode 100644 index bdb61cd..0000000 --- a/testing.csv +++ /dev/null @@ -1,875 +0,0 @@ -market_cap_by_available_supply,price_btc,price_platform,price_usd,volume_usd -"[1498936454000L, 0]","[1498936454000L, 0.000426253]","[1498936454000L, 0.00379662]","[1498936454000L, 1.03134]","[1498936454000L, 11487600]" -"[1498958054000L, 0]","[1498958054000L, 0.00039633800000000004]","[1498958054000L, 0.00348802]","[1498958054000L, 0.944058]","[1498958054000L, 15204800]" -"[1498979658000L, 0]","[1498979658000L, 0.00045288800000000003]","[1498979658000L, 0.00406351]","[1498979658000L, 1.08837]","[1498979658000L, 38076800]" -"[1499001258000L, 0]","[1499001258000L, 0.0008952270000000001]","[1499001258000L, 0.00796018]","[1499001258000L, 2.1641500000000002]","[1499001258000L, 186403000]" -"[1499022857000L, 0]","[1499022857000L, 0.0010195]","[1499022857000L, 0.008883]","[1499022857000L, 2.51268]","[1499022857000L, 288045000]" -"[1499044457000L, 0]","[1499044457000L, 0.00108336]","[1499044457000L, 0.0094503]","[1499044457000L, 2.68792]","[1499044457000L, 319367000]" -"[1499066057000L, 686570708]","[1499066057000L, 0.00183018]","[1499066057000L, 0.0158764]","[1499066057000L, 4.55874]","[1499066057000L, 642558000]" -"[1499087658000L, 643449379]","[1499087658000L, 0.00169443]","[1499087658000L, 0.015012]","[1499087658000L, 4.27242]","[1499087658000L, 559907000]" -"[1499109264000L, 731088773]","[1499109264000L, 0.00181076]","[1499109264000L, 0.0160112]","[1499109264000L, 4.57676]","[1499109264000L, 490624000]" -"[1499130865000L, 662755926]","[1499130865000L, 0.00161563]","[1499130865000L, 0.0145589]","[1499130865000L, 4.13384]","[1499130865000L, 413315000]" -"[1499152467000L, 609283534]","[1499152467000L, 0.00144793]","[1499152467000L, 0.0133009]","[1499152467000L, 3.7908600000000003]","[1499152467000L, 330011000]" -"[1499174062000L, 555705453]","[1499174062000L, 0.0013143]","[1499174062000L, 0.0120097]","[1499174062000L, 3.41847]","[1499174062000L, 218610000]" -"[1499195661000L, 549750298]","[1499195661000L, 0.00131627]","[1499195661000L, 0.0123001]","[1499195661000L, 3.37383]","[1499195661000L, 219852000]" -"[1499217260000L, 559910519]","[1499217260000L, 0.00134086]","[1499217260000L, 0.012677599999999999]","[1499217260000L, 3.43086]","[1499217260000L, 226655000]" -"[1499238861000L, 498227313]","[1499238861000L, 0.0012000700000000001]","[1499238861000L, 0.0112696]","[1499238861000L, 3.04534]","[1499238861000L, 157869000]" -"[1499260457000L, 478038837]","[1499260457000L, 0.00113447]","[1499260457000L, 0.0108504]","[1499260457000L, 2.88729]","[1499260457000L, 152274000]" -"[1499282058000L, 511621336]","[1499282058000L, 0.00119033]","[1499282058000L, 0.0112149]","[1499282058000L, 3.07509]","[1499282058000L, 136389000]" -"[1499303657000L, 505771988]","[1499303657000L, 0.00116917]","[1499303657000L, 0.011405499999999999]","[1499303657000L, 3.03382]","[1499303657000L, 118341000]" -"[1499325256000L, 555180889]","[1499325256000L, 0.00128362]","[1499325256000L, 0.012222499999999999]","[1499325256000L, 3.32472]","[1499325256000L, 207333000]" -"[1499346855000L, 565606461]","[1499346855000L, 0.0012963]","[1499346855000L, 0.0123279]","[1499346855000L, 3.35313]","[1499346855000L, 193226000]" -"[1499368455000L, 561282034]","[1499368455000L, 0.00128711]","[1499368455000L, 0.012384599999999999]","[1499368455000L, 3.32354]","[1499368455000L, 176423000]" -"[1499390055000L, 559315648]","[1499390055000L, 0.00127228]","[1499390055000L, 0.012242699999999999]","[1499390055000L, 3.30616]","[1499390055000L, 175115000]" -"[1499411659000L, 499884636]","[1499411659000L, 0.00115346]","[1499411659000L, 0.0111659]","[1499411659000L, 2.95269]","[1499411659000L, 99277500]" -"[1499433256000L, 484086511]","[1499433256000L, 0.00111741]","[1499433256000L, 0.010981999999999999]","[1499433256000L, 2.82233]","[1499433256000L, 86697500]" -"[1499454855000L, 497502532]","[1499454855000L, 0.0011488]","[1499454855000L, 0.011446699999999999]","[1499454855000L, 2.89719]","[1499454855000L, 94709000]" -"[1499476454000L, 481786693]","[1499476454000L, 0.0011272300000000001]","[1499476454000L, 0.0117097]","[1499476454000L, 2.80467]","[1499476454000L, 101184000]" -"[1499498055000L, 439476469]","[1499498055000L, 0.00101719]","[1499498055000L, 0.00989105]","[1499498055000L, 2.53562]","[1499498055000L, 98507600]" -"[1499519655000L, 392013151]","[1499519655000L, 0.000897496]","[1499519655000L, 0.00920763]","[1499519655000L, 2.25901]","[1499519655000L, 117757000]" -"[1499541259000L, 418580598]","[1499541259000L, 0.000953245]","[1499541259000L, 0.00973286]","[1499541259000L, 2.4111700000000003]","[1499541259000L, 140208000]" -"[1499562867000L, 508935221]","[1499562867000L, 0.00101264]","[1499562867000L, 0.0103396]","[1499562867000L, 2.5847100000000003]","[1499562867000L, 151693000]" -"[1499584464000L, 438227794]","[1499584464000L, 0.0008692810000000001]","[1499584464000L, 0.00877726]","[1499584464000L, 2.20519]","[1499584464000L, 125085000]" -"[1499606059000L, 483547562]","[1499606059000L, 0.0009588140000000001]","[1499606059000L, 0.00991363]","[1499606059000L, 2.42987]","[1499606059000L, 113731000]" -"[1499627659000L, 475501841]","[1499627659000L, 0.000941272]","[1499627659000L, 0.00976073]","[1499627659000L, 2.38726]","[1499627659000L, 95084800]" -"[1499649259000L, 448167175]","[1499649259000L, 0.000868603]","[1499649259000L, 0.00896638]","[1499649259000L, 2.17329]","[1499649259000L, 82378200]" -"[1499670858000L, 477008929]","[1499670858000L, 0.0009122900000000001]","[1499670858000L, 0.00944569]","[1499670858000L, 2.2915900000000002]","[1499670858000L, 81578500]" -"[1499692459000L, 458719573]","[1499692459000L, 0.000891424]","[1499692459000L, 0.00957481]","[1499692459000L, 2.2028]","[1499692459000L, 75407100]" -"[1499714073000L, 439399921]","[1499714073000L, 0.000875635]","[1499714073000L, 0.009442599999999999]","[1499714073000L, 2.10859]","[1499714073000L, 74646100]" -"[1499735684000L, 333344015]","[1499735684000L, 0.0006776250000000001]","[1499735684000L, 0.00742423]","[1499735684000L, 1.59938]","[1499735684000L, 73309900]" -"[1499757298000L, 325699654]","[1499757298000L, 0.000674441]","[1499757298000L, 0.00823462]","[1499757298000L, 1.5490400000000002]","[1499757298000L, 90397600]" -"[1499778941000L, 379452218]","[1499778941000L, 0.0007571850000000001]","[1499778941000L, 0.00855669]","[1499778941000L, 1.80375]","[1499778941000L, 123936000]" -"[1499800528000L, 347394771]","[1499800528000L, 0.0007081640000000001]","[1499800528000L, 0.00851701]","[1499800528000L, 1.6509200000000002]","[1499800528000L, 121721000]" -"[1499822109000L, 336783562]","[1499822109000L, 0.000702255]","[1499822109000L, 0.00826989]","[1499822109000L, 1.60034]","[1499822109000L, 104331000]" -"[1499870961000L, 369974219]","[1499870961000L, 0.000739906]","[1499870961000L, 0.00841985]","[1499870961000L, 1.74157]","[1499870961000L, 74999000]" -"[1499892563000L, 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0.000735677]","[1500023655000L, 0.00844574]","[1500023655000L, 1.72266]","[1500023655000L, 43155800]" -"[1500045255000L, 349740668]","[1500045255000L, 0.000708346]","[1500045255000L, 0.00824788]","[1500045255000L, 1.61468]","[1500045255000L, 42333800]" -"[1500066855000L, 343097209]","[1500066855000L, 0.00073392]","[1500066855000L, 0.00814823]","[1500066855000L, 1.58378]","[1500066855000L, 40580600]" -"[1500088455000L, 325927619]","[1500088455000L, 0.0006928310000000001]","[1500088455000L, 0.00774118]","[1500088455000L, 1.49126]","[1500088455000L, 39648700]" -"[1500110058000L, 316880460]","[1500110058000L, 0.000697826]","[1500110058000L, 0.007765520000000001]","[1500110058000L, 1.4494500000000001]","[1500110058000L, 36694700]" -"[1500131657000L, 294824065]","[1500131657000L, 0.000667836]","[1500131657000L, 0.007583980000000001]","[1500131657000L, 1.34847]","[1500131657000L, 32582100]" -"[1500153255000L, 302849974]","[1500153255000L, 0.000685315]","[1500153255000L, 0.00783584]","[1500153255000L, 1.38511]","[1500153255000L, 34864400]" -"[1500174857000L, 293093773]","[1500174857000L, 0.000651808]","[1500174857000L, 0.00774562]","[1500174857000L, 1.32921]","[1500174857000L, 35364400]" -"[1500196455000L, 282664093]","[1500196455000L, 0.000648916]","[1500196455000L, 0.00829982]","[1500196455000L, 1.28165]","[1500196455000L, 32698700]" -"[1500218057000L, 275382014]","[1500218057000L, 0.000652256]","[1500218057000L, 0.00821681]","[1500218057000L, 1.2476]","[1500218057000L, 33535700]" -"[1500239657000L, 268620522]","[1500239657000L, 0.000648909]","[1500239657000L, 0.00813011]","[1500239657000L, 1.21672]","[1500239657000L, 31352900]" -"[1500261257000L, 294814815]","[1500261257000L, 0.000676755]","[1500261257000L, 0.00822554]","[1500261257000L, 1.3260800000000001]","[1500261257000L, 35596800]" -"[1500282855000L, 306344481]","[1500282855000L, 0.0006691]","[1500282855000L, 0.00797904]","[1500282855000L, 1.3773900000000001]","[1500282855000L, 44027900]" -"[1500304456000L, 314198273]","[1500304456000L, 0.000677072]","[1500304456000L, 0.0081132]","[1500304456000L, 1.41246]","[1500304456000L, 50914200]" -"[1500326057000L, 330341434]","[1500326057000L, 0.0006788380000000001]","[1500326057000L, 0.00805733]","[1500326057000L, 1.47777]","[1500326057000L, 54734900]" -"[1500347655000L, 340394767]","[1500347655000L, 0.000692896]","[1500347655000L, 0.00815375]","[1500347655000L, 1.51835]","[1500347655000L, 57682300]" -"[1500369255000L, 337007988]","[1500369255000L, 0.000675723]","[1500369255000L, 0.00792705]","[1500369255000L, 1.50205]","[1500369255000L, 53296700]" -"[1500390855000L, 378905167]","[1500390855000L, 0.0007207810000000001]","[1500390855000L, 0.00798441]","[1500390855000L, 1.68852]","[1500390855000L, 55793800]" -"[1500412456000L, 380306268]","[1500412456000L, 0.0007175020000000001]","[1500412456000L, 0.00706502]","[1500412456000L, 1.68185]","[1500412456000L, 61154700]" -"[1500434055000L, 361391494]","[1500434055000L, 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0.013605599999999999]","[1517007555000L, 14.025]","[1517007555000L, 1405060000]" -"[1517029156000L, 8970392240L]","[1517029156000L, 0.00128079]","[1517029156000L, 0.0135365]","[1517029156000L, 14.2471]","[1517029156000L, 1437400000]" -"[1517050755000L, 9030681740L]","[1517050755000L, 0.00127775]","[1517050755000L, 0.013377]","[1517050755000L, 14.3424]","[1517050755000L, 1059050000]" -"[1517072356000L, 9198129820L]","[1517072356000L, 0.00127089]","[1517072356000L, 0.0132882]","[1517072356000L, 14.5707]","[1517072356000L, 712266000]" -"[1517093956000L, 9037283472L]","[1517093956000L, 0.00125642]","[1517093956000L, 0.012835899999999999]","[1517093956000L, 14.3118]","[1517093956000L, 674098000]" -"[1517115556000L, 9436049637L]","[1517115556000L, 0.00128446]","[1517115556000L, 0.0131563]","[1517115556000L, 14.9405]","[1517115556000L, 595633000]" -"[1517137156000L, 9248114827L]","[1517137156000L, 0.00125072]","[1517137156000L, 0.0122151]","[1517137156000L, 14.6017]","[1517137156000L, 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0.00114776]","[1517417956000L, 0.0104154]","[1517417956000L, 11.4184]","[1517417956000L, 1264270000]" -"[1517439556000L, 7650048823L]","[1517439556000L, 0.00118507]","[1517439556000L, 0.0108883]","[1517439556000L, 11.9604]","[1517439556000L, 1027980000]" -"[1517461157000L, 7581401290L]","[1517461157000L, 0.0011710800000000001]","[1517461157000L, 0.0104265]","[1517461157000L, 11.8526]","[1517461157000L, 669542000]" -"[1517482757000L, 6899427477L]","[1517482757000L, 0.00112914]","[1517482757000L, 0.00985768]","[1517482757000L, 10.7567]","[1517482757000L, 684225000]" -"[1517504356000L, 6536387104L]","[1517504356000L, 0.00112103]","[1517504356000L, 0.00980057]","[1517504356000L, 10.1878]","[1517504356000L, 832823000]" -"[1517525955000L, 6517361003L]","[1517525955000L, 0.00113127]","[1517525955000L, 0.010135499999999999]","[1517525955000L, 10.1579]","[1517525955000L, 918500000]" -"[1517547557000L, 5795554746L]","[1517547557000L, 0.0010385400000000001]","[1517547557000L, 0.00949345]","[1517547557000L, 9.01305]","[1517547557000L, 1011310000]" -"[1517569156000L, 5440560514L]","[1517569156000L, 0.0010264900000000001]","[1517569156000L, 0.00976439]","[1517569156000L, 8.45526]","[1517569156000L, 1014140000]" -"[1517590756000L, 6536835725L]","[1517590756000L, 0.00111655]","[1517590756000L, 0.0105833]","[1517590756000L, 10.1571]","[1517590756000L, 1559500000]" -"[1517612357000L, 6300673439L]","[1517612357000L, 0.00111345]","[1517612357000L, 0.010811]","[1517612357000L, 9.79]","[1517612357000L, 1527260000]" -"[1517633956000L, 5767384811L]","[1517633956000L, 0.0010628200000000001]","[1517633956000L, 0.010153599999999999]","[1517633956000L, 8.94049]","[1517633956000L, 1258620000]" -"[1517655557000L, 6146595121L]","[1517655557000L, 0.0010794700000000001]","[1517655557000L, 0.0101256]","[1517655557000L, 9.52261]","[1517655557000L, 1201570000]" -"[1517677156000L, 6443516627L]","[1517677156000L, 0.00107426]","[1517677156000L, 0.010222]","[1517677156000L, 9.98151]","[1517677156000L, 832326000]" -"[1517698756000L, 6334681981L]","[1517698756000L, 0.00107408]","[1517698756000L, 0.0102135]","[1517698756000L, 9.81265]","[1517698756000L, 739663000]" -"[1517720357000L, 6356436341L]","[1517720357000L, 0.0010754999999999999]","[1517720357000L, 0.0103217]","[1517720357000L, 9.82182]","[1517720357000L, 637931000]" -"[1517741958000L, 6189362245L]","[1517741958000L, 0.00108704]","[1517741958000L, 0.0106614]","[1517741958000L, 9.56075]","[1517741958000L, 602007000]" -"[1517763556000L, 5764147129L]","[1517763556000L, 0.00105837]","[1517763556000L, 0.0102654]","[1517763556000L, 8.90116]","[1517763556000L, 538134000]" -"[1517785157000L, 5734677112L]","[1517785157000L, 0.00105851]","[1517785157000L, 0.0105551]","[1517785157000L, 8.85533]","[1517785157000L, 604454000]" -"[1517806756000L, 5656501417L]","[1517806756000L, 0.00105234]","[1517806756000L, 0.0102899]","[1517806756000L, 8.71108]","[1517806756000L, 660498000]" -"[1517814857000L, 5551953402L]","[1517814857000L, 0.0010521999999999999]","[1517814857000L, 0.0102787]","[1517814857000L, 8.54752]","[1517814857000L, 631376000]" diff --git a/tradingStrategies.py b/tradingStrategies.py index f048aa8..da4ed4f 100644 --- a/tradingStrategies.py +++ b/tradingStrategies.py @@ -4,10 +4,48 @@ import numpy as np import matplotlib.pyplot as plt import matplotlib.dates as mdates -%matplotlib inline + +from sklearn import linear_model + +#uncomment on Jupyter Notebook +#from IPython import get_ipython +#get_ipython().run_line_magic('matplotlib', 'inline') + +#90 60 +#60 30 #Load Data for csv file #Use the T-Lines +def linearRegression(df): + n = len(df['time']) + X = df['time'] + Y = df['price_usd'] + lm = LinearRegression() + lm.fit(X,Y) + coef90 = lm.coef_ + + if n >= 60: + X = X[n-60:] + Y = Y[n-60:] + lm.fit(X,Y) + coef60 = lm.coef_ + + if n >= 30: + X = X[n-30:] + Y = Y[n-30:] + lm.fit(X,Y) + coef30 = lm.coef_ + + X = X[n-7:] + Y = Y[n-7:] + lm.fit(X,Y) + coef7 = lm.coef_ + + if coef90 > 0 and coef60 > 0 and coef30 > 0 and coef7 < 0: + return "Dip Buy Potential" + if coef90 > 0 and coef60 > 0 and coef30 > 0 and coef7 > 0: + return "Potential Future Buy" + def TlinesAnalysis(df): # TODO LIST # 1. Filter and cutdown data Within the past 3 months @@ -25,38 +63,32 @@ def TlinesAnalysis(df): # Rollover - happen when the price can go over the T-line, high possibility of it will drop # https://www.investopedia.com/ask/answers/122314/what-exponential-moving-average-ema-formula-and-how-ema-calculated.asp # https://blog.quantopian.com/a-professional-quant-equity-workflow/ - threeMonth = df['time'].iloc[-1] - datetime.timedelta(days=90) - df = df[df['time']>threeMonth] - print(df.describe()) + def EMA8DAY(df): #format the date and calculate the 8 day simple average average fig = plt.figure(figsize=(15,9)) ax = fig.add_subplot(1,1,1) + short_rolling = df.rolling(window=8).mean() + start = df['time'][0] + end = df['time'][-1] + start_date = '2015-01-01' # #whatever we set it to be + end_date = '2016-12-31' #whatever we set it to be - my_year_month_fmt = mdates.DateFormatter('%m/%y') - short_rolling = df.rolling(window=8).mean() - start = df['time'][0] - end = df['time'][-1] - - start_date = '2015-01-01' # #whatever we set it to be - end_date = '2016-12-31' #whatever we set it to be - - ax.plot(short_rolling.ix[start_date:end_date, :].index, short_rolling.ix[start_date:end_date, 'MSFT'], label = '8-days SMA') - ema_short = df.ewm(span=8, adjust=False).mean() + ax.plot(short_rolling.ix[start_date:end_date, :].index, short_rolling.ix[start_date:end_date, 'MSFT'], label = '8-days SMA') + ema_short = df.ewm(span=8, adjust=False).mean() #Taking the different between the prices and the EMA timeseries - trading_positions_raw = data - ema_short + trading_positions_raw = data - ema_short - ax.plot(data.ix[start_date:end_date, :].index, data.ix[start_date:end_date, 'MSFT'], label='Price') + ax.plot(data.ix[start_date:end_date, :].index, data.ix[start_date:end_date, 'MSFT'], label='Price') ax.plot(ema_short.ix[start_date:end_date, :].index, ema_short.ix[start_date:end_date, 'MSFT'], label = 'Span 8-days EMA') ax.legend(loc='best') ax.set_ylabel('Price in $') ax.grid() #set grid - year_month_format = mdates.DateFormatter('%d/%m/%y') ax.xaxis.set_major_fomratter(year_month_format) @@ -64,6 +96,17 @@ def EMA8DAY(df): #When the price timeseries p(t) crosses the EMA timeseries e(t) from above, we will close any existing long position and go short (sell) one unit of the asset. + #Only Buy when it has a candlestick buy signal and the price close above the t -line + + #Only sell when it has a sell candlestick signal and price price close the t-line + + #Caveats: + #higher move away from the t-line the high the possiblities it move away from t-line then look for a sell signal + + #T-line rollover - when a stock is having indecisive trading and can close above the T-Line - time to short if you can, or get out + + + #Custom trading Prediction for 8EMA trading_positions = trading_positions_raw.apply(np.sign) * 1 # Lagging our trading signals by one day. diff --git a/twitterAPI.py b/twitterAPI.py index 2cd79f8..7659ec5 100644 --- a/twitterAPI.py +++ b/twitterAPI.py @@ -3,16 +3,19 @@ from tweepy import OAuthHandler from textblob import TextBlob from secret_settings import * +import json +import datetime as dt +import time +import os +import sys class TwitterClient(object): def __init__(self): - # keys and tokens from the Twitter Dev Console - consumer_key = CONSUMER_KEY - consumer_secret = CONSUMER_SECRET - access_token = ACCESS_TOKEN - access_token_secret = ACCESS_TOKEN_SECRET - + consumer_key = os.environ['CONSUMER_KEY'] + consumer_secret = os.environ['CONSUMER_SECRET'] + access_token = os.environ['ACCESS_TOKEN'] + access_token_secret = os.environ['ACCESS_TOKEN_SECRET'] # attempt authentication try: # create OAuthHandler object @@ -81,12 +84,68 @@ def get_tweets(self, query, count = 10): except tweepy.TweepError as e: # print error (if any) print("Error : " + str(e)) - -def main(): + +def tweet_search(api, query, max_tweets, max_id, since_id, geocode='39.8,-95.583068847656,2500km'): + ''' Function that takes in a search string 'query', the maximum + number of tweets 'max_tweets', and the minimum (i.e., starting) + tweet id. It returns a list of tweepy.models.Status objects. ''' + + searched_tweets = [] + while len(searched_tweets) &lt; max_tweets: + remaining_tweets = max_tweets - len(searched_tweets) + try: + new_tweets = api.search(q=query, count=remaining_tweets, + since_id=str(since_id), + max_id=str(max_id-1)) +# geocode=geocode) + print('found',len(new_tweets),'tweets') + if not new_tweets: + print('no tweets found') + break + searched_tweets.extend(new_tweets) + max_id = new_tweets[-1].id + except tweepy.TweepError: + print('exception raised, waiting 15 minutes') + print('(until:', dt.datetime.now()+dt.timedelta(minutes=15), ')') + time.sleep(15*60) + break # stop the loop + return searched_tweets, max_id + +def get_tweet_id(api, date='', days_ago=9, query='a'): + ''' Function that gets the ID of a tweet. This ID can + then be used as a 'starting point' from which to + search. The query is required and has been set to + a commonly used word by default. The variable + 'days_ago' has been initialized to the maximum amount + we are able to search back in time (9).''' + + if date: # return an ID from the start of the given day + td = date + dt.timedelta(days=1) + tweet_date = '{0}-{1:0&gt;2}-{2:0&gt;2}'.format(td.year, td.month, td.day) + tweet = api.search(q=query, count=1, until=tweet_date) + else: + # return an ID from __ days ago + td = dt.datetime.now() - dt.timedelta(days=days_ago) + tweet_date = '{0}-{1:0&gt;2}-{2:0&gt;2}'.format(td.year, td.month, td.day) + # get list of up to 10 tweets + tweet = api.search(q=query, count=10, until=tweet_date) + print('search limit (start/stop):',tweet[0].created_at) + # return the id of the first tweet in the list + return tweet[0].id + +def sentimentAnalysis(slug): # creating object of TwitterClient Class api = TwitterClient() # calling function to get tweets #name of the coins + search_name = slug + time_limit = 24 # runtime limit in hours + min_days_old, max_days_old = 1, 2 # search limits + + # e.g. min_days_old, max_days_old = 7, 8 + # gives the current weekday from last week, + # min_days_old=0 will search from right now + tweets = api.get_tweets(query = "XLM" , count = 200) # picking positive tweets from tweets @@ -112,5 +171,4 @@ def main(): print(tweet['text']) if __name__ == "__main__": - # calling main function - main() \ No newline at end of file + sentimentAnalysis() \ No newline at end of file From 186fd03468ef9f2832bdf101b11ab3f732415aa5 Mon Sep 17 00:00:00 2001 From: ptang0917 Date: Sun, 25 Feb 2018 15:49:00 -0500 Subject: [PATCH 2/7] added def VolumeAnalyst --- tradingStrategies.py | 115 ++++++++++++++++++++----------------------- 1 file changed, 54 insertions(+), 61 deletions(-) diff --git a/tradingStrategies.py b/tradingStrategies.py index da4ed4f..3d227fd 100644 --- a/tradingStrategies.py +++ b/tradingStrategies.py @@ -4,48 +4,10 @@ import numpy as np import matplotlib.pyplot as plt import matplotlib.dates as mdates - -from sklearn import linear_model - -#uncomment on Jupyter Notebook -#from IPython import get_ipython -#get_ipython().run_line_magic('matplotlib', 'inline') - -#90 60 -#60 30 +%matplotlib inline #Load Data for csv file #Use the T-Lines -def linearRegression(df): - n = len(df['time']) - X = df['time'] - Y = df['price_usd'] - lm = LinearRegression() - lm.fit(X,Y) - coef90 = lm.coef_ - - if n >= 60: - X = X[n-60:] - Y = Y[n-60:] - lm.fit(X,Y) - coef60 = lm.coef_ - - if n >= 30: - X = X[n-30:] - Y = Y[n-30:] - lm.fit(X,Y) - coef30 = lm.coef_ - - X = X[n-7:] - Y = Y[n-7:] - lm.fit(X,Y) - coef7 = lm.coef_ - - if coef90 > 0 and coef60 > 0 and coef30 > 0 and coef7 < 0: - return "Dip Buy Potential" - if coef90 > 0 and coef60 > 0 and coef30 > 0 and coef7 > 0: - return "Potential Future Buy" - def TlinesAnalysis(df): # TODO LIST # 1. Filter and cutdown data Within the past 3 months @@ -63,32 +25,70 @@ def TlinesAnalysis(df): # Rollover - happen when the price can go over the T-line, high possibility of it will drop # https://www.investopedia.com/ask/answers/122314/what-exponential-moving-average-ema-formula-and-how-ema-calculated.asp # https://blog.quantopian.com/a-professional-quant-equity-workflow/ - - + threeMonth = df['time'].iloc[-1] - datetime.timedelta(days=90) + df = df[df['time']>threeMonth] + + print(df.describe()) + +def VolumeAnalyst(df): + pricearray = df['price_usd'] + volumearray = df['volume_usd'] + positivelist = [] + negativelist = [] + sumpositive = 0 + sumnegative = 0 + totaloverallsum = 0 + percentage = 0 + setpercentage = .50 + #For the sake of the testing, we will do up to 20 'datapoints' + for x in 20: + difference = (pricearray[x+1] - pricearray[x])*volumearray[x] + if difference > 0: + positivelist.append(difference) + elif difference < 0: + negativelist.append(difference) + + for x in positivelist: + sumpositive += positivelist[x] + for x in negativelist: + sumnegative += negativelist[x] + totaloverallsum = sumpositive+sumnegative + percentage = ((sumpositive - sumnegative) / totaloverallsum) * 100 + if abs(percentage) > setpercentage: + if percentage > 0: + print("A spike has occurred ") + elif percentage: + print("A drop has occured") + elif: + print("No drastic spike / drop has occured") def EMA8DAY(df): #format the date and calculate the 8 day simple average average fig = plt.figure(figsize=(15,9)) ax = fig.add_subplot(1,1,1) - short_rolling = df.rolling(window=8).mean() - start = df['time'][0] - end = df['time'][-1] - start_date = '2015-01-01' # #whatever we set it to be - end_date = '2016-12-31' #whatever we set it to be - ax.plot(short_rolling.ix[start_date:end_date, :].index, short_rolling.ix[start_date:end_date, 'MSFT'], label = '8-days SMA') - ema_short = df.ewm(span=8, adjust=False).mean() + my_year_month_fmt = mdates.DateFormatter('%m/%y') + short_rolling = df.rolling(window=8).mean() + start = df['time'][0] + end = df['time'][-1] + + start_date = '2015-01-01' # #whatever we set it to be + end_date = '2016-12-31' #whatever we set it to be + + ax.plot(short_rolling.ix[start_date:end_date, :].index, short_rolling.ix[start_date:end_date, 'MSFT'], label = '8-days SMA') + ema_short = df.ewm(span=8, adjust=False).mean() #Taking the different between the prices and the EMA timeseries - trading_positions_raw = data - ema_short + trading_positions_raw = data - ema_short - ax.plot(data.ix[start_date:end_date, :].index, data.ix[start_date:end_date, 'MSFT'], label='Price') + ax.plot(data.ix[start_date:end_date, :].index, data.ix[start_date:end_date, 'MSFT'], label='Price') ax.plot(ema_short.ix[start_date:end_date, :].index, ema_short.ix[start_date:end_date, 'MSFT'], label = 'Span 8-days EMA') ax.legend(loc='best') ax.set_ylabel('Price in $') ax.grid() #set grid + year_month_format = mdates.DateFormatter('%d/%m/%y') ax.xaxis.set_major_fomratter(year_month_format) @@ -96,23 +96,16 @@ def EMA8DAY(df): #When the price timeseries p(t) crosses the EMA timeseries e(t) from above, we will close any existing long position and go short (sell) one unit of the asset. - #Only Buy when it has a candlestick buy signal and the price close above the t -line - - #Only sell when it has a sell candlestick signal and price price close the t-line - - #Caveats: - #higher move away from the t-line the high the possiblities it move away from t-line then look for a sell signal - - #T-line rollover - when a stock is having indecisive trading and can close above the T-Line - time to short if you can, or get out - - - #Custom trading Prediction for 8EMA trading_positions = trading_positions_raw.apply(np.sign) * 1 # Lagging our trading signals by one day. trading_positions_final = trading_positions.shift(1) #Find the log of the prices is taken and the difference of the consecutive log observations + #Predict the price + #Above the Tline -> Sell + #Below the Tline -> Buy + asset_log_returns = np.log(data).diff() strategy_asset_log_returns = trading_positions_final * asset_log_returns From 63308abf73e279a878f682fb8e2cc406e4771ef5 Mon Sep 17 00:00:00 2001 From: ptang0917 Date: Sun, 25 Feb 2018 16:50:40 -0500 Subject: [PATCH 3/7] Update scrape.py --- scrape.py | 92 +++++++++++++++++++++++++++++++++++++++++-------------- 1 file changed, 69 insertions(+), 23 deletions(-) diff --git a/scrape.py b/scrape.py index cf786e3..5a6c82b 100644 --- a/scrape.py +++ b/scrape.py @@ -5,6 +5,7 @@ import requests import time from datetime import datetime +# from datetime import timedelta from random import random import logging import pandas as pd @@ -16,7 +17,7 @@ parser = argparse.ArgumentParser(description='Scraping Tokens and Coins') parser.add_argument('min_market_cap', metavar='min_cap', type=int, nargs='?', default=0, help='minimum market cap [usd] for currency to be scraped (default: scrape all)') -parser.add_argument('max_date', metavar='max_date', type=str, nargs='?', default="3M", +parser.add_argument('max_date', metavar='max_date', type=str, nargs='?', default="90D", help='Get data from the request time between the request and the current day. (default 3M) Example: 9D = 9 day from the current time. (D= Day)') args = parser.parse_args([]) @@ -91,29 +92,73 @@ def LoopandFilterListData(html): return data def filterTimeFrom(df): - dataString = args.max_date - print(dataString) - number = args.max_date[:len(args.max_date)-1] - suffix = args.max_date[-1].upper() - if number.isdigit(): - number = int(num) + # dataString = args.max_date + # print(dataString) + # number = args.max_date[:len(args.max_date)-1] + # suffix = args.max_date[-1].upper() + # if number.isdigit(): + # number = int(number) + # else: + # logging.info("invalid max_date") + # sys.exit() + # if suffix.isdigit(): + # logging.info("invalid max_date") + # sys.exit() + + # if suffix == "D": + # d = timedelta(days=number) + # else: + # logging.info("invalid string. Please retry with Y,M,D only.") + # sys.exit() + + # threeMonth = int(df['time'].iloc[-1]) - d + # print(threeMonth) + # df = df[int(df['time'])>threeMonth] + + # #print(df.describe()) + currmonth = datetime.now().month + year = datetime.now().year + x = currmonth - 3 + if(x > 0 and x < 10): + s = "0"+str(x) + df3 = df[df['time'].str.contains(str(year)+"-"+s)] + elif(x < 0): + x += 12 + year -= 1 + df3 = df[df['time'].str.contains(str(year)+"-"+str(x))] else: - logging.info("invalid max_date") - sys.exit() - if suffix.isdigit() - logging.info("invalid max_date") - sys.exit() - - if suffix == "D" - d = datetime.timedelta(day=number) + df3 = df[df['time'].str.contains(str(year)+"-"+str(x))] + + x+=1 + if(x>12): + x-=12 + year+=1 + + if(x<10): + s = "0"+str(x) + df4 = df[df['time'].str.contains(str(year)+"-"+s)] else: - logging.info("invalid string. Please retry with Y,M,D only.") - sys.exit() + df4 = df[df['time'].str.contains(str(year)+"-"+str(x))] + + x+=1 + if(x>12): + x-=12 + year+=1 + if(x<10): + s = "0"+str(x) + df5 = df[df['time'].str.contains(str(year)+"-"+s)] + else: + df5 = df[df['time'].str.contains(str(year)+"-"+str(x))] + + frames = [df3,df4,df5] + result = pd.concat(frames) + df = result - threeMonth = df['time'].iloc[-1] - d - df = df[df['time']>threeMonth] - print(df.describe()) + + + + return df def getDetailandGraphData(token): URL = "{0}/currencies/{1}/".format(graphBASE_URL, token['slug']) @@ -153,10 +198,11 @@ def main(): #print coins def testing(): + df = pd.read_csv('eos.csv') - filterTimeFrom(df) - #trading.technicalAnalysis(df) + df = filterTimeFrom(df) + trading.volumeAnalysis(df) #def main(): # d = get_historical_data(COIN) @@ -170,4 +216,4 @@ def testing(): if __name__=='__main__': #main() - testing() \ No newline at end of file + testing() From e2ce0f85443171bf8f3bb3f06d52ce1021027d65 Mon Sep 17 00:00:00 2001 From: ptang0917 Date: Thu, 8 Mar 2018 16:35:27 -0500 Subject: [PATCH 4/7] Update tradingStrategies.py --- tradingStrategies.py | 77 ++++++++++++++++++++++---------------------- 1 file changed, 38 insertions(+), 39 deletions(-) diff --git a/tradingStrategies.py b/tradingStrategies.py index 3d227fd..f2ba514 100644 --- a/tradingStrategies.py +++ b/tradingStrategies.py @@ -4,7 +4,7 @@ import numpy as np import matplotlib.pyplot as plt import matplotlib.dates as mdates -%matplotlib inline +#%matplotlib inline #Load Data for csv file #Use the T-Lines @@ -30,60 +30,59 @@ def TlinesAnalysis(df): print(df.describe()) -def VolumeAnalyst(df): - pricearray = df['price_usd'] - volumearray = df['volume_usd'] +def volumeAnalysis(df): + positivelist = [] negativelist = [] - sumpositive = 0 - sumnegative = 0 totaloverallsum = 0 - percentage = 0 + #set percentage is to determine if the time period of price values is spiking or drop or just a plateau setpercentage = .50 - #For the sake of the testing, we will do up to 20 'datapoints' - for x in 20: - difference = (pricearray[x+1] - pricearray[x])*volumearray[x] + # For the sake of the testing, we will do up to 20 'datapoints' + for x in range(0,20): + difference = (float(df.iloc[x+1]['price_usd']) - float(df.iloc[x]['price_usd']))*float(df.iloc[x]['volume_usd']) + + if difference > 0: positivelist.append(difference) elif difference < 0: - negativelist.append(difference) - - for x in positivelist: - sumpositive += positivelist[x] - for x in negativelist: - sumnegative += negativelist[x] - totaloverallsum = sumpositive+sumnegative - percentage = ((sumpositive - sumnegative) / totaloverallsum) * 100 - if abs(percentage) > setpercentage: - if percentage > 0: - print("A spike has occurred ") - elif percentage: - print("A drop has occured") - elif: - print("No drastic spike / drop has occured") + negativelist.append(difference) + + #cool way to calculate the sum of the positve and negative lists respectively + sumpositive = sum(map(float,positivelist)) + sumnegative = sum(map(float,negativelist)) + + + totaloverallsum = sumpositive+sumnegative + percentage = ((sumpositive - sumnegative) / totaloverallsum) * 100 + if abs(percentage) > setpercentage: + if percentage > 0: + print("A spike has occured from " + df.iloc[0]['time'] + " to " + df.iloc[20]['time']) + + elif percentage < 0: + print("A drop has occured from " + df.iloc[0]['time'] + " to " + df.iloc[20]['time']) + else: + print("No drop has occured from " + df.iloc[0]['time'] + " to " + df.iloc[20]['time']) + + def EMA8DAY(df): #format the date and calculate the 8 day simple average average fig = plt.figure(figsize=(15,9)) ax = fig.add_subplot(1,1,1) - - - my_year_month_fmt = mdates.DateFormatter('%m/%y') - short_rolling = df.rolling(window=8).mean() - start = df['time'][0] - end = df['time'][-1] - - start_date = '2015-01-01' # #whatever we set it to be - end_date = '2016-12-31' #whatever we set it to be - - ax.plot(short_rolling.ix[start_date:end_date, :].index, short_rolling.ix[start_date:end_date, 'MSFT'], label = '8-days SMA') - ema_short = df.ewm(span=8, adjust=False).mean() + my_year_month_fmt = mdates.DateFormatter('%m/%y') + short_rolling = df.rolling(window=8).mean() + start = df['time'][0] + end = df['time'][-1] + start_date = '2015-01-01' # #whatever we set it to be + end_date = '2016-12-31' #whatever we set it to be + ax.plot(short_rolling.ix[start_date:end_date, :].index, short_rolling.ix[start_date:end_date, 'MSFT'], label = '8-days SMA') + ema_short = df.ewm(span=8, adjust=False).mean() #Taking the different between the prices and the EMA timeseries - trading_positions_raw = data - ema_short + trading_positions_raw = data - ema_short - ax.plot(data.ix[start_date:end_date, :].index, data.ix[start_date:end_date, 'MSFT'], label='Price') + ax.plot(data.ix[start_date:end_date, :].index, data.ix[start_date:end_date, 'MSFT'], label='Price') ax.plot(ema_short.ix[start_date:end_date, :].index, ema_short.ix[start_date:end_date, 'MSFT'], label = 'Span 8-days EMA') ax.legend(loc='best') ax.set_ylabel('Price in $') From 39fd02a118a18b5afef1e7f81fd660343e444d65 Mon Sep 17 00:00:00 2001 From: Junjie Jiang Date: Sat, 17 Mar 2018 18:43:29 -0400 Subject: [PATCH 5/7] test --- scrape.py | 28 +++------- tradingStrategies.py | 126 ++++++++++++++++++++++--------------------- 2 files changed, 72 insertions(+), 82 deletions(-) diff --git a/scrape.py b/scrape.py index cf786e3..db46267 100644 --- a/scrape.py +++ b/scrape.py @@ -4,7 +4,7 @@ import argparse import requests import time -from datetime import datetime +import datetime from random import random import logging import pandas as pd @@ -16,8 +16,8 @@ parser = argparse.ArgumentParser(description='Scraping Tokens and Coins') parser.add_argument('min_market_cap', metavar='min_cap', type=int, nargs='?', default=0, help='minimum market cap [usd] for currency to be scraped (default: scrape all)') -parser.add_argument('max_date', metavar='max_date', type=str, nargs='?', default="3M", - help='Get data from the request time between the request and the current day. (default 3M) Example: 9D = 9 day from the current time. (D= Day)') +parser.add_argument('max_date', metavar='max_date', type=str, nargs='?', default="90", + help='Get data from the request time between the request and the current day. (default 90) Example: 90 day from the current time. (D= Day)') args = parser.parse_args([]) @@ -92,33 +92,21 @@ def LoopandFilterListData(html): def filterTimeFrom(df): dataString = args.max_date - print(dataString) - number = args.max_date[:len(args.max_date)-1] - suffix = args.max_date[-1].upper() + number = args.max_date + if number.isdigit(): - number = int(num) + number = int(number) else: logging.info("invalid max_date") sys.exit() - if suffix.isdigit() - logging.info("invalid max_date") - sys.exit() - - if suffix == "D" - d = datetime.timedelta(day=number) - else: - logging.info("invalid string. Please retry with Y,M,D only.") - sys.exit() - + d = datetime.timedelta(days=number).total_seconds()*1000 threeMonth = df['time'].iloc[-1] - d df = df[df['time']>threeMonth] - print(df.describe()) - def getDetailandGraphData(token): URL = "{0}/currencies/{1}/".format(graphBASE_URL, token['slug']) rawData = pd.read_json(URL) - #rawData['time'] = rawData['market_cap_by_available_supply'].apply(lambda x: datetime.utcfromtimestamp(float(x[0]/1000)).strftime('%Y-%m-%d %H:%M:%S')) #convert UNIX TIMEstamp into readable data + #rawData['time'] = rawData['market_cap_by_available_supply'].apply(lambda x: datetime.datetime.utcfromtimestamp(float(x[0]/1000)).strftime('%Y-%m-%d %H:%M:%S')) #convert UNIX TIMEstamp into readable data rawData['time'] = rawData['market_cap_by_available_supply'].apply(lambda x: x[0]) rawData['market_cap_by_available_supply'] = rawData['market_cap_by_available_supply'].apply(lambda x: x[1]) rawData['price_btc'] = rawData['price_btc'].apply(lambda x: x[1]) diff --git a/tradingStrategies.py b/tradingStrategies.py index 3d227fd..551870f 100644 --- a/tradingStrategies.py +++ b/tradingStrategies.py @@ -4,63 +4,65 @@ import numpy as np import matplotlib.pyplot as plt import matplotlib.dates as mdates -%matplotlib inline - +# %matplotlib +# from IPython import get_ipython +# get_ipython().run_line_magic('matplotlib', 'inline') #Load Data for csv file #Use the T-Lines def TlinesAnalysis(df): -# TODO LIST -# 1. Filter and cutdown data Within the past 3 months -# Analysis the trendline Linear Regression -# Compare the peaks and High highs and low lows -# The total volumn between spikes -# Swing Trading Points -# Always enter a trade with a clear trading plan, the four key elements of which are a target, a limit, a stop loss and an add-on point. -# Always align your trade with the overall direction of the market. -# focus on the 6-month daily chart. Here, I can see finer details that the weekly chart obscures. -# look for volume dries up at lows -# Dont get caught up in the coin or company. -# Use a T-Line trading strategy. -8-day exponential moving average -# the farther away from the T-line, the high the possible of it going back to the T-Line -# Rollover - happen when the price can go over the T-line, high possibility of it will drop -# https://www.investopedia.com/ask/answers/122314/what-exponential-moving-average-ema-formula-and-how-ema-calculated.asp -# https://blog.quantopian.com/a-professional-quant-equity-workflow/ - threeMonth = df['time'].iloc[-1] - datetime.timedelta(days=90) - df = df[df['time']>threeMonth] - - print(df.describe()) - -def VolumeAnalyst(df): - pricearray = df['price_usd'] - volumearray = df['volume_usd'] +# TODO LIST +# 1. Filter and cutdown data Within the past 3 months +# Analysis the trendline Linear Regression +# Compare the peaks and High highs and low lows +# The total volumn between spikes +# Swing Trading Points +# Always enter a trade with a clear trading plan, the four key elements of which are a target, a limit, a stop loss and an add-on point. +# Always align your trade with the overall direction of the market. +# focus on the 6-month daily chart. Here, I can see finer details that the weekly chart obscures. +# look for volume dries up at lows +# Dont get caught up in the coin or company. +# Use a T-Line trading strategy. -8-day exponential moving average +# the farther away from the T-line, the high the possible of it going back to the T-Line +# Rollover - happen when the price can go over the T-line, high possibility of it will drop +# https://www.investopedia.com/ask/answers/122314/what-exponential-moving-average-ema-formula-and-how-ema-calculated.asp +# https://blog.quantopian.com/a-professional-quant-equity-workflow/ + threeMonth = df['time'].iloc[-1] - datetime.timedelta(days=90) + df = df[df['time']>threeMonth] + + print(df.describe()) + +def volumeAnalysis(df): + positivelist = [] negativelist = [] - sumpositive = 0 - sumnegative = 0 totaloverallsum = 0 - percentage = 0 + #set percentage is to determine if the time period of price values is spiking or drop or just a plateau setpercentage = .50 - #For the sake of the testing, we will do up to 20 'datapoints' - for x in 20: - difference = (pricearray[x+1] - pricearray[x])*volumearray[x] + # For the sake of the testing, we will do up to 20 'datapoints' + for x in range(0,20): + difference = (float(df.iloc[x+1]['price_usd']) - float(df.iloc[x]['price_usd']))*float(df.iloc[x]['volume_usd']) + + if difference > 0: positivelist.append(difference) elif difference < 0: - negativelist.append(difference) - - for x in positivelist: - sumpositive += positivelist[x] - for x in negativelist: - sumnegative += negativelist[x] - totaloverallsum = sumpositive+sumnegative - percentage = ((sumpositive - sumnegative) / totaloverallsum) * 100 - if abs(percentage) > setpercentage: - if percentage > 0: - print("A spike has occurred ") - elif percentage: - print("A drop has occured") - elif: - print("No drastic spike / drop has occured") + negativelist.append(difference) + + #cool way to calculate the sum of the positve and negative lists respectively + sumpositive = sum(map(float,positivelist)) + sumnegative = sum(map(float,negativelist)) + + + totaloverallsum = sumpositive+sumnegative + percentage = ((sumpositive - sumnegative) / totaloverallsum) * 100 + if abs(percentage) > setpercentage: + if percentage > 0: + print("A spike has occured from " + df.iloc[0]['time'] + " to " + df.iloc[20]['time']) + + elif percentage < 0: + print("A drop has occured from " + df.iloc[0]['time'] + " to " + df.iloc[20]['time']) + else: + print("No drop has occured from " + df.iloc[0]['time'] + " to " + df.iloc[20]['time']) def EMA8DAY(df): @@ -69,21 +71,21 @@ def EMA8DAY(df): ax = fig.add_subplot(1,1,1) - my_year_month_fmt = mdates.DateFormatter('%m/%y') - short_rolling = df.rolling(window=8).mean() - start = df['time'][0] - end = df['time'][-1] + my_year_month_fmt = mdates.DateFormatter('%m/%y') + short_rolling = df.rolling(window=8).mean() + start = df['time'][0] + end = df['time'][-1] - start_date = '2015-01-01' # #whatever we set it to be - end_date = '2016-12-31' #whatever we set it to be + start_date = '2015-01-01' # #whatever we set it to be + end_date = '2016-12-31' #whatever we set it to be - ax.plot(short_rolling.ix[start_date:end_date, :].index, short_rolling.ix[start_date:end_date, 'MSFT'], label = '8-days SMA') - ema_short = df.ewm(span=8, adjust=False).mean() + ax.plot(short_rolling.ix[start_date:end_date, :].index, short_rolling.ix[start_date:end_date, 'MSFT'], label = '8-days SMA') + ema_short = df.ewm(span=8, adjust=False).mean() - #Taking the different between the prices and the EMA timeseries - trading_positions_raw = data - ema_short + #Taking the different between the prices and the EMA timeseries + trading_positions_raw = data - ema_short - ax.plot(data.ix[start_date:end_date, :].index, data.ix[start_date:end_date, 'MSFT'], label='Price') + ax.plot(data.ix[start_date:end_date, :].index, data.ix[start_date:end_date, 'MSFT'], label='Price') ax.plot(ema_short.ix[start_date:end_date, :].index, ema_short.ix[start_date:end_date, 'MSFT'], label = 'Span 8-days EMA') ax.legend(loc='best') ax.set_ylabel('Price in $') @@ -92,7 +94,7 @@ def EMA8DAY(df): year_month_format = mdates.DateFormatter('%d/%m/%y') ax.xaxis.set_major_fomratter(year_month_format) - #When the price timeseries p(t) crosses the EMA timeseries e(t) from below, we will close any existing short position and go long (buy) one unit of the asset. + #When the price timeseries p(t) crosses the EMA timeseries e(t) from below, we will close any existing short position and go long (buy) one unit of the asset. #When the price timeseries p(t) crosses the EMA timeseries e(t) from above, we will close any existing long position and go short (sell) one unit of the asset. @@ -135,7 +137,7 @@ def BuyAndHoldStrategy(df): ax = fig.add_subplot(2,1,2) for c in log_returns: - ax.plot(log_returns.index, 100*(np.exp(log_returns[c].cumsum()) - 1), label=str(c)) + ax.plot(log_returns.index, 100*(np.exp(log_returns[c].cumsum()) - 1), label=str(c)) ax.set_ylabel('Total relative returns (%)') ax.legend(loc='best') @@ -170,6 +172,6 @@ def BuyAndHoldStrategy(df): average_yearly_return = (1 + total_portfolio_return)**(1 / number_of_years) - 1 print('Total portfolio return is: ' + - '{:5.2f}'.format(100 * total_portfolio_return) + '%') + '{:5.2f}'.format(100 * total_portfolio_return) + '%') print('Average yearly return is: ' + - '{:5.2f}'.format(100 * average_yearly_return) + '%') + '{:5.2f}'.format(100 * average_yearly_return) + '%') From 8cc38a6cc2cb3c16ca394c8eba5051c5d0399986 Mon Sep 17 00:00:00 2001 From: ptang0917 Date: Sun, 18 Mar 2018 17:19:23 -0400 Subject: [PATCH 6/7] Update tradingStrategies.py --- tradingStrategies.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/tradingStrategies.py b/tradingStrategies.py index 9ee8663..ac20ab9 100644 --- a/tradingStrategies.py +++ b/tradingStrategies.py @@ -58,12 +58,12 @@ def volumeAnalysis(df): percentage = ((sumpositive - sumnegative) / totaloverallsum) * 100 if abs(percentage) > setpercentage: if percentage > 0: - print("A spike has occured from " + df.iloc[0]['time'] + " to " + df.iloc[20]['time']) + print("A spike has occured from " + str(df.iloc[0]['time']) + " to " + str(df.iloc[20]['time'])) elif percentage < 0: - print("A drop has occured from " + df.iloc[0]['time'] + " to " + df.iloc[20]['time']) + print("A drop has occured from " + str(df.iloc[0]['time']) + " to " + str(df.iloc[20]['time'])) else: - print("No drop has occured from " + df.iloc[0]['time'] + " to " + df.iloc[20]['time']) + print("A drop has occured from " + str(df.iloc[0]['time']) + " to " + str(df.iloc[20]['time'])) From f40062b6a3070309a4b9151282d8c381984bd8b6 Mon Sep 17 00:00:00 2001 From: ptang0917 Date: Sun, 18 Mar 2018 18:35:34 -0400 Subject: [PATCH 7/7] Update tradingStrategies.py --- tradingStrategies.py | 62 ++++++++++++++++++++++++++------------------ 1 file changed, 37 insertions(+), 25 deletions(-) diff --git a/tradingStrategies.py b/tradingStrategies.py index ac20ab9..12ed663 100644 --- a/tradingStrategies.py +++ b/tradingStrategies.py @@ -1,4 +1,4 @@ -from datetime import datetime +import datetime import pandas as pd import numpy as np @@ -15,11 +15,14 @@ def TlinesAnalysis(df): # Analysis the trendline Linear Regression # Compare the peaks and High highs and low lows # The total volumn between spikes -# Swing Trading Points -# Always enter a trade with a clear trading plan, the four key elements of which are a target, a limit, a stop loss and an add-on point. -# Always align your trade with the overall direction of the market. -# focus on the 6-month daily chart. Here, I can see finer details that the weekly chart obscures. -# look for volume dries up at lows +# Swing Trading Po +# Always enter +# Always align +# focus on the +# look for volume dries up at lowsints + # a trade with a clear trading plan, the four key elements of which are a target, a limit, a stop loss and an add-on point. + # your trade with the overall direction of the market. + # 6-month daily chart. Here, I can see finer details that the weekly chart obscures. # Dont get caught up in the coin or company. # Use a T-Line trading strategy. -8-day exponential moving average # the farther away from the T-line, the high the possible of it going back to the T-Line @@ -28,43 +31,52 @@ def TlinesAnalysis(df): # https://blog.quantopian.com/a-professional-quant-equity-workflow/ threeMonth = df['time'].iloc[-1] - datetime.timedelta(days=90) df = df[df['time']>threeMonth] - + print(df.describe()) def volumeAnalysis(df): - + print(df) positivelist = [] negativelist = [] totaloverallsum = 0 + a = 0 + b = 10 + c = len(df.index) #set percentage is to determine if the time period of price values is spiking or drop or just a plateau - setpercentage = .50 + setpercentage = 0.50 # For the sake of the testing, we will do up to 20 'datapoints' - for x in range(0,20): - difference = (float(df.iloc[x+1]['price_usd']) - float(df.iloc[x]['price_usd']))*float(df.iloc[x]['volume_usd']) - + #print(c) + for x in range(0,c-1): + difference = (float(df.iloc[x+1]['price_usd']) - float(df.iloc[x]['price_usd']))*float(df.iloc[x]['volume_usd']) if difference > 0: positivelist.append(difference) elif difference < 0: negativelist.append(difference) + #way to calculate the sum of the positve and negative lists respectively + + #For every 5 dataset, we calculate the + if(x % 5 == 0): + sumpositive = sum(map(float,positivelist)) + sumnegative = sum(map(float,negativelist)) + totaloverallsum = sumpositive+sumnegative + percentage = ((sumpositive - sumnegative) / totaloverallsum) * 100 + + if abs(percentage) > setpercentage: + if percentage > 0: + + print("A spike has occured from " + datetime.datetime.utcfromtimestamp((df.iloc[x-5]['time']/1000)).strftime('%Y-%m-%d %H:%M:%S') + " to " + datetime.datetime.utcfromtimestamp((df.iloc[x]['time']/1000)).strftime('%Y-%m-%d %H:%M:%S')) - #cool way to calculate the sum of the positve and negative lists respectively - sumpositive = sum(map(float,positivelist)) - sumnegative = sum(map(float,negativelist)) - + elif percentage < 0: + print("A drop has occured from " + datetime.datetime.utcfromtimestamp((df.iloc[x-5]['time']/1000)).strftime('%Y-%m-%d %H:%M:%S') + " to " + datetime.datetime.utcfromtimestamp((df.iloc[x]['time']/1000)).strftime('%Y-%m-%d %H:%M:%S')) - totaloverallsum = sumpositive+sumnegative - percentage = ((sumpositive - sumnegative) / totaloverallsum) * 100 - if abs(percentage) > setpercentage: - if percentage > 0: - print("A spike has occured from " + str(df.iloc[0]['time']) + " to " + str(df.iloc[20]['time'])) + else: + print("No drop has occured from " + datetime.datetime.utcfromtimestamp((df.iloc[x-5]['time']/1000)).strftime('%Y-%m-%d %H:%M:%S') + " to " + datetime.datetime.utcfromtimestamp((df.iloc[x]['time']/1000)).strftime('%Y-%m-%d %H:%M:%S')) - elif percentage < 0: - print("A drop has occured from " + str(df.iloc[0]['time']) + " to " + str(df.iloc[20]['time'])) - else: - print("A drop has occured from " + str(df.iloc[0]['time']) + " to " + str(df.iloc[20]['time'])) + + def EMA8DAY(df):