#dd 1.09
Text
Sep 3, 2023. ETH. Short
Another nice trade after rejecting 4h OB.
Price took buy-side liquidity and rejected from old 4h bearish OB.
Entry after MSB on 5m TF at 2:30. Limit order placed at FVG.
Risk = 0.32, RR = 1.42 (1 / 1.09)
Limit order filled at 2:32 (2m). DD - 0m. Trade duration - 1h 7m.
Price hit TP.
View from 30m TF:
#crypto#day trading#market structure#orderblock#smart money concept#smc#daytrading#take profit#trading strategy#tradingstrategy#technical analysis#scalping#supply and demand
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After ISS is done, will you consider publishing all of your headcanons about Irken society? Like how degrees translate to minutes and such? :DD
Sure!! In fact I can give you that one right now.
Even though I made up a different unit of time, I decided to make Irken days the same length as Earth days, so that I'd only have to worry about converting degrees to hours/minutes, not any "okay it's been four days on Earth so that's 2.36 days on Irk..." nonsense. It might not be Good Worldbuilding, but it does make my life a lot easier.
Irkens use the time unit "degrees" because I decided that they divide their circles into 220 degrees instead of 360 degrees, and days, being cyclical, obviously have the same degrees as a circle. I can't exactly remember why I chose the number 220, but I think I chose it so that there would be almost, but not quite, 10 degrees an hour—again, simplifying my math.
It's not a headcanon I'd reuse now, I think—it's simply different for the sake of being different, rather than uniquely Irken—but! That's what I did. So:
220 degrees = 1 day (both Irken and human) = 24 hours/1440 minutes.
And this is the list of time conversions I refer back to while I'm writing so I don't have to do math:
1 minute/.1527 degrees6.54 minutes/1 degree1 hour/9.16 degrees1.09 hours/10 degrees2 hours/18.3 degrees1 day/220 degrees
I'm pretty willing to answer any questions about headcanons as long as the answer isn't a spoiler, it doesn't just have to be when I'm finished writing. I just don't know what you're interested in hearing!
(I'm banning y'all smartasses from saying "everything.")
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Stock daily Filter Report for 2021/05/22 05-40-12
*******************Part 1.0 Big Cap Industry Overiew********************* big_industry_uptrending_count tickers industry Basic Materials 15 Communication Services 12 Consumer Cyclical 5 Consumer Defensive 19 Energy 18 Financial Services 38 Healthcare 32 Industrials 10 Real Estate 6 Technology 11 Utilities 6 unknown 2 **************************************** big_industry_downtrending_count tickers industry Basic Materials 2 Communication Services 16 Consumer Cyclical 15 Consumer Defensive 4 Energy 1 Financial Services 5 Healthcare 13 Industrials 2 Real Estate 1 Technology 25 Utilities 4 *******************Part 1.1 Big Cap Long Entry SPAN MACD********************* big_long_signal_entry_span_macd Symbol Day Return Market Cap Long/Short score MACD Signal Count Market Value Span Signal Count industry 142 MXIM 1 0.0 big-cap Long NaN 1.0 1.701627e+08 3.0 Technology Mean Return: nan Mean Day/Week: inf Accuracy:nan *******************Part 1.2 Big Cap Short Entry SPAN MACD********************* big_short_signal_entry_span_macd Symbol Day Return Market Cap Long/Short score MACD Signal Count Market Value Span Signal Count industry 113 PCAR 3 0.84 big-cap Short NaN -3.0 2.094717e+08 -5.0 Industrials Mean Return: 0.84 Mean Day/Week: 3.0 Accuracy:0.0 *******************Part 1.3 Big Cap Long Entry SPAN********************* big_long_signal_entry_span Symbol Day Return Market Cap Long/Short score MACD Signal Count Market Value Span Signal Count industry 142 MXIM 1 0.0 big-cap Long NaN 1.0 1.701627e+08 3.0 Technology Mean Return: nan Mean Day/Week: inf Accuracy:nan *******************Part 1.4 Big Cap Short Entry SPAN********************* big_short_signal_entry_span Symbol Day Return Market Cap Long/Short score MACD Signal Count Market Value Span Signal Count industry 113 PCAR 3 0.84 big-cap Short NaN -3.0 2.094717e+08 -5.0 Industrials 225 SQM 3 -0.77 big-cap Short NaN -9.0 1.013677e+08 -5.0 Basic Materials Mean Return: 0.034999999999999976 Mean Day/Week: 3.0 Accuracy:0.5 *******************Part 1.5 Big Cap Long Entry MACD********************* big_long_signal_entry_macd Symbol Day Return Market Cap Long/Short score MACD Signal Count Market Value Span Signal Count industry 40 TGT 2 1.49 big-cap Long NaN 2.0 9.071505e+08 45.0 Consumer Defensive 12 PEP 2 -0.09 big-cap Long NaN 2.0 6.175045e+08 40.0 Consumer Defensive 18 BMY 2 0.07 big-cap Long NaN 4.0 5.694120e+08 29.0 Healthcare 32 INTU 3 2.92 big-cap Long NaN 3.0 4.933109e+08 9.0 Technology 146 ED 1 0.00 big-cap Long NaN 1.0 2.522853e+08 49.0 Utilities 83 EXC 2 0.22 big-cap Long NaN 2.0 2.187498e+08 42.0 Utilities 135 CPRT 1 0.00 big-cap Long NaN 1.0 1.833569e+08 32.0 Industrials 142 MXIM 1 0.00 big-cap Long NaN 1.0 1.701627e+08 3.0 Technology 150 AVB 5 1.09 big-cap Long NaN 5.0 1.561617e+08 76.0 Real Estate 249 HWM 2 -0.09 big-cap Long NaN 2.0 1.512634e+08 80.0 Industrials 222 BXP 1 0.00 big-cap Long NaN 1.0 1.323106e+08 63.0 Real Estate 238 LNT 3 0.71 big-cap Long NaN 3.0 1.059043e+08 50.0 Utilities 58 BAM 5 0.29 big-cap Long NaN 5.0 1.036875e+08 80.0 Financial Services 241 NLY 4 0.00 big-cap Long NaN 4.0 1.018300e+08 50.0 Real Estate 227 MKL 2 -0.72 big-cap Long NaN 2.0 9.007450e+07 76.0 Financial Services 234 OSH 1 0.00 big-cap Long NaN 1.0 8.925946e+07 26.0 Healthcare 242 BSY 2 1.82 big-cap Long NaN 2.0 8.684085e+07 37.0 Technology 243 CNP 5 -0.62 big-cap Long NaN 5.0 6.601064e+07 50.0 Utilities 223 ZLAB 1 0.00 big-cap Long NaN 1.0 6.382036e+07 7.0 Healthcare 203 BPY 3 0.27 big-cap Long NaN 3.0 5.615473e+07 80.0 Real Estate 66 STLA 5 1.60 big-cap Long NaN 5.0 5.212064e+07 13.0 unknown 140 FMX 5 0.83 big-cap Long NaN 5.0 4.199085e+07 49.0 Consumer Defensive 11 TM 5 1.57 big-cap Long NaN 5.0 3.069131e+07 8.0 Consumer Cyclical Mean Return: 0.6682352941176471 Mean Day/Week: 3.7058823529411766 Accuracy:0.7058823529411765 *******************Part 1.6 Big Cap Short Entry MACD********************* big_short_signal_entry_macd Symbol Day Return Market Cap Long/Short score MACD Signal Count Market Value Span Signal Count industry 113 PCAR 3 0.84 big-cap Short NaN -3.0 2.094717e+08 -5.0 Industrials 218 ATHM 3 -6.39 big-cap Short NaN -3.0 8.465947e+07 -54.0 Communication Services Mean Return: -2.775 Mean Day/Week: 3.0 Accuracy:0.5 *******************Part 1.7 Big Cap Long Maintainance********************* big_long_signal_maintainance Symbol Day Return Market Cap Long/Short score MACD Signal Count Market Value Span Signal Count industry 4 JPM 6 -0.81 big-cap Long NaN 11.0 1.573291e+09 80.0 Financial Services 23 C 7 3.13 big-cap Long NaN 7.0 1.431810e+09 80.0 Financial Services 5 PG 10 0.27 big-cap Long NaN 10.0 1.268628e+09 35.0 Consumer Defensive 2 JNJ 11 1.47 big-cap Long NaN 14.0 1.108243e+09 25.0 Healthcare 22 MS 12 1.86 big-cap Long NaN 13.0 8.171627e+08 80.0 Financial Services 9 PFE 8 0.66 big-cap Long NaN 52.0 7.854975e+08 31.0 Healthcare 16 COST 7 0.33 big-cap Long NaN 43.0 6.254959e+08 33.0 Consumer Defensive 123 MPC 12 1.26 big-cap Long NaN 14.0 5.803818e+08 80.0 Energy 67 COF 18 10.12 big-cap Long NaN 18.0 5.544482e+08 80.0 Financial Services 39 CVS 18 17.80 big-cap Long NaN 27.0 5.431243e+08 42.0 Healthcare 45 MDLZ 18 4.03 big-cap Long NaN 52.0 5.208115e+08 47.0 Consumer Defensive 26 CHTR 20 6.18 big-cap Long NaN 26.0 5.137003e+08 27.0 Communication Services 33 RTX 7 3.04 big-cap Long NaN 19.0 4.980004e+08 80.0 Industrials 13 ABBV 17 4.70 big-cap Long NaN 27.0 4.907347e+08 26.0 Healthcare 54 FDX 17 4.76 big-cap Long NaN 17.0 4.781636e+08 47.0 Industrials 49 EQIX 7 1.79 big-cap Long NaN 45.0 4.471228e+08 27.0 Real Estate 64 NEM 15 13.41 big-cap Long NaN 52.0 4.349608e+08 36.0 Basic Materials 19 AZN 21 9.52 big-cap Long NaN 48.0 4.161357e+08 27.0 Healthcare 104 ALL 41 18.38 big-cap Long NaN 57.0 3.717186e+08 59.0 Financial Services 85 GOLD 15 10.92 big-cap Long NaN 50.0 3.490102e+08 27.0 Basic Materials 59 REGN 7 -2.01 big-cap Long NaN 50.0 3.007226e+08 21.0 Healthcare 128 BNTX 11 7.46 big-cap Long NaN 36.0 2.979504e+08 38.0 Healthcare 50 CL 16 4.33 big-cap Long NaN 46.0 2.946301e+08 30.0 Consumer Defensive 102 ALXN 26 8.30 big-cap Long NaN 26.0 2.917117e+08 27.0 Healthcare 116 SLB 8 -0.24 big-cap Long NaN 13.0 2.738950e+08 15.0 Energy 188 ET 18 19.16 big-cap Long NaN 18.0 2.707160e+08 77.0 Energy 88 DOW 12 -0.21 big-cap Long NaN 12.0 2.651986e+08 77.0 Basic Materials 63 NOC 17 5.15 big-cap Long NaN 58.0 2.526178e+08 55.0 Industrials 62 ITUB 10 2.58 big-cap Long NaN 47.0 2.499097e+08 22.0 Financial Services 52 PBR 6 -2.69 big-cap Long NaN 38.0 2.491712e+08 10.0 Energy 211 NUE 14 13.66 big-cap Long NaN 14.0 2.479914e+08 69.0 Basic Materials 183 AKAM 12 3.02 big-cap Long NaN 44.0 2.476980e+08 13.0 Technology 125 ADM 20 11.74 big-cap Long NaN 20.0 2.371538e+08 80.0 Consumer Defensive 192 PKI 6 0.93 big-cap Long NaN 39.0 2.323185e+08 13.0 Healthcare 245 DVN 8 -0.46 big-cap Long NaN 13.0 2.184781e+08 21.0 Energy 60 DD 13 4.03 big-cap Long NaN 14.0 2.131955e+08 28.0 Basic Materials 115 PRU 28 9.62 big-cap Long NaN 28.0 2.129138e+08 80.0 Financial Services 166 SYF 15 5.86 big-cap Long NaN 15.0 2.114981e+08 80.0 unknown 158 EFX 26 23.00 big-cap Long NaN 44.0 2.083173e+08 37.0 Industrials 112 KMI 17 8.10 big-cap Long NaN 17.0 2.022210e+08 80.0 Energy 141 CERN 10 0.27 big-cap Long NaN 45.0 2.021257e+08 17.0 Healthcare 51 CME 7 1.20 big-cap Long NaN 7.0 2.017577e+08 80.0 Financial Services 57 APD 7 -0.53 big-cap Long NaN 54.0 2.006464e+08 45.0 Basic Materials 122 NOK 6 3.14 big-cap Long NaN 31.0 1.950679e+08 17.0 Technology 110 AIG 12 2.38 big-cap Long NaN 12.0 1.906768e+08 80.0 Financial Services 69 ABEV 12 5.73 big-cap Long NaN 12.0 1.673745e+08 28.0 Consumer Defensive 209 EXPD 13 7.47 big-cap Long NaN 13.0 1.647354e+08 59.0 Industrials 84 KHC 12 2.35 big-cap Long NaN 12.0 1.631728e+08 80.0 Consumer Defensive 233 NLOK 8 6.60 big-cap Long NaN 8.0 1.589948e+08 37.0 Technology 105 PPG 7 0.25 big-cap Long NaN 55.0 1.575924e+08 53.0 Basic Materials 117 HSY 16 6.13 big-cap Long NaN 16.0 1.571586e+08 52.0 Consumer Defensive 111 AFL 7 1.48 big-cap Long NaN 15.0 1.531325e+08 80.0 Financial Services 191 INVH 7 1.97 big-cap Long NaN 49.0 1.508920e+08 52.0 Real Estate 196 RF 6 -2.09 big-cap Long NaN 14.0 1.470535e+08 80.0 Financial Services 145 ANET 7 4.50 big-cap Long NaN 40.0 1.457327e+08 33.0 Technology 195 HES 15 6.71 big-cap Long NaN 15.0 1.419183e+08 80.0 Energy 167 K 12 -1.46 big-cap Long NaN 12.0 1.414272e+08 48.0 Consumer Defensive 194 KEY 17 4.86 big-cap Long NaN 17.0 1.355256e+08 80.0 Financial Services 96 TROW 13 3.74 big-cap Long NaN 13.0 1.350924e+08 80.0 Financial Services 89 BCE 17 4.71 big-cap Long NaN 17.0 1.346821e+08 55.0 Communication Services 224 IT 22 18.17 big-cap Long NaN 22.0 1.325332e+08 80.0 Technology 136 WMB 15 6.44 big-cap Long NaN 15.0 1.321886e+08 80.0 Energy 170 IP 41 15.95 big-cap Long NaN 41.0 1.294135e+08 59.0 Consumer Cyclical 119 MSI 10 0.99 big-cap Long NaN 10.0 1.196013e+08 78.0 Technology 37 GSK 7 1.86 big-cap Long NaN 51.0 1.182995e+08 27.0 Healthcare 185 APO 22 9.68 big-cap Long NaN 27.0 1.118785e+08 27.0 Financial Services 250 UHS 32 14.64 big-cap Long NaN 52.0 1.100259e+08 36.0 Healthcare 247 LKQ 20 11.70 big-cap Long NaN 20.0 1.096814e+08 78.0 Consumer Cyclical 220 ELAN 6 2.73 big-cap Long NaN 23.0 1.064012e+08 11.0 Healthcare 186 TRU 7 2.62 big-cap Long NaN 44.0 1.046690e+08 36.0 Industrials 139 FRC 21 6.00 big-cap Long NaN 21.0 1.034014e+08 80.0 Financial Services 137 SU 8 -0.41 big-cap Long NaN 13.0 1.010440e+08 80.0 Energy 124 NTR 8 0.12 big-cap Long NaN 14.0 1.008071e+08 14.0 Basic Materials 228 EMN 17 7.07 big-cap Long NaN 17.0 9.565473e+07 80.0 Basic Materials 31 TD 17 5.20 big-cap Long NaN 17.0 9.110346e+07 80.0 Financial Services 70 EPD 8 1.74 big-cap Long NaN 17.0 9.109066e+07 59.0 Energy 25 RY 17 6.27 big-cap Long NaN 17.0 9.072112e+07 80.0 Financial Services 229 BEN 13 -0.13 big-cap Long NaN 13.0 9.068234e+07 80.0 Financial Services 230 LUMN 6 -0.88 big-cap Long NaN 10.0 8.996755e+07 12.0 Communication Services 251 ICLR 21 6.10 big-cap Long NaN 40.0 8.869705e+07 27.0 Healthcare 179 NTRS 19 7.70 big-cap Long NaN 19.0 8.827196e+07 78.0 Financial Services 256 TXT 38 19.09 big-cap Long NaN 52.0 8.552851e+07 80.0 Industrials 171 WPM 12 8.75 big-cap Long NaN 50.0 8.385507e+07 27.0 Basic Materials 65 BMO 17 8.43 big-cap Long NaN 17.0 8.152635e+07 80.0 Financial Services 189 AEM 10 5.25 big-cap Long NaN 47.0 6.890509e+07 15.0 Basic Materials 253 KL 11 6.56 big-cap Long NaN 48.0 6.763160e+07 27.0 Basic Materials 14 NVO 10 6.43 big-cap Long NaN 29.0 6.709535e+07 25.0 Healthcare 24 SNY 11 3.85 big-cap Long NaN 51.0 6.653449e+07 31.0 Healthcare 201 LNG 16 7.51 big-cap Long NaN 16.0 6.561987e+07 80.0 Energy 221 CINF 15 4.69 big-cap Long NaN 15.0 6.196068e+07 80.0 Financial Services 163 CCEP 27 12.20 big-cap Long NaN 27.0 6.161993e+07 80.0 Consumer Defensive 17 UL 13 3.50 big-cap Long NaN 51.0 6.127041e+07 27.0 Consumer Defensive 131 CNQ 11 -3.51 big-cap Long NaN 11.0 6.005924e+07 80.0 Energy 20 BUD 25 10.94 big-cap Long NaN 47.0 5.979275e+07 31.0 Consumer Defensive 149 FNV 31 10.70 big-cap Long NaN 51.0 5.887297e+07 37.0 Basic Materials 90 CM 17 7.62 big-cap Long NaN 17.0 5.763476e+07 80.0 Financial Services 165 DB 8 6.38 big-cap Long NaN 17.0 5.557225e+07 21.0 Financial Services 35 DEO 8 3.46 big-cap Long NaN 32.0 5.482561e+07 58.0 Consumer Defensive 144 MPLX 14 4.56 big-cap Long NaN 15.0 4.655147e+07 80.0 Energy 259 CBOE 15 4.42 big-cap Long NaN 15.0 3.916679e+07 68.0 Financial Services 81 NGG 16 6.58 big-cap Long NaN 49.0 3.232915e+07 36.0 Utilities 30 HSBC 7 0.89 big-cap Long NaN 17.0 3.131437e+07 21.0 Financial Services 213 PBA 17 3.85 big-cap Long NaN 18.0 2.213337e+07 57.0 Energy 75 AMX 11 1.47 big-cap Long NaN 11.0 2.092462e+07 32.0 Communication Services 216 IMO 17 16.42 big-cap Long NaN 17.0 2.065781e+07 80.0 Energy 133 TU 10 1.80 big-cap Long NaN 17.0 1.920941e+07 13.0 Communication Services 261 RDY 17 4.88 big-cap Long NaN 40.0 1.869027e+07 27.0 Healthcare 103 CRH 12 0.34 big-cap Long NaN 12.0 1.738985e+07 41.0 Basic Materials 180 FTS 6 0.85 big-cap Long NaN 11.0 1.397692e+07 51.0 Utilities 61 SAN 8 4.31 big-cap Long NaN 18.0 1.299049e+07 80.0 Financial Services 193 GRFS 11 2.73 big-cap Long NaN 47.0 1.124417e+07 28.0 Healthcare 118 BBVA 11 5.11 big-cap Long NaN 17.0 9.597872e+06 20.0 Financial Services 151 RCI 15 3.00 big-cap Long NaN 30.0 7.828126e+06 36.0 Communication Services 108 LYG 17 7.75 big-cap Long NaN 17.0 7.798603e+06 80.0 Financial Services 237 NTCO 7 0.58 big-cap Long NaN 32.0 7.628455e+06 13.0 Consumer Defensive 147 TEF 6 3.62 big-cap Long NaN 15.0 4.772407e+06 19.0 Communication Services 27 PTR 7 0.60 big-cap Long NaN 13.0 4.563957e+06 18.0 Energy 127 BSBR 7 5.17 big-cap Long NaN 26.0 4.166699e+06 13.0 Financial Services 154 FMS 7 2.06 big-cap Long NaN 51.0 3.757434e+06 25.0 Healthcare 73 AMOV 10 2.16 big-cap Long NaN 11.0 3.645000e+04 24.0 Communication Services 200 VAR 14 0.27 big-cap Long NaN 14.0 0.000000e+00 54.0 Healthcare Mean Return: 5.143719008264463 Mean Day/Week: 13.776859504132231 Accuracy:0.8925619834710744 *******************Part 1.8 Big Cap Short Maintainance********************* big_short_signal_maintainance Symbol Day Return Market Cap Long/Short score MACD Signal Count Market Value Span Signal Count industry 0 BABA 10 -3.64 big-cap Short NaN -10.0 4.752492e+09 -50.0 Consumer Cyclical 42 NIO 8 1.71 big-cap Short NaN -8.0 1.683336e+09 -62.0 Consumer Cyclical 29 ABNB 12 -12.65 big-cap Short NaN -61.0 1.347076e+09 -33.0 Communication Services 6 DIS 6 -0.72 big-cap Short NaN -56.0 1.330960e+09 -21.0 Communication Services 41 MU 6 1.31 big-cap Short NaN -28.0 1.054225e+09 -16.0 Technology 34 UBER 12 5.02 big-cap Short NaN -23.0 9.759098e+08 -17.0 Technology 68 TAL 13 -21.58 big-cap Short NaN -13.0 8.339036e+08 -52.0 Consumer Defensive 36 MELI 11 -7.50 big-cap Short NaN -11.0 7.561267e+08 -58.0 Consumer Cyclical 43 BIDU 20 -12.80 big-cap Short NaN -59.0 7.231908e+08 -42.0 Communication Services 160 DKNG 12 -13.91 big-cap Short NaN -41.0 6.757260e+08 -29.0 Consumer Cyclical 1 TSM 16 -3.99 big-cap Short NaN -27.0 6.553203e+08 -50.0 Technology 28 NOW 9 -2.40 big-cap Short NaN -15.0 6.441631e+08 -17.0 Technology 56 TWLO 14 -7.12 big-cap Short NaN -14.0 5.228247e+08 -17.0 Communication Services 94 TTD 12 -7.69 big-cap Short NaN -12.0 5.073880e+08 -16.0 Technology 48 CNI 6 -2.83 big-cap Short NaN -24.0 4.433641e+08 -9.0 Industrials 138 ETSY 13 -7.91 big-cap Short NaN -24.0 4.175659e+08 -25.0 Consumer Cyclical 169 VIPS 9 -11.52 big-cap Short NaN -41.0 4.012757e+08 -37.0 Consumer Cyclical 76 DOCU 13 -0.99 big-cap Short NaN -13.0 3.998944e+08 -17.0 Technology 120 EDU 10 -24.44 big-cap Short NaN -10.0 3.758607e+08 -49.0 Consumer Defensive 71 CVNA 10 2.37 big-cap Short NaN -11.0 3.643453e+08 -13.0 Consumer Cyclical 182 DISCK 8 -6.24 big-cap Short NaN -40.0 3.391936e+08 -34.0 Communication Services 87 TDOC 15 -14.13 big-cap Short NaN -15.0 3.313668e+08 -57.0 Healthcare 130 ZS 14 -2.77 big-cap Short NaN -14.0 2.966115e+08 -57.0 Technology 107 Z 18 -20.30 big-cap Short NaN -61.0 2.850105e+08 -46.0 Communication Services 199 PENN 19 -16.45 big-cap Short NaN -47.0 2.768256e+08 -36.0 Consumer Cyclical 100 RNG 13 -14.01 big-cap Short NaN -13.0 2.626855e+08 -57.0 Technology 82 CHWY 13 -8.73 big-cap Short NaN -13.0 2.626750e+08 -57.0 Consumer Cyclical 214 WIX 8 3.98 big-cap Short NaN -12.0 2.369573e+08 -16.0 Technology 184 NVAX 12 -16.12 big-cap Short NaN -13.0 2.369019e+08 -17.0 Healthcare 55 SPOT 16 -9.17 big-cap Short NaN -16.0 2.270667e+08 -61.0 Communication Services 187 TYL 7 3.79 big-cap Short NaN -16.0 2.096829e+08 -14.0 Technology 207 QS 19 -31.49 big-cap Short NaN -42.0 2.036141e+08 -139.0 Consumer Cyclical 129 SPLK 17 -7.70 big-cap Short NaN -17.0 1.911316e+08 -139.0 Technology 93 APTV 6 0.95 big-cap Short NaN -9.0 1.897485e+08 -10.0 Consumer Cyclical 197 AES 6 -0.02 big-cap Short NaN -18.0 1.859322e+08 -14.0 Utilities 72 TME 17 -15.56 big-cap Short NaN -42.0 1.837111e+08 -42.0 Communication Services 168 FTCH 16 -18.19 big-cap Short NaN -62.0 1.792006e+08 -43.0 Consumer Cyclical 148 EXAS 14 -7.86 big-cap Short NaN -14.0 1.756512e+08 -17.0 Healthcare 152 COUP 12 -0.20 big-cap Short NaN -12.0 1.665557e+08 -57.0 Technology 235 FSLY 14 -21.89 big-cap Short NaN -14.0 1.624619e+08 -66.0 Technology 236 CHGG 14 -11.48 big-cap Short NaN -14.0 1.621197e+08 -57.0 Consumer Defensive 206 GH 9 1.25 big-cap Short NaN -14.0 1.580491e+08 -14.0 Healthcare 246 BYND 14 -15.06 big-cap Short NaN -14.0 1.543782e+08 -52.0 Consumer Defensive 161 HOLX 17 -5.84 big-cap Short NaN -18.0 1.356067e+08 -24.0 Healthcare 198 CTXS 15 -6.22 big-cap Short NaN -25.0 1.304194e+08 -17.0 Technology 219 OPEN 12 -13.96 big-cap Short NaN -12.0 1.302975e+08 -43.0 Real Estate 175 TXG 9 14.59 big-cap Short NaN -13.0 1.283227e+08 -12.0 Healthcare 153 PAYC 13 -2.73 big-cap Short NaN -13.0 1.213833e+08 -16.0 Technology 159 MKTX 13 0.18 big-cap Short NaN -20.0 1.027423e+08 -24.0 Financial Services 257 NRG 24 -10.16 big-cap Short NaN -47.0 8.419709e+07 -47.0 Utilities 231 AVLR 13 -1.89 big-cap Short NaN -13.0 8.108310e+07 -64.0 Technology 217 MASI 14 -2.34 big-cap Short NaN -14.0 7.972712e+07 -64.0 Healthcare 79 RKT 12 -11.10 big-cap Short NaN -44.0 7.776006e+07 -28.0 Financial Services 174 CTLT 12 -4.01 big-cap Short NaN -13.0 7.546562e+07 -16.0 Healthcare 244 RGEN 10 2.99 big-cap Short NaN -13.0 7.261740e+07 -14.0 Healthcare 212 APPN 17 -41.21 big-cap Short NaN -65.0 6.996138e+07 -57.0 Technology 173 ZI 13 -8.76 big-cap Short NaN -13.0 6.425749e+07 -15.0 Technology 172 GDRX 12 -11.72 big-cap Short NaN -12.0 5.811801e+07 -56.0 Healthcare 121 RPRX 7 1.79 big-cap Short NaN -11.0 5.049486e+07 -17.0 Healthcare 21 SNE 13 0.21 big-cap Short NaN -21.0 5.010612e+07 -18.0 Technology 126 IBKR 7 -0.27 big-cap Short NaN -47.0 4.821088e+07 -18.0 Financial Services 210 CGC 15 -10.15 big-cap Short NaN -65.0 4.629105e+07 -46.0 Healthcare 239 KC 13 -15.37 big-cap Short NaN -13.0 4.526286e+07 -47.0 Technology 106 ZG 18 -21.04 big-cap Short NaN -61.0 4.266834e+07 -46.0 Communication Services 252 PCTY 11 -0.09 big-cap Short NaN -13.0 4.091127e+07 -15.0 Technology 205 LSXMK 8 0.63 big-cap Short NaN -27.0 3.897638e+07 -15.0 Communication Services 162 WISH 7 16.42 big-cap Short NaN -7.0 3.399489e+07 -108.0 Consumer Cyclical 204 LSXMA 10 -0.45 big-cap Short NaN -27.0 2.873259e+07 -15.0 Communication Services 232 ERIE 16 -7.34 big-cap Short NaN -16.0 1.021264e+07 -56.0 Financial Services 240 ENIA 16 -1.75 big-cap Short NaN -28.0 8.235133e+06 -23.0 Utilities 258 BCH 8 -12.83 big-cap Short NaN -40.0 1.459538e+06 -11.0 Financial Services Mean Return: -7.198591549295775 Mean Day/Week: 12.366197183098592 Accuracy:0.7887323943661971 ************************************** ************************************** ************************************** *******************Part 2.0 Small Cap Industry Overiew********************* small_industry_uptrending_count tickers industry Basic Materials 8 Communication Services 4 Consumer Cyclical 5 Consumer Defensive 1 Energy 6 Financial Services 11 Healthcare 5 Industrials 7 Real Estate 5 Technology 3 Utilities 3 unknown 1 **************************************** small_industry_downtrending_count tickers industry Communication Services 3 Consumer Cyclical 8 Consumer Defensive 3 Financial Services 3 Healthcare 16 Industrials 5 Real Estate 3 Technology 20 Utilities 4 *******************Part 2.1 Small Cap Long Entry SPAN MACD********************* small_long_signal_entry_span_macd Symbol Day Return Market Cap Long/Short score MACD Signal Count Market Value Span Signal Count industry 311 PAAS 1 0.0 small-cap Long NaN 1.0 9.018416e+07 5.0 Basic Materials Mean Return: nan Mean Day/Week: inf Accuracy:nan *******************Part 2.2 Small Cap Short Entry SPAN MACD********************* small_short_signal_entry_span_macd Empty DataFrame Columns: [Symbol, Day, Return, Market Cap, Long/Short, score, MACD Signal Count, Market Value, Span Signal Count, industry] Index: [] Mean Return: nan Mean Day/Week: nan Accuracy:nan *******************Part 2.3 Small Cap Long Entry SPAN********************* small_long_signal_entry_span Symbol Day Return Market Cap Long/Short score MACD Signal Count Market Value Span Signal Count industry 311 PAAS 1 0.0 small-cap Long NaN 1.0 9.018416e+07 5.0 Basic Materials Mean Return: nan Mean Day/Week: inf Accuracy:nan *******************Part 2.4 Small Cap Short Entry SPAN********************* small_short_signal_entry_span Empty DataFrame Columns: [Symbol, Day, Return, Market Cap, Long/Short, score, MACD Signal Count, Market Value, Span Signal Count, industry] Index: [] Mean Return: nan Mean Day/Week: nan Accuracy:nan *******************Part 2.5 Small Cap Long Entry MACD********************* small_long_signal_entry_macd Symbol Day Return Market Cap Long/Short score MACD Signal Count Market Value Span Signal Count industry 269 IPG 2 0.61 small-cap Long NaN 2.0 1.003809e+08 80.0 Communication Services 311 PAAS 1 0.00 small-cap Long NaN 1.0 9.018416e+07 5.0 Basic Materials 331 VRT 5 2.23 small-cap Long NaN 5.0 4.725059e+07 36.0 Industrials 312 ARW 2 0.97 small-cap Long NaN 2.0 4.468919e+07 55.0 Technology 275 CONE 2 0.83 small-cap Long NaN 2.0 4.083628e+07 30.0 Real Estate 316 ADT 3 5.77 small-cap Long NaN 3.0 3.444747e+07 30.0 Industrials 320 BSMX 4 -0.17 small-cap Long NaN 4.0 2.167428e+06 40.0 Financial Services Mean Return: 1.7066666666666668 Mean Day/Week: 3.1666666666666665 Accuracy:0.8333333333333334 *******************Part 2.6 Small Cap Short Entry MACD********************* small_short_signal_entry_macd Symbol Day Return Market Cap Long/Short score MACD Signal Count Market Value Span Signal Count industry 349 PRLB 1 0.0 small-cap Short NaN -1.0 7.979074e+07 -64.0 Industrials 287 CIB 1 0.0 small-cap Short NaN -1.0 3.547977e+06 -66.0 Financial Services Mean Return: nan Mean Day/Week: inf Accuracy:nan *******************Part 2.7 Small Cap Long Maintaiance********************* small_long_signal_maintainance Symbol Day Return Market Cap Long/Short score MACD Signal Count Market Value Span Signal Count industry 263 AMH 7 1.27 small-cap Long NaN 49.0 2.999032e+08 53.0 Real Estate 285 JAZZ 11 3.12 small-cap Long NaN 12.0 1.592537e+08 13.0 Healthcare 314 STLD 19 11.82 small-cap Long NaN 19.0 1.363659e+08 76.0 Basic Materials 335 DXC 21 16.16 small-cap Long NaN 38.0 1.122396e+08 41.0 Technology 297 NLSN 11 -1.73 small-cap Long NaN 11.0 1.116784e+08 80.0 Industrials 281 GFI 11 14.14 small-cap Long NaN 11.0 1.027814e+08 36.0 Basic Materials 327 BERY 14 6.78 small-cap Long NaN 14.0 9.769139e+07 74.0 Consumer Cyclical 279 LNC 11 -0.80 small-cap Long NaN 11.0 9.578597e+07 80.0 Financial Services 290 AGNC 6 1.75 small-cap Long NaN 41.0 8.782810e+07 80.0 Real Estate 278 KGC 7 7.25 small-cap Long NaN 50.0 8.697279e+07 34.0 Basic Materials 367 PSXP 12 10.45 small-cap Long NaN 61.0 8.605797e+07 57.0 Energy 270 HSIC 23 8.52 small-cap Long NaN 49.0 8.355826e+07 31.0 Healthcare 299 CMA 16 3.37 small-cap Long NaN 16.0 8.030148e+07 80.0 Financial Services 353 MTZ 19 12.27 small-cap Long NaN 19.0 7.600076e+07 80.0 Industrials 301 AFG 12 2.12 small-cap Long NaN 12.0 7.534539e+07 80.0 Financial Services 303 ATH 27 15.17 small-cap Long NaN 27.0 6.527197e+07 80.0 Financial Services 330 SEE 31 19.27 small-cap Long NaN 54.0 6.443992e+07 54.0 Consumer Cyclical 319 RGLD 12 4.84 small-cap Long NaN 39.0 6.140042e+07 37.0 Basic Materials 355 PRGO 9 3.35 small-cap Long NaN 27.0 5.991029e+07 12.0 Healthcare 359 CCJ 11 -2.71 small-cap Long NaN 13.0 5.976774e+07 80.0 Energy 271 IRM 11 4.20 small-cap Long NaN 11.0 5.705345e+07 80.0 Real Estate 338 TRGP 19 13.37 small-cap Long NaN 19.0 5.498251e+07 80.0 Energy 317 CLR 9 0.81 small-cap Long NaN 13.0 5.107144e+07 20.0 Energy 347 PAA 10 4.81 small-cap Long NaN 17.0 4.483783e+07 21.0 Energy 333 TFII 19 13.67 small-cap Long NaN 19.0 4.246031e+07 80.0 Industrials 365 VNT 8 3.04 small-cap Long NaN 28.0 4.029712e+07 12.0 Technology 358 JHG 23 12.40 small-cap Long NaN 36.0 3.979621e+07 35.0 Financial Services 305 JLL 14 8.11 small-cap Long NaN 14.0 3.950275e+07 80.0 Real Estate 381 BTG 6 3.77 small-cap Long NaN 49.0 3.119186e+07 27.0 Basic Materials 295 CHE 6 2.61 small-cap Long NaN 43.0 2.915376e+07 18.0 Healthcare 366 ORI 35 16.97 small-cap Long NaN 72.0 2.608360e+07 80.0 Financial Services 364 CHH 7 2.26 small-cap Long NaN 11.0 2.212618e+07 42.0 Consumer Cyclical 283 PHYS 10 2.66 small-cap Long NaN 35.0 1.877620e+07 15.0 unknown 321 SC 33 27.39 small-cap Long NaN 33.0 1.790342e+07 78.0 Financial Services 348 SRCL 18 12.72 small-cap Long NaN 35.0 1.354271e+07 30.0 Industrials 378 KT 9 2.61 small-cap Long NaN 9.0 9.224305e+06 70.0 Communication Services 276 EBR 7 5.04 small-cap Long NaN 62.0 5.620418e+06 37.0 Utilities 383 SBS 7 0.05 small-cap Long NaN 48.0 5.439540e+06 30.0 Utilities 274 KOF 6 0.36 small-cap Long NaN 6.0 5.130560e+06 34.0 Consumer Defensive 292 PSO 11 0.65 small-cap Long NaN 11.0 3.369426e+06 80.0 Communication Services 354 WF 7 4.50 small-cap Long NaN 65.0 3.515374e+05 51.0 Financial Services Mean Return: 6.790487804878049 Mean Day/Week: 13.78048780487805 Accuracy:0.926829268292683 *******************Part 2.8 Small Cap Short Maintaiance********************* small_short_signal_maintainance Symbol Day Return Market Cap Long/Short score MACD Signal Count Market Value Span Signal Count industry 324 MSTR 26 -39.97 small-cap Short NaN -62.0 3.668692e+08 -43.0 Technology 336 IOVA 9 -26.44 small-cap Short NaN -9.0 2.019111e+08 -64.0 Healthcare 262 BLDP 14 1.78 small-cap Short NaN -14.0 1.234003e+08 -57.0 Industrials 377 APPS 12 2.14 small-cap Short NaN -55.0 1.169113e+08 -26.0 Technology 382 ARRY 10 -27.11 small-cap Short NaN -10.0 8.637527e+07 -67.0 Technology 306 CHDN 12 -2.87 small-cap Short NaN -43.0 7.681601e+07 -22.0 Consumer Cyclical 379 VNET 18 -30.89 small-cap Short NaN -68.0 7.234234e+07 -56.0 Technology 308 RDFN 12 -0.07 small-cap Short NaN -12.0 6.967853e+07 -49.0 Real Estate 310 BL 12 0.75 small-cap Short NaN -12.0 6.962326e+07 -64.0 Technology 315 API 17 -19.19 small-cap Short NaN -17.0 6.434227e+07 -47.0 Technology 328 TGTX 15 -25.81 small-cap Short NaN -42.0 6.278768e+07 -50.0 Healthcare 344 BE 12 2.27 small-cap Short NaN -12.0 6.134148e+07 -61.0 Industrials 264 NVTA 15 -17.90 small-cap Short NaN -15.0 6.019120e+07 -68.0 Healthcare 272 BFAM 16 -5.99 small-cap Short NaN -31.0 5.503714e+07 -25.0 Consumer Cyclical 291 OHI 8 3.55 small-cap Short NaN -13.0 5.343598e+07 -14.0 Real Estate 286 WEX 10 -2.42 small-cap Short NaN -17.0 5.178217e+07 -17.0 Technology 309 YY 18 -12.12 small-cap Short NaN -55.0 4.843422e+07 -40.0 Communication Services 284 TWST 14 -14.13 small-cap Short NaN -14.0 4.696080e+07 -61.0 Healthcare 329 KOD 12 -21.94 small-cap Short NaN -12.0 4.607353e+07 -67.0 Healthcare 294 NYT 33 -15.31 small-cap Short NaN -56.0 4.530848e+07 -52.0 Communication Services 325 ONEM 10 -13.84 small-cap Short NaN -10.0 4.444747e+07 -56.0 Healthcare 350 HUYA 17 -20.05 small-cap Short NaN -59.0 4.232690e+07 -43.0 Communication Services 288 FATE 12 -2.83 small-cap Short NaN -12.0 4.224080e+07 -57.0 Healthcare 340 CYBR 13 6.02 small-cap Short NaN -13.0 4.200349e+07 -19.0 Technology 268 GWRE 11 -2.83 small-cap Short NaN -11.0 4.178408e+07 -64.0 Technology 352 SLAB 9 0.09 small-cap Short NaN -17.0 3.690137e+07 -17.0 Technology 376 BIGC 12 7.75 small-cap Short NaN -12.0 3.679209e+07 -69.0 Technology 334 ORA 13 -1.39 small-cap Short NaN -13.0 3.606210e+07 -61.0 Utilities 296 IONS 12 -2.79 small-cap Short NaN -12.0 3.521180e+07 -57.0 Healthcare 368 NATI 15 -0.28 small-cap Short NaN -16.0 3.453747e+07 -16.0 Technology 357 HAE 25 -26.26 small-cap Short NaN -25.0 3.410630e+07 -56.0 Healthcare 300 EXPI 28 -26.03 small-cap Short NaN -61.0 3.262708e+07 -43.0 Real Estate 313 MLCO 11 -4.74 small-cap Short NaN -43.0 3.221952e+07 -17.0 Consumer Cyclical 318 QTWO 12 -0.46 small-cap Short NaN -12.0 3.078720e+07 -58.0 Technology 360 VRNS 15 -8.58 small-cap Short NaN -15.0 2.935120e+07 -47.0 Technology 326 TTEK 9 -2.52 small-cap Short NaN -26.0 2.908148e+07 -20.0 Industrials 370 INSP 10 -0.75 small-cap Short NaN -13.0 2.870751e+07 -13.0 Healthcare 385 POWI 10 0.51 small-cap Short NaN -14.0 2.704849e+07 -56.0 Technology 339 SDGR 13 -6.63 small-cap Short NaN -13.0 2.620143e+07 -56.0 Healthcare 302 AMWL 13 -14.19 small-cap Short NaN -13.0 2.465734e+07 -64.0 Healthcare 372 SDC 13 -2.56 small-cap Short NaN -13.0 2.349636e+07 -67.0 Healthcare 362 FROG 12 -5.96 small-cap Short NaN -12.0 2.208907e+07 -64.0 Technology 342 MDLA 11 -5.22 small-cap Short NaN -11.0 1.888364e+07 -55.0 Technology 322 RCM 13 4.25 small-cap Short NaN -13.0 1.488420e+07 -15.0 Healthcare 346 NEO 13 -7.42 small-cap Short NaN -13.0 1.482194e+07 -19.0 Healthcare 345 FIZZ 17 -5.27 small-cap Short NaN -17.0 1.272572e+07 -51.0 Consumer Defensive 351 CWEN 11 0.28 small-cap Short NaN -11.0 1.206124e+07 -66.0 Utilities 375 CERT 7 3.99 small-cap Short NaN -7.0 8.436168e+06 -16.0 Healthcare 384 ENIC 17 -9.30 small-cap Short NaN -19.0 1.380524e+06 -67.0 Utilities Mean Return: -8.136326530612246 Mean Day/Week: 13.857142857142858 Accuracy:0.7551020408163265 ************************************** ************************************** ************************************** *******************Part 3.0 Penny Cap Industry Overiew********************* penny_industry_uptrending_count tickers industry Basic Materials 28 Communication Services 7 Consumer Cyclical 22 Consumer Defensive 9 Energy 22 Financial Services 43 Healthcare 21 Industrials 27 Real Estate 10 Technology 14 Utilities 1 unknown 3 **************************************** penny_industry_downtrending_count tickers industry Basic Materials 4 Communication Services 8 Consumer Cyclical 20 Consumer Defensive 9 Energy 5 Financial Services 20 Healthcare 78 Industrials 18 Real Estate 18 Technology 48 Utilities 1 unknown 1 *******************Part 3.1 Penny Cap Long Entry SPAN MACD********************* penny_long_signal_entry_span_macd Symbol Day Return Market Cap Long/Short score MACD Signal Count Market Value Span Signal Count industry 737 PACK 4 8.02 penny-cap Long NaN 4.0 9.549535e+06 5.0 Consumer Cyclical Mean Return: 8.02 Mean Day/Week: 4.0 Accuracy:1.0 *******************Part 3.2 Penny Cap Short Entry SPAN MACD********************* penny_short_signal_entry_span_macd Empty DataFrame Columns: [Symbol, Day, Return, Market Cap, Long/Short, score, MACD Signal Count, Market Value, Span Signal Count, industry] Index: [] Mean Return: nan Mean Day/Week: nan Accuracy:nan *******************Part 3.3 Penny Cap Long Entry SPAN********************* penny_long_signal_entry_span Symbol Day Return Market Cap Long/Short score MACD Signal Count Market Value Span Signal Count industry 649 CRC 4 2.74 penny-cap Long NaN 11.0 1.781249e+07 5.0 Energy 737 PACK 4 8.02 penny-cap Long NaN 4.0 9.549535e+06 5.0 Consumer Cyclical Mean Return: 5.38 Mean Day/Week: 4.0 Accuracy:1.0 *******************Part 3.4 Penny Cap Short Entry SPAN********************* penny_short_signal_entry_span Symbol Day Return Market Cap Long/Short score MACD Signal Count Market Value Span Signal Count industry 738 ASTE 4 -1.29 penny-cap Short NaN -29.0 1.793121e+07 -5.0 Industrials 462 TLS 3 9.75 penny-cap Short NaN -33.0 1.773046e+07 -4.0 Technology 792 PLUS 1 0.00 penny-cap Short NaN -42.0 1.618017e+07 -3.0 Technology 579 COOP 1 0.00 penny-cap Short NaN -39.0 1.188020e+07 -4.0 Financial Services 703 UIS 1 0.00 penny-cap Short NaN -63.0 6.127797e+06 -3.0 Technology 800 ZUMZ 3 -3.08 penny-cap Short NaN -65.0 5.934912e+06 -4.0 Consumer Cyclical 817 ENTA 5 0.18 penny-cap Short NaN -11.0 5.727020e+06 -5.0 Healthcare Mean Return: 1.3900000000000001 Mean Day/Week: 4.5 Accuracy:0.5 *******************Part 3.5 Penny Cap Long Entry MACD********************* penny_long_signal_entry_macd Symbol Day Return Market Cap Long/Short score MACD Signal Count Market Value Span Signal Count industry 812 PLCE 5 2.26 penny-cap Long NaN 5.0 7.902182e+07 6.0 Consumer Cyclical 447 SWAV 2 -0.09 penny-cap Long NaN 2.0 6.133124e+07 38.0 Healthcare 711 DEN 2 2.29 penny-cap Long NaN 2.0 6.086065e+07 80.0 Energy 414 SLG 1 0.00 penny-cap Long NaN 1.0 5.847459e+07 64.0 Real Estate 736 CORE 2 -1.49 penny-cap Long NaN 2.0 5.488638e+07 64.0 Consumer Defensive 440 PGNY 2 1.43 penny-cap Long NaN 2.0 4.648384e+07 31.0 Healthcare 497 AQUA 1 0.00 penny-cap Long NaN 1.0 4.192801e+07 31.0 Industrials 474 STL 1 0.00 penny-cap Long NaN 1.0 3.868365e+07 80.0 Financial Services 391 DEI 1 0.00 penny-cap Long NaN 2.0 3.457293e+07 63.0 Real Estate 715 CNR 4 9.59 penny-cap Long NaN 4.0 2.868237e+07 80.0 Industrials 525 EBC 1 0.00 penny-cap Long NaN 1.0 2.202652e+07 80.0 Financial Services 586 CIM 4 0.73 penny-cap Long NaN 4.0 2.162039e+07 80.0 Real Estate 500 VG 3 2.84 penny-cap Long NaN 3.0 2.121732e+07 7.0 Communication Services 798 EXTR 2 -3.24 penny-cap Long NaN 2.0 2.092014e+07 33.0 Technology 549 WERN 3 -0.10 penny-cap Long NaN 5.0 1.928874e+07 72.0 Industrials 679 VRTS 4 0.41 penny-cap Long NaN 4.0 1.249336e+07 80.0 Financial Services 788 TVTY 1 0.00 penny-cap Long NaN 2.0 1.110004e+07 24.0 Healthcare 626 PVG 5 -1.50 penny-cap Long NaN 5.0 1.092154e+07 15.0 Basic Materials 737 PACK 4 8.02 penny-cap Long NaN 4.0 9.549535e+06 5.0 Consumer Cyclical 630 WAFD 5 0.04 penny-cap Long NaN 5.0 7.958413e+06 80.0 Financial Services 776 RAVN 1 0.00 penny-cap Long NaN 5.0 7.820562e+06 23.0 Industrials 638 SHEN 1 0.00 penny-cap Long NaN 1.0 7.301782e+06 15.0 Communication Services 668 MGRC 5 1.01 penny-cap Long NaN 5.0 6.055288e+06 7.0 Industrials 797 SAFT 2 0.17 penny-cap Long NaN 2.0 4.868920e+06 63.0 Financial Services 501 NAD 2 -0.06 penny-cap Long NaN 2.0 4.276603e+06 33.0 Financial Services 566 EXG 4 0.93 penny-cap Long NaN 4.0 4.222478e+06 55.0 Financial Services 719 EVV 2 0.00 penny-cap Long NaN 2.0 3.519692e+06 6.0 Financial Services 742 ETV 2 0.00 penny-cap Long NaN 2.0 2.995899e+06 39.0 Financial Services 659 ETY 2 -0.22 penny-cap Long NaN 2.0 2.518742e+06 52.0 Financial Services 782 ARKO 3 -0.56 penny-cap Long NaN 3.0 2.346628e+06 59.0 Consumer Cyclical 807 ETW 1 0.00 penny-cap Long NaN 1.0 2.234994e+06 53.0 Financial Services 821 SRCE 1 0.00 penny-cap Long NaN 1.0 1.884672e+06 80.0 Financial Services 717 VCTR 4 0.27 penny-cap Long NaN 4.0 1.741823e+06 55.0 Financial Services 796 LOMA 2 -1.65 penny-cap Long NaN 5.0 7.763896e+05 51.0 Basic Materials 544 TDI 5 0.69 penny-cap Long NaN 5.0 1.375711e+05 35.0 unknown Mean Return: 0.8707999999999998 Mean Day/Week: 3.6 Accuracy:0.56 *******************Part 3.6 Penny Cap Short Entry MACD********************* penny_short_signal_entry_macd Symbol Day Return Market Cap Long/Short score MACD Signal Count Market Value Span Signal Count industry 503 TRN 4 2.94 penny-cap Short NaN -4.0 1.361782e+07 -46.0 Industrials 759 CHRS 1 0.00 penny-cap Short NaN -1.0 9.813049e+06 -68.0 Healthcare 795 STRO 2 -0.49 penny-cap Short NaN -2.0 6.726247e+06 -43.0 Healthcare 801 RES 2 0.00 penny-cap Short NaN -2.0 4.309561e+06 -26.0 Energy 733 WMK 3 -0.72 penny-cap Short NaN -3.0 2.969554e+06 -24.0 Consumer Defensive 793 APSG 2 0.21 penny-cap Short NaN -2.0 1.423675e+06 -58.0 Financial Services 819 CONX 1 0.00 penny-cap Short NaN -1.0 1.282001e+06 -105.0 Financial Services 784 PRPB 4 -0.10 penny-cap Short NaN -4.0 1.218976e+06 -57.0 Financial Services 652 OFLX 1 0.00 penny-cap Short NaN -1.0 4.755332e+05 -49.0 Industrials Mean Return: 0.30666666666666664 Mean Day/Week: 3.3333333333333335 Accuracy:0.5 *******************Part 3.7 Penny Cap Long Maintainance********************* penny_long_signal_maintainance Symbol Day Return Market Cap Long/Short score MACD Signal Count Market Value Span Signal Count industry 410 WEN 6 0.83 penny-cap Long NaN 52.0 1.211114e+08 34.0 Consumer Cyclical 493 HRB 10 7.82 penny-cap Long NaN 10.0 9.764557e+07 78.0 Consumer Cyclical 459 HL 10 20.93 penny-cap Long NaN 12.0 9.487785e+07 12.0 Basic Materials 401 EQT 11 8.48 penny-cap Long NaN 17.0 9.269635e+07 20.0 Energy 418 NCR 7 5.36 penny-cap Long NaN 35.0 8.246841e+07 41.0 Technology 487 IGT 9 16.68 penny-cap Long NaN 17.0 8.232064e+07 9.0 Consumer Cyclical 423 VVV 26 18.74 penny-cap Long NaN 26.0 8.142056e+07 80.0 Energy 582 RRC 8 17.45 penny-cap Long NaN 15.0 7.133501e+07 15.0 Energy 396 AUY 12 4.14 penny-cap Long NaN 50.0 6.939103e+07 27.0 Basic Materials 644 AR 11 17.49 penny-cap Long NaN 11.0 6.753883e+07 80.0 Energy 556 ELY 9 0.66 penny-cap Long NaN 23.0 6.643143e+07 12.0 Consumer Cyclical 393 UNM 7 0.33 penny-cap Long NaN 12.0 6.253495e+07 80.0 Financial Services 434 RYN 7 0.07 penny-cap Long NaN 32.0 6.252787e+07 36.0 Real Estate 558 SWN 10 15.84 penny-cap Long NaN 13.0 5.639308e+07 19.0 Energy 677 UFS 20 34.28 penny-cap Long NaN 20.0 5.283717e+07 80.0 Basic Materials 539 ABCB 8 7.69 penny-cap Long NaN 13.0 4.851835e+07 80.0 Financial Services 519 COMM 11 1.48 penny-cap Long NaN 11.0 4.733588e+07 80.0 Technology 422 MSM 19 3.02 penny-cap Long NaN 19.0 4.639624e+07 55.0 Industrials 645 DDS 6 7.92 penny-cap Long NaN 6.0 4.513227e+07 21.0 Consumer Cyclical 435 SWCH 6 2.98 penny-cap Long NaN 37.0 4.395723e+07 28.0 Technology 389 BPOP 13 5.16 penny-cap Long NaN 13.0 4.393860e+07 80.0 Financial Services 424 XEC 13 1.95 penny-cap Long NaN 13.0 4.391562e+07 80.0 Energy 814 SBLK 23 22.07 penny-cap Long NaN 23.0 4.222132e+07 80.0 Industrials 658 EPC 12 4.48 penny-cap Long NaN 12.0 4.148431e+07 50.0 Consumer Defensive 657 MUR 8 5.17 penny-cap Long NaN 12.0 4.027596e+07 20.0 Energy 790 RGR 9 6.32 penny-cap Long NaN 10.0 3.903437e+07 12.0 Industrials 688 PAGP 11 9.96 penny-cap Long NaN 17.0 3.881302e+07 21.0 Energy 413 JOBS 12 2.58 penny-cap Long NaN 34.0 3.686118e+07 14.0 Industrials 651 PRFT 17 11.74 penny-cap Long NaN 17.0 3.558425e+07 80.0 Technology 635 ASO 12 -0.38 penny-cap Long NaN 12.0 3.428339e+07 80.0 Consumer Cyclical 565 BNL 17 5.36 penny-cap Long NaN 36.0 3.378105e+07 27.0 Real Estate 540 ARNC 15 19.01 penny-cap Long NaN 29.0 3.366681e+07 28.0 Industrials 427 CC 15 10.27 penny-cap Long NaN 39.0 3.272462e+07 40.0 Basic Materials 448 WCC 18 13.14 penny-cap Long NaN 18.0 3.195852e+07 80.0 Industrials 502 UNVR 21 17.29 penny-cap Long NaN 40.0 3.150905e+07 55.0 Basic Materials 465 SGMS 19 15.54 penny-cap Long NaN 27.0 3.133085e+07 23.0 Consumer Cyclical 650 MTDR 14 7.38 penny-cap Long NaN 14.0 3.041062e+07 80.0 Energy 571 SUM 11 7.02 penny-cap Long NaN 11.0 3.015112e+07 80.0 Basic Materials 697 HOME 13 18.14 penny-cap Long NaN 20.0 3.013331e+07 80.0 Consumer Cyclical 461 AM 10 7.13 penny-cap Long NaN 13.0 2.925877e+07 23.0 Energy 484 AMN 17 11.04 penny-cap Long NaN 23.0 2.679063e+07 23.0 Healthcare 471 PACW 7 3.05 penny-cap Long NaN 18.0 2.658123e+07 80.0 Financial Services 531 NUS 10 1.21 penny-cap Long NaN 30.0 2.603844e+07 12.0 Consumer Defensive 552 RRR 18 4.90 penny-cap Long NaN 18.0 2.575824e+07 80.0 Consumer Cyclical 547 HMY 6 9.94 penny-cap Long NaN 56.0 2.565174e+07 36.0 Basic Materials 580 MIC 14 4.28 penny-cap Long NaN 14.0 2.537754e+07 33.0 Industrials 506 AGI 6 4.43 penny-cap Long NaN 53.0 2.529913e+07 33.0 Basic Materials 520 CHX 8 2.62 penny-cap Long NaN 13.0 2.414991e+07 23.0 Energy 560 EXLS 13 3.47 penny-cap Long NaN 13.0 2.383664e+07 57.0 Technology 614 MLHR 10 0.07 penny-cap Long NaN 10.0 2.321059e+07 77.0 Consumer Cyclical 456 SSRM 6 7.28 penny-cap Long NaN 49.0 2.301077e+07 18.0 Basic Materials 543 MIME 9 4.55 penny-cap Long NaN 28.0 2.294886e+07 11.0 Technology 405 ALSN 7 -1.81 penny-cap Long NaN 11.0 2.264224e+07 34.0 Consumer Cyclical 774 FOE 9 -1.00 penny-cap Long NaN 9.0 2.230748e+07 73.0 Basic Materials 553 MED 10 6.74 penny-cap Long NaN 13.0 2.218628e+07 12.0 Consumer Cyclical 594 PDCE 11 -1.48 penny-cap Long NaN 11.0 2.186896e+07 80.0 Energy 561 MDRX 6 1.43 penny-cap Long NaN 26.0 2.116349e+07 9.0 Healthcare 629 TDS 10 0.07 penny-cap Long NaN 10.0 2.025246e+07 55.0 Communication Services 815 CTS 6 9.88 penny-cap Long NaN 20.0 1.984981e+07 13.0 Technology 515 GOLF 12 -0.51 penny-cap Long NaN 12.0 1.968399e+07 12.0 Consumer Cyclical 574 EAF 7 5.55 penny-cap Long NaN 18.0 1.904143e+07 80.0 Industrials 578 PRMW 6 1.23 penny-cap Long NaN 6.0 1.892492e+07 36.0 Consumer Defensive 527 CNO 7 -0.97 penny-cap Long NaN 13.0 1.884666e+07 80.0 Financial Services 663 VSTO 8 7.12 penny-cap Long NaN 14.0 1.856878e+07 15.0 Consumer Cyclical 400 FLS 7 0.37 penny-cap Long NaN 14.0 1.850266e+07 80.0 Industrials 713 AAWW 7 1.44 penny-cap Long NaN 38.0 1.842119e+07 67.0 Industrials 616 NAVI 52 29.35 penny-cap Long NaN 52.0 1.779239e+07 80.0 Financial Services 568 CBT 15 11.55 penny-cap Long NaN 21.0 1.623446e+07 80.0 Basic Materials 681 IRWD 11 11.65 penny-cap Long NaN 22.0 1.593400e+07 30.0 Healthcare 674 RLGY 10 5.05 penny-cap Long NaN 17.0 1.591910e+07 17.0 Real Estate 584 MD 11 -1.46 penny-cap Long NaN 11.0 1.534938e+07 36.0 Healthcare 628 OI 21 17.58 penny-cap Long NaN 40.0 1.534341e+07 41.0 Consumer Cyclical 746 XPEL 10 12.73 penny-cap Long NaN 10.0 1.505955e+07 36.0 Consumer Cyclical 783 FDP 13 0.99 penny-cap Long NaN 13.0 1.459665e+07 60.0 Consumer Defensive 516 PDCO 14 -1.92 penny-cap Long NaN 15.0 1.427161e+07 21.0 Healthcare 757 SAND 9 4.61 penny-cap Long NaN 54.0 1.420173e+07 36.0 Basic Materials 623 SGRY 20 5.76 penny-cap Long NaN 20.0 1.367635e+07 80.0 Healthcare 508 SXT 7 2.28 penny-cap Long NaN 27.0 1.365386e+07 55.0 Basic Materials 541 NGVT 8 -0.37 penny-cap Long NaN 17.0 1.364422e+07 23.0 Basic Materials 639 CVA 10 -0.03 penny-cap Long NaN 16.0 1.355060e+07 16.0 Industrials 406 HLI 8 5.37 penny-cap Long NaN 8.0 1.339921e+07 8.0 Financial Services 524 HWC 20 9.06 penny-cap Long NaN 20.0 1.327165e+07 80.0 Financial Services 754 RPTX 6 5.96 penny-cap Long NaN 37.0 1.315153e+07 20.0 Healthcare 671 GTN 7 2.09 penny-cap Long NaN 25.0 1.285270e+07 80.0 Communication Services 587 ANAT 12 24.65 penny-cap Long NaN 12.0 1.238940e+07 57.0 Financial Services 598 PLXS 6 0.79 penny-cap Long NaN 6.0 1.223527e+07 80.0 Technology 573 EVTC 14 6.54 penny-cap Long NaN 36.0 1.198651e+07 36.0 Technology 789 CYH 13 -1.71 penny-cap Long NaN 13.0 1.185098e+07 16.0 Healthcare 655 CSTM 14 3.13 penny-cap Long NaN 20.0 1.168390e+07 55.0 Basic Materials 627 PBH 10 5.45 penny-cap Long NaN 10.0 1.149345e+07 15.0 Healthcare 595 ENBL 17 16.10 penny-cap Long NaN 17.0 1.127449e+07 71.0 Energy 577 COKE 6 6.76 penny-cap Long NaN 6.0 1.079867e+07 55.0 Consumer Defensive 563 AUB 10 6.12 penny-cap Long NaN 12.0 1.050050e+07 80.0 Financial Services 438 UBSI 7 -0.52 penny-cap Long NaN 7.0 1.043609e+07 80.0 Financial Services 603 MTX 12 1.98 penny-cap Long NaN 12.0 1.001493e+07 80.0 Basic Materials 589 FCFS 21 12.73 penny-cap Long NaN 28.0 9.819865e+06 56.0 Financial Services 686 GOL 11 -0.92 penny-cap Long NaN 34.0 9.675161e+06 22.0 Industrials 535 GHC 11 2.94 penny-cap Long NaN 11.0 9.566039e+06 36.0 Consumer Defensive 534 JJSF 17 3.73 penny-cap Long NaN 22.0 9.147471e+06 26.0 Consumer Defensive 612 BDC 11 -4.22 penny-cap Long NaN 13.0 8.793312e+06 13.0 Industrials 620 ENLC 6 5.12 penny-cap Long NaN 6.0 8.781015e+06 6.0 Energy 431 CEF 8 2.43 penny-cap Long NaN 31.0 8.688026e+06 15.0 unknown 758 CASH 17 1.90 penny-cap Long NaN 17.0 8.120916e+06 80.0 Financial Services 498 CWK 15 4.33 penny-cap Long NaN 15.0 7.779257e+06 55.0 Real Estate 511 NG 7 8.54 penny-cap Long NaN 12.0 7.466662e+06 32.0 Basic Materials 770 VLRS 19 6.76 penny-cap Long NaN 19.0 7.353936e+06 80.0 Industrials 634 WSBC 7 1.49 penny-cap Long NaN 7.0 7.247721e+06 80.0 Financial Services 680 PIPR 7 2.89 penny-cap Long NaN 7.0 6.575720e+06 75.0 Financial Services 767 WIRE 20 11.38 penny-cap Long NaN 20.0 6.546580e+06 80.0 Industrials 741 HSC 11 0.15 penny-cap Long NaN 28.0 6.406695e+06 14.0 Industrials 802 SBSI 14 4.71 penny-cap Long NaN 14.0 6.154114e+06 80.0 Financial Services 643 OR 15 11.08 penny-cap Long NaN 50.0 5.600958e+06 33.0 Basic Materials 791 FCF 7 2.54 penny-cap Long NaN 7.0 5.499606e+06 80.0 Financial Services 463 TIGO 8 1.45 penny-cap Long NaN 15.0 5.329343e+06 17.0 Communication Services 499 CNS 10 3.88 penny-cap Long NaN 36.0 4.982256e+06 21.0 Financial Services 803 AUDC 7 6.45 penny-cap Long NaN 32.0 4.695275e+06 13.0 Technology 761 NMRK 11 -0.87 penny-cap Long NaN 11.0 4.617254e+06 80.0 Real Estate 551 USM 9 -0.83 penny-cap Long NaN 9.0 4.576664e+06 55.0 Communication Services 731 PFS 7 0.52 penny-cap Long NaN 14.0 4.566158e+06 80.0 Financial Services 743 FFG 15 0.35 penny-cap Long NaN 15.0 4.046945e+06 15.0 Financial Services 809 ARCO 17 8.44 penny-cap Long NaN 26.0 3.870072e+06 20.0 Consumer Cyclical 724 HNI 7 1.78 penny-cap Long NaN 17.0 3.788909e+06 59.0 Industrials 769 NXE 6 8.70 penny-cap Long NaN 13.0 3.652871e+06 18.0 Energy 773 BPMP 13 4.67 penny-cap Long NaN 20.0 3.390435e+06 57.0 Energy 822 BRKL 7 2.28 penny-cap Long NaN 14.0 3.164370e+06 80.0 Financial Services 530 BBU 17 6.26 penny-cap Long NaN 19.0 2.003244e+06 20.0 Industrials 804 CLNC 7 4.44 penny-cap Long NaN 12.0 1.746265e+06 7.0 Real Estate 691 DKL 6 3.63 penny-cap Long NaN 30.0 1.208788e+06 8.0 Energy 555 CIXX 7 6.05 penny-cap Long NaN 32.0 6.027097e+05 78.0 Financial Services 395 SHI 10 0.72 penny-cap Long NaN 10.0 3.973548e+05 32.0 Energy 664 SIM 7 6.43 penny-cap Long NaN 28.0 7.344436e+03 29.0 Basic Materials Mean Return: 6.090763358778626 Mean Day/Week: 11.458015267175572 Accuracy:0.8778625954198473 *******************Part 3.8 Penny Cap Short Maintainance********************* penny_short_signal_maintainance Symbol Day Return Market Cap Long/Short score MACD Signal Count Market Value Span Signal Count industry 748 RIOT 10 -22.97 penny-cap Short NaN -43.0 5.526092e+08 -27.0 Technology 455 APHA 14 -10.00 penny-cap Short NaN -57.0 2.524698e+08 -40.0 Healthcare 550 TLRY 15 -8.05 penny-cap Short NaN -62.0 2.334945e+08 -42.0 Healthcare 751 SNDL 17 -15.25 penny-cap Short NaN -60.0 1.460616e+08 -41.0 Healthcare 624 KURA 14 -12.20 penny-cap Short NaN -14.0 9.029191e+07 -139.0 Healthcare 386 ALLO 16 -16.36 penny-cap Short NaN -17.0 6.760280e+07 -18.0 Healthcare 567 TIGR 12 3.12 penny-cap Short NaN -12.0 5.941738e+07 -42.0 Financial Services 482 VCYT 18 -28.12 penny-cap Short NaN -26.0 5.537704e+07 -48.0 Healthcare 426 NOVA 14 -6.81 penny-cap Short NaN -14.0 5.534578e+07 -58.0 Technology 583 NNDM 17 -20.10 penny-cap Short NaN -66.0 5.405095e+07 -57.0 Technology 473 VLDR 9 -10.91 penny-cap Short NaN -9.0 5.318850e+07 -68.0 Technology 402 SHAK 16 -24.19 penny-cap Short NaN -62.0 5.191867e+07 -32.0 Consumer Cyclical 478 SABR 6 -3.75 penny-cap Short NaN -43.0 4.609406e+07 -14.0 Technology 449 CCXI 14 -66.49 penny-cap Short NaN -14.0 3.693361e+07 -56.0 Healthcare 450 LOPE 10 -0.92 penny-cap Short NaN -38.0 3.605091e+07 -12.0 Consumer Defensive 404 EVBG 14 -3.16 penny-cap Short NaN -14.0 3.537870e+07 -55.0 Technology 477 SBRA 8 3.84 penny-cap Short NaN -15.0 3.525039e+07 -16.0 Real Estate 613 REAL 11 -37.57 penny-cap Short NaN -11.0 3.277017e+07 -16.0 Consumer Cyclical 619 CLNE 17 -37.02 penny-cap Short NaN -62.0 3.250520e+07 -35.0 Energy 390 GSHD 9 -12.50 penny-cap Short NaN -9.0 3.208548e+07 -46.0 Financial Services 397 NARI 10 -3.55 penny-cap Short NaN -14.0 3.171565e+07 -14.0 Healthcare 467 VC 8 0.37 penny-cap Short NaN -8.0 3.121914e+07 -46.0 Consumer Cyclical 399 ALRM 14 -6.77 penny-cap Short NaN -14.0 3.077979e+07 -18.0 Technology 443 PD 13 -6.50 penny-cap Short NaN -13.0 2.978337e+07 -57.0 Technology 546 EAT 9 -4.21 penny-cap Short NaN -44.0 2.882842e+07 -16.0 Consumer Cyclical 606 ACB 7 4.21 penny-cap Short NaN -7.0 2.865710e+07 -48.0 Healthcare 485 EQC 12 0.46 penny-cap Short NaN -13.0 2.661847e+07 -13.0 Real Estate 618 YEXT 15 -8.18 penny-cap Short NaN -15.0 2.517934e+07 -56.0 Technology 451 GKOS 10 -1.89 penny-cap Short NaN -11.0 2.403209e+07 -12.0 Healthcare 494 CPA 7 0.66 penny-cap Short NaN -7.0 2.338806e+07 -9.0 Industrials 433 DOYU 17 -24.83 penny-cap Short NaN -57.0 2.321079e+07 -43.0 Communication Services 421 BAND 9 -0.28 penny-cap Short NaN -9.0 2.316889e+07 -61.0 Technology 466 EGHT 15 -29.27 penny-cap Short NaN -15.0 2.301062e+07 -17.0 Technology 417 ARNA 13 -2.18 penny-cap Short NaN -13.0 2.293380e+07 -46.0 Healthcare 492 CDLX 12 -3.73 penny-cap Short NaN -12.0 2.241561e+07 -17.0 Communication Services 488 SLQT 8 -6.08 penny-cap Short NaN -24.0 2.163976e+07 -10.0 Financial Services 464 GOOS 7 2.43 penny-cap Short NaN -50.0 2.156859e+07 -16.0 Consumer Cyclical 532 PHR 14 -6.58 penny-cap Short NaN -14.0 2.142895e+07 -49.0 Healthcare 517 GBT 13 -4.85 penny-cap Short NaN -13.0 2.090782e+07 -61.0 Healthcare 559 MAXR 14 2.75 penny-cap Short NaN -14.0 2.082359e+07 -48.0 Technology 460 INSM 13 -13.79 penny-cap Short NaN -13.0 1.971603e+07 -49.0 Healthcare 394 SAGE 12 3.61 penny-cap Short NaN -12.0 1.962914e+07 -14.0 Healthcare 489 IRBT 17 -15.49 penny-cap Short NaN -62.0 1.943196e+07 -51.0 Technology 521 NHI 8 1.59 penny-cap Short NaN -31.0 1.924189e+07 -14.0 Real Estate 696 LTC 11 -4.05 penny-cap Short NaN -43.0 1.909793e+07 -15.0 Real Estate 407 BLI 7 15.83 penny-cap Short NaN -7.0 1.896753e+07 -139.0 Healthcare 458 AHCO 28 -12.80 penny-cap Short NaN -29.0 1.866266e+07 -29.0 Healthcare 432 CRNC 12 -3.76 penny-cap Short NaN -12.0 1.858099e+07 -48.0 Technology 480 CNNE 9 1.79 penny-cap Short NaN -18.0 1.842132e+07 -14.0 Consumer Cyclical 454 MRCY 11 -0.81 penny-cap Short NaN -13.0 1.762853e+07 -13.0 Industrials 601 OM 7 10.37 penny-cap Short NaN -12.0 1.750312e+07 -12.0 Healthcare 545 GDOT 9 -3.48 penny-cap Short NaN -9.0 1.727904e+07 -139.0 Financial Services 491 CRSR 8 0.86 penny-cap Short NaN -8.0 1.717800e+07 -61.0 Technology 505 NSTG 9 -12.50 penny-cap Short NaN -12.0 1.702871e+07 -14.0 Healthcare 483 NIU 13 -8.72 penny-cap Short NaN -13.0 1.699844e+07 -43.0 Consumer Cyclical 740 PLT 6 8.41 penny-cap Short NaN -57.0 1.685700e+07 -8.0 Technology 392 SMTC 15 -6.41 penny-cap Short NaN -15.0 1.670544e+07 -19.0 Technology 437 KNSL 8 5.83 penny-cap Short NaN -8.0 1.669195e+07 -139.0 Financial Services 647 MTOR 9 -2.52 penny-cap Short NaN -45.0 1.597415e+07 -37.0 Consumer Cyclical 569 DCPH 13 -10.81 penny-cap Short NaN -13.0 1.559901e+07 -15.0 Healthcare 710 PAYA 25 -9.42 penny-cap Short NaN -46.0 1.542733e+07 -66.0 Technology 599 TTGT 10 4.47 penny-cap Short NaN -13.0 1.516692e+07 -43.0 Communication Services 611 EAR 12 -25.19 penny-cap Short NaN -12.0 1.498273e+07 -46.0 Healthcare 538 LGND 18 -17.05 penny-cap Short NaN -66.0 1.497269e+07 -43.0 Healthcare 512 VSAT 8 -0.47 penny-cap Short NaN -13.0 1.413219e+07 -30.0 Technology 604 QTRX 14 -8.14 penny-cap Short NaN -14.0 1.372990e+07 -54.0 Healthcare 709 PAR 7 9.67 penny-cap Short NaN -12.0 1.343475e+07 -13.0 Technology 682 HRTX 6 -0.76 penny-cap Short NaN -7.0 1.338976e+07 -7.0 Healthcare 398 CVET 8 2.88 penny-cap Short NaN -8.0 1.324771e+07 -48.0 Healthcare 522 FRHC 9 0.31 penny-cap Short NaN -30.0 1.321676e+07 -31.0 Financial Services 766 ICPT 13 -9.71 penny-cap Short NaN -13.0 1.305104e+07 -64.0 Healthcare 533 UNIT 9 3.32 penny-cap Short NaN -9.0 1.305021e+07 -58.0 Real Estate 605 PUBM 12 -8.00 penny-cap Short NaN -54.0 1.249008e+07 -27.0 Technology 575 ESE 8 -4.69 penny-cap Short NaN -29.0 1.237665e+07 -10.0 Technology 602 STRA 17 -5.00 penny-cap Short NaN -28.0 1.190024e+07 -25.0 Consumer Defensive 495 FORM 16 -13.18 penny-cap Short NaN -17.0 1.165339e+07 -17.0 Technology 479 SEER 14 -34.41 penny-cap Short NaN -14.0 1.129772e+07 -116.0 Healthcare 662 DEA 9 1.67 penny-cap Short NaN -9.0 1.123206e+07 -61.0 Real Estate 526 ALXO 8 0.04 penny-cap Short NaN -8.0 1.116588e+07 -72.0 Healthcare 570 MATX 8 3.99 penny-cap Short NaN -45.0 1.106627e+07 -24.0 Industrials 670 CSTL 13 -5.63 penny-cap Short NaN -13.0 1.082945e+07 -55.0 Healthcare 600 EPAY 13 -1.66 penny-cap Short NaN -13.0 1.076146e+07 -14.0 Technology 666 ACMR 17 -17.81 penny-cap Short NaN -60.0 1.065050e+07 -42.0 Technology 729 OCUL 9 -13.33 penny-cap Short NaN -9.0 1.036494e+07 -16.0 Healthcare 608 AVNS 16 -7.60 penny-cap Short NaN -16.0 1.025975e+07 -46.0 Healthcare 436 RXT 8 11.39 penny-cap Short NaN -21.0 1.020805e+07 -9.0 Technology 725 VCRA 16 -4.93 penny-cap Short NaN -16.0 1.010192e+07 -50.0 Technology 469 NKTR 9 -6.60 penny-cap Short NaN -54.0 9.977963e+06 -40.0 Healthcare 744 CVGW 8 1.48 penny-cap Short NaN -20.0 9.957420e+06 -16.0 Consumer Defensive 476 SPSC 9 -0.07 penny-cap Short NaN -14.0 9.863100e+06 -19.0 Technology 648 PRO 15 -9.86 penny-cap Short NaN -15.0 9.564074e+06 -18.0 Technology 745 JRVR 12 -3.44 penny-cap Short NaN -19.0 8.872154e+06 -24.0 Financial Services 669 SVC 9 -2.22 penny-cap Short NaN -43.0 8.737991e+06 -16.0 Real Estate 528 ROCK 9 -0.90 penny-cap Short NaN -13.0 8.618405e+06 -13.0 Industrials 780 LOTZ 9 -3.17 penny-cap Short NaN -9.0 8.590880e+06 -100.0 Consumer Cyclical 475 CRON 17 -11.09 penny-cap Short NaN -63.0 8.462447e+06 -44.0 Healthcare 592 BTRS 12 -11.48 penny-cap Short NaN -12.0 8.450614e+06 -42.0 Technology 513 AMRN 34 -31.64 penny-cap Short NaN -64.0 8.403118e+06 -46.0 Healthcare 641 ARCE 6 -2.73 penny-cap Short NaN -6.0 8.307741e+06 -73.0 Consumer Defensive 805 AKRO 12 2.54 penny-cap Short NaN -12.0 8.198217e+06 -16.0 Healthcare 787 TPGY 11 -21.31 penny-cap Short NaN -11.0 8.098013e+06 -57.0 Financial Services 721 IMGN 13 -15.12 penny-cap Short NaN -56.0 7.926671e+06 -39.0 Healthcare 753 TRIL 6 -4.14 penny-cap Short NaN -6.0 7.857759e+06 -139.0 Healthcare 811 PASG 6 -10.21 penny-cap Short NaN -6.0 7.842607e+06 -139.0 Healthcare 718 UPLD 12 -0.40 penny-cap Short NaN -13.0 7.714071e+06 -14.0 Technology 452 OPK 18 -12.05 penny-cap Short NaN -65.0 7.576298e+06 -52.0 Healthcare 576 INDB 10 0.23 penny-cap Short NaN -43.0 7.087838e+06 -27.0 Financial Services 700 ATSG 12 -3.83 penny-cap Short NaN -30.0 6.877021e+06 -23.0 Industrials 640 PRAX 7 5.83 penny-cap Short NaN -7.0 6.823564e+06 -61.0 Healthcare 813 DENN 7 4.46 penny-cap Short NaN -13.0 6.680433e+06 -12.0 Consumer Cyclical 610 MYGN 6 0.59 penny-cap Short NaN -13.0 6.636327e+06 -13.0 Healthcare 750 CEVA 16 -22.82 penny-cap Short NaN -61.0 5.946326e+06 -43.0 Technology 706 CDXS 12 -0.44 penny-cap Short NaN -15.0 5.548800e+06 -17.0 Healthcare 617 ADCT 10 -6.84 penny-cap Short NaN -10.0 5.478344e+06 -139.0 Healthcare 564 XNCR 12 1.36 penny-cap Short NaN -12.0 5.394032e+06 -46.0 Healthcare 672 PGEN 13 3.85 penny-cap Short NaN -13.0 5.179451e+06 -48.0 Healthcare 728 NRIX 11 -7.74 penny-cap Short NaN -11.0 5.164938e+06 -46.0 Healthcare 755 EVER 16 -16.12 penny-cap Short NaN -58.0 5.085275e+06 -50.0 Communication Services 419 JAMF 13 -7.62 penny-cap Short NaN -15.0 5.026462e+06 -15.0 Technology 453 RSI 17 -10.26 penny-cap Short NaN -44.0 4.984531e+06 -45.0 Consumer Cyclical 723 MASS 10 -19.35 penny-cap Short NaN -10.0 4.975350e+06 -106.0 Healthcare 707 LKFN 9 0.83 penny-cap Short NaN -44.0 4.919625e+06 -19.0 Financial Services 756 VERI 15 -19.81 penny-cap Short NaN -15.0 4.781351e+06 -55.0 Technology 654 AZRE 16 -8.51 penny-cap Short NaN -16.0 4.324124e+06 -68.0 Utilities 637 SRRK 14 -11.39 penny-cap Short NaN -42.0 4.300505e+06 -42.0 Healthcare 765 MODN 9 -3.15 penny-cap Short NaN -13.0 4.192470e+06 -17.0 Technology 752 KROS 10 1.09 penny-cap Short NaN -13.0 4.071648e+06 -16.0 Healthcare 692 PNTG 18 -23.34 penny-cap Short NaN -88.0 4.032676e+06 -64.0 Healthcare 768 ADVM 17 -9.50 penny-cap Short NaN -17.0 3.977966e+06 -58.0 Healthcare 712 BCAB 10 -7.33 penny-cap Short NaN -39.0 3.877723e+06 -32.0 Healthcare 730 MRSN 10 -5.81 penny-cap Short NaN -10.0 3.842450e+06 -139.0 Healthcare 632 STTK 8 -8.55 penny-cap Short NaN -8.0 3.765429e+06 -64.0 Healthcare 818 VITL 12 -5.81 penny-cap Short NaN -12.0 3.734999e+06 -45.0 Consumer Defensive 747 KRYS 10 0.20 penny-cap Short NaN -14.0 3.715387e+06 -15.0 Healthcare 694 SLP 14 -6.37 penny-cap Short NaN -14.0 3.695170e+06 -48.0 Healthcare 726 KNSA 14 -11.49 penny-cap Short NaN -47.0 3.534116e+06 -44.0 Healthcare 665 SPNS 13 -6.77 penny-cap Short NaN -14.0 3.438497e+06 -14.0 Technology 808 DHC 14 -15.71 penny-cap Short NaN -42.0 3.435070e+06 -24.0 Real Estate 799 MORF 13 -5.12 penny-cap Short NaN -40.0 3.336288e+06 -29.0 Healthcare 816 SRG 14 -8.49 penny-cap Short NaN -44.0 3.182126e+06 -31.0 Real Estate 588 MANU 6 -1.64 penny-cap Short NaN -10.0 3.097044e+06 -15.0 Consumer Cyclical 735 ALX 8 0.01 penny-cap Short NaN -40.0 2.664119e+06 -22.0 Real Estate 763 CLLS 13 -5.64 penny-cap Short NaN -13.0 2.662402e+06 -67.0 Healthcare 591 CANG 35 -37.77 penny-cap Short NaN -65.0 2.455229e+06 -50.0 Communication Services 779 ATRI 18 -6.99 penny-cap Short NaN -18.0 2.358541e+06 -22.0 Healthcare 786 QNST 14 -9.61 penny-cap Short NaN -14.0 2.116487e+06 -45.0 Communication Services 781 RMAX 24 -1.93 penny-cap Short NaN -46.0 2.019080e+06 -29.0 Real Estate 785 FDMT 12 -23.18 penny-cap Short NaN -12.0 1.915068e+06 -46.0 Healthcare 777 LXRX 18 -14.75 penny-cap Short NaN -68.0 1.658361e+06 -43.0 Healthcare 693 SYX 9 1.00 penny-cap Short NaN -16.0 1.219073e+06 -13.0 Industrials 820 IH 6 5.94 penny-cap Short NaN -6.0 4.002200e+05 -139.0 Consumer Defensive 701 HLG 43 -22.42 penny-cap Short NaN -43.0 1.855838e+05 -139.0 Consumer Defensive 609 GB 12 -10.80 penny-cap Short NaN -30.0 1.550495e+05 -68.0 Technology Mean Return: -7.368888888888889 Mean Day/Week: 12.372549019607844 Accuracy:0.7450980392156863 ************************************** ************************************** ************************************** Error: []
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https://ift.tt/2YW9oY0
En ces temps troublés – et malgré un retard entièrement imputable à ma personne (avec l'aide de Borderlands 3) – il est bon de ne pas oublier les fondamentaux, comme le bilan mensuel création et mécénat. Voici donc l'édition de février 2020, où je parle de tout ce que j'ai créé, donné et reçu le mois passé.
Création
D'abord, la création, à commencer par les 21 articles publiés sur ce présent blog. Comme souvent, une bonne moitié est consacrée aux chroniques musicales: neuf chroniques d'albums, un live-report et le making-of d'un épisode de Radio-Erdorin.
Dans les autres chroniques de ce mois-ci, quatre livres et une série télé et un jeu vidéo. S'y ajoutent deux compte-rendus de convention et un gros article bis-annuel sur la création et le mécénat, plus l'habituel bilan création et mécénat du mois précédent. Tous ces articles sont aussi partagés sur le blog de secours et je publie aussi sur Instagram les liens vers les chroniques musicales.
Sur le plan des photos, j'ai publié deux albums, pour un total de 77 images. Ces photos sont publiées en basse-résolution sur Facebook et en haute-résolution sur Flickr (sauf que là, ben y'en a pas). En vidéo, un épisode de Radio-Erdorin a été mis en ligne sur YouTube et sur Peertube. Rien de nouveau en fiction pour le moment.
Mécénat
Ensuite, le mécénat, en commençant par Flattr. Comme d'habitude, zéro dons reçus et $6 donnés, répartis en 18 flattrs:
Wikipédia (divers articles, 12 flattrs),
Le blog de Ploum (article "Se passer d'écran avec un téléphone e-ink")
The Internet Archive
Cory Doctorow (sur Twitter)
Angry Metal Guy,
Korben et
The Document Foundation (LibreOffice).
Liberapay est retombé en février aux trois abonnés habituels et mes revenus sont redescendus à €0.51 par semaine, pour un total mensuel de €2.04.
Mes dons se montent toujours à €9 par mois:
Les projets et services Liberapay, Mastodon et Pair2Jeux
Le développeur lopo (Duplicate Post)
Les auteurs et autrices Cestdoncvrai, Jeanne, Ploum et Pouhiou
Le blog Neoprog
Sur uTip, j'ai reçu €2.09, dont €1.09 en pubs et €1 en dons récurrents. Je donne €3.29 par mois en "FireTipR", qui vont à Cestdoncvrai, à Neil Jomunsi et au Greg (€1 chacun, le solde en frais divers).
Calme plat sur MyTip et sur Ko-Fi.
L'activité des plateformes de mécénat, Patreon et Tipeee, reste globalement stable, avec un nouvel artiste suivi sur Tipeee. Ce qui nous fait:
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Bimatoprost CAS#: 155206-00-1
IdentificationPhysical DataSpectraRoute of Synthesis (ROS)Safety and HazardsOther Data
Identification
Product NameBimatoprostIUPAC Name(Z)-7-cyclopentyl]-N-ethylhept-5-enamideMolecular Structure
CAS Registry Number 155206-00-1Synonymsbimatoprost, lumigan; (Z)-7-cyclopentenyl]-5-N-ethylheptenamide, Bimatoprost, cyclopentane N-ethyl-heptenamide-5-cis-2-(3α-hydroxy-5-phenyl-1-trans-pentenyl)-3,5-dihydroxy, , (5Z)-7-cyclopentyl]-N-ethylhept-5-enamide, (5Z)-N-ethyl-7--cyclopentyl]hept-5-enamide, (Z)-7-((1R,2R,3R,5S)-3,5-dihydroxy-2-((3S,E)-3-hydroxy-5-phenylpent-1-en-1-yl)cyclopentyl)-N-ethylhept-5-enamideMolecular FormulaC25H37NO4Molecular Weight415.573InChIInChI=1S/C25H37NO4/c1-2-26-25(30)13-9-4-3-8-12-21-22(24(29)18-23(21)28)17-16-20(27)15-14-19-10-6-5-7-11-19/h3,5-8,10-11,16-17,20-24,27-29H,2,4,9,12-15,18H2,1H3,(H,26,30)/b8-3-,17-16+/t20-,21+,22+,23-,24+/m0/s1 InChI KeyAQOKCDNYWBIDND-FTOWTWDKSA-NCanonical SMILESCCNC(=O)CCCC=CCC1C(CC(C1C=CC(CCC2=CC=CC=C2)O)O)OIsomeric SMILESCCNC(=O)CCC/C=CC1(C(1/C=C/(CCC2=CC=CC=C2)O)O)O
Patent InformationPatent IDTitlePublication DateKR2017/25682Novel method for preparing Prostaglandin derivatives2017US2015/51410PROCESSES AND INTERMEDIATES FOR THE PREPARATIONS OF ISOMER FREE PROSTAGLANDINS2015US2015/158837Compound And Method2015US2015/31898PROCESS FOR PREPARATION OF PROSTAGLANDIN F2 ALPHA ANALOGUES2015US2014/135503NOVEL PROCESSES FOR THE PREPARATION OF PROSTAGLANDIN AMIDES2014WO2013/133730PROCESS FOR PREPARATION OF PROSTAGLANDIN F2α ANALOGUES2013WO2012/11128PREPARATION OF PROSTAGLANDIN DERIVATIVES2012WO2012/112451ESTER DERIVATIVES OF BIMATOPROST COMPOSITIONS AND METHODS2012WO2011/46569PROCESS FOR THE PREPARATION OF F-SERIES PROSTAGLANDINS2011WO2011/55377A NOVEL PROCESS FOR THE PREPARATION OF PROSTAGLANDINS AND INTERMEDIATES THEREOF 2011US2007/286890Eyelash applicator and method2007US2005/69507Method for imparting artificial tan to human skin2005US2005/58614Methods for the treatment of gray hair using cyclopentane(ene) heptan(en)oic acid amides 2005
Physical Data
AppearanceWhite powderWater SolubilitySlightly soluble(1.87e-02 g/L)
Melting Point, °C Solvent (Melting Point) 65.7 - 72.7ethyl acetate, tert-butyl methyl ether71.9 - 72.5water7875.9diethyl ether77.2methanol72.9water, ethanol62.1acetonitrile
log POWTemperature (Partition octan-1-ol/water (MCS)), °CpH3.4122.425~ 7.4
Spectra
Description (NMR Spectroscopy)Nucleus (NMR Spectroscopy)Solvents (NMR Spectroscopy)Frequency (NMR Spectroscopy), MHzOriginal Text (NMR Spectroscopy)Chemical shifts1Hchloroform-d1 4001H NMR (400 MHz; CDCl3) δH=1.09 (t, J=7.1 Hz, 3H, CH3), 1.42-2.40 (m, 14H, 6×CH2, 2×CH), 2.67 (m, 2H, CH2), 3.22 (dq, J=7.1, 6.3 Hz, 2H, CH2NH), 3.41 (broad s, 3H, 3×OH), 3.80-4.30 (broad m, 3H, 3×CHOH), 5.37 (m, 2H, 2×═CH), 5.47 (dd, J=15.2, 7.9 Hz, 1H, ═CH), 5.59 (dd, J=15.2, 7.9 Hz, 1H, ═CH), 5.90 (broad s, 1H, NH), 7.17 (m, 3H, ArCH's), 7.26 (m, 2H, ArCH's) Chemical shifts 1Hchloroform-d16001H NMR (CDC13, 600 MHz, 25° C.) ö (ppm): 1.10 (t, J=7.2 Hz, 3H, —-CH2CH3), 1.46 (m, 1H, CH-i of cyclopentyl ring),1.62 (m, 1 H, one proton of CH2-3 group of a chain), 1.68 (m,H, one proton of CH2-3 group of a chain), 1.74 (m, 1 H, one proton of CH2-4 group of cyclopentyl ring), 1.78 (m, 1H, one proton of CH2-4 group of ca chain), 1.90 (m, 1H, one proton of CH2-4 group of w chain), 2.02-2.06(m, 2H, one proton of CH2-4 group and one of CH2-7 group of a chain), 2.11-2.15 (m, 3H, CH2-2 of a chain and one proton of CH2-4 group ofchain), 2.21 (m, 1H, one proton of CH2-4 group of cyclopentyl ring), 2.29 (m, 1H, one proton of CH2-7 group of a chain), 2.34 (m, 1H, CH-2cyclopentyl ring), 2.67 (m, 2H, CH2-S of w chain), 3.22 (m, 2H, -CCH3), 3.55 (s, 3H, three —-OH groups), 3.91 (m, 1H, CH-3 of cyclopentyl group), 4.08 (m, 1H, CH-3 of w chain), 4.12 (m, 1H, CH-S of cyclopentyl ring), 5.34 (m, 1H, CH-S of a chain), 5.41 (m, 1H, CH-6 of a chain), 5.47 (dd, J=9.0 and 15.3 Hz, 1H, CH-i ofw chain), S.S9 (dd, J=7.3 Hz and 1S.3 Hz, 1H, CH-2 ofw chain), S.98 (t, J=S.i Hz, 1H, >NH), 7.17 (m, 1H, H-4 aromatic), 7.18 (m, 2H, H-2 andH-6 aromatic), 7.26 (m, 2H, H-3 and H-S aromatic). Chemical shifts13Cchloroform-d115013C NMR (150 MHz, CDC13, 2S° C.) ö(ppm): 14.77 (——CH2CH3), 2S.38 (C-7 of a chain), 2S.63 (C-3 of a chain), 26.70 (C-4 of a chain), 31.88 (C-S of w chain), 34.40 (-CH2CH3), 3S.82 (C-2 of a chain), 38.7S(C-4 of w chain), 42.93 (C-4 of cyclopentyl ring), 50.19 (C-i of cyclopentyl ring), 55.47 (C-2 of cyclopentyl ring), 72.25 (C-3 of w chain), 72.33 (C-5 of cyclopentyl ring), 77.67 (C-3 of cyclopentyl ring), i25.77 (C-4 aromatic), i28.35 (2C, C-3 andC-5 aromatic), i28.35 (2C, C-2 andC-6 aromatic), 142.0 (C-i aromatic), i29.i8 (C-6 of a chain), i29.66 (C-5 of a chain), 133.20 (C-i of w chain), 135.12 (C-2 of w chain), 173.42 (CrrrO). Chemical shifts 13Cchloroform-d110013C NMR (100 MHz; CDCl3) δC=14.8 (CH3), 25.4 (CH2), 25.6 (CH2), 26.7 (CH2), 31.9 (CH2), 34.4 (CH2NH), 35.8 (CH2C═O), 38.8 (CH2), 42.9 (CH2), 50.2 (CH), 55.5 (CH), 72.3 (CHOH), 72.4 (CHOH), 77.7 (CHOH), 125.8 (ArCH), 128.4 (2×ArCH), 128.5 (2×ArCH), 129.1 (═CH), 129.7 (═CH), 133.7 (═CH), 135.1 (═CH), 142.0 (ArC), 173.4 (C═O)
Description (IR Spectroscopy)Solvent (IR Spectroscopy)Original Text (IR Spectroscopy)Bandspotassium bromideFT-IR (KBr) Vmax (cm’): 3420, 3327,3084, 3011,2914, 2865, 2933, 1620,1546,1496,1456,1372, 1317,1290,1249, 1151, 1097,1055,1027,976,920,698. in KBrIR: 3415.03cm-1, 3326.69cm-1, 3085.84cm-1, 2914.06cm-1, 2865.73cm-1, 1619.61cm-1, 1546.22cm-1, 1496.54cm-1, 1455.38cm-1, 1372.19cm-1, 1346.23cm-1, 1317.02cm-1, 1290.25cm-1, 1248.99cm-1, 1151.57cm-1, 1097.45cm-1, 1054.67cm-1, 1027.58cm-1, 975.78cm-1, 920.47cm-1, 859.10cm-1, 698.34cm-1 and 596.08cm-1 paraffin oil (nujol)IR (Nujol): 3418.5, 3328.2, 3085.2, 3062.4, 2953.1, 2925.4, 2854.7, 1619.6, 1545.3, 1496.3, 1456.5, 1376.5, 1346.2, 1316.5, 1290.0, 1261.0, 1248.7, 1229.1, 1203.3, 1151.1, 1122.6, 1097.5, 1054.6, 1027.1, 975.9, 961.0, 920.3, 768.1, 721.8, 697.8, 595.7 and 545.4 cm-1
Bimatoprost CAS#: 155206-00-1 IR
Chromatographic dataOriginal stringTLC (Thin layer chromatography)R&f%On a silica TLC (MTBE:EtOH 9:1 ): 0.35LC (Liquid chromatography)Rt=22.56 mm.HPLC (High performance liquid chromatography)HPLC-MS (ESI): Kinetex XB-C 18, 2.7 μηι, kolumna 150 x 4.6 mm, (600 μ ΝΗ&3%· H&2%0 : 500 μ CH&3%COOH : 1 dm%3& H&2%0) : CH&3%CN (8:2, phase A)/ (600 μ ΝΗ&3% · H&2%0 : 500 μ CH&3%COOH : 1 dm%3& H&2%0) : CH&3%CN (8: 1, phase B) in concentration gradient 100percent - 75percent, 1.0 ml/min, R&t% = 21.79 min. (m/z = 416.3 %+& for (15i?)-(+)-7b), R&t% = 22.33 min. (m/z = 416.3 %+& for (5E, 15S)-(+)-7a), R&t% = 22.91 min. (m/z = 416.3 %+& dla (155)- (+)-10a).
Type (Optical Rotatory Power)Concentration (Optical Rotatory Power)Enantiomeric excess, %eeSolvent (Optical Rotatory Power)Optical Rotatory Power, degWavelength (Optical Rotatory Power), nmTemperature (Optical Rotatory Power), °C1 g/100mlmethanol32.5589221 g/100mlchloroform39.07589200.35 g/100mldichloromethane41.1589221.0 weight percent98.72dichloromethane39.0758920
Route of Synthesis (ROS)
Route of Synthesis (ROS) of Bimatoprost CAS 155206-00-1
ConditionsYieldStage #1: (3aR,4R,5R,6aS)-hexahydro-4--2H-cyclopentafuran-2,5-diol With potassium tert-butylate In tetrahydrofuran at 0℃; for 0.666667h;
Stage #2: (5-(ethylamino)-5-oxopentyl)triphenylphosphonium bromide In tetrahydrofuran at 0℃;65%Stage #1: (5-(ethylamino)-5-oxopentyl)triphenylphosphonium bromide With potassium tert-butylate In tetrahydrofuran at 0℃ for 0.666667h Inert atmosphere
Stage #2: (3aR,4R,5R,6aS)-hexahydro-4--2H-cyclopentafuran-2,5-diol In tetrahydrofuran at 0 – 20℃ for 1h Inert atmosphere 41%Stage #1: (5-(ethylamino)-5-oxopentyl)triphenylphosphonium bromide With potassium tert-butylate In tetrahydrofuran at 0℃ for 1h
Stage #2: (3aR,4R,5R,6aS)-hexahydro-4--2H-cyclopentafuran-2,5-diol In tetrahydrofuran at -17℃ for 20.5h Wittig Reaction Stage #1: (5-(ethylamino)-5-oxopentyl)triphenylphosphonium bromide With potassium tert-butylate In tetrahydrofuran at 0℃; for 0.666667h; Wittig Olefination; Schlenk technique; Inert atmosphere;
Stage #2: (3aR,4R,5R,6aS)-hexahydro-4--2H-cyclopentafuran-2,5-diol In tetrahydrofuran at 20℃; for 1h; Wittig Olefination; Schlenk technique; Inert atmosphere;82.6mgStage #1: (5-(ethylamino)-5-oxopentyl)triphenylphosphonium bromide With potassium tert-butylate In tetrahydrofuran at 0℃; for 0.666667h; Inert atmosphere;
Stage #2: (3aR,4R,5R,6aS)-hexahydro-4--2H-cyclopentafuran-2,5-diol In tetrahydrofuran at 0 - 20℃; for 2h; Inert atmosphere;108 mg
Safety and Hazards
Pictogram(s)
SignalDangerGHS Hazard StatementsH302: Harmful if swallowed
H312: Harmful in contact with skin
H319: Causes serious eye irritation
H340: May cause genetic defects
H360: May damage fertility or the unborn child
H361: Suspected of damaging fertility or the unborn child
Information may vary between notifications depending on impurities, additives, and other factors. Precautionary Statement CodesP201, P202, P264, P270, P280, P281, P301+P312, P302+P352, P305+P351+P338, P308+P313, P312, P322, P330, P337+P313, P363, P405, and P501
(The corresponding statement to each P-code can be found at the GHS Classification page.)
Other Data
TransportationNot dangerous goodsUnder 2-8℃ for shipmentHS Code293750StorageUnder -20℃ for long time storageShelf Life1 yearMarket PriceUSD 450/g
Use PatternBimatoprost CAS#: 155206-00-1 can be used in pharmaceuticalsBimatoprost CAS#: 155206-00-1 can be used for treating hair loss of the eyebrowsBimatoprost CAS#: 155206-00-1 is used for treating hypotrichosis associated with chemotherapy treatment regimensBimatoprost CAS#: 155206-00-1 is used for treating hypotrichosis of the eyelashestreatment of dry-eye and related symptomstreating or preventing skin diseases or disorderstreating or preventing vitreous adhesions
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New Post has been published on https://fitnesshealthyoga.com/prenatal-and-infant-exposure-to-ambient-pesticides-and-autism-spectrum-disorder-in-children-population-based-case-control-study/
Prenatal and infant exposure to ambient pesticides and autism spectrum disorder in children: population based case-control study
Abstract
Objective To examine associations between early developmental exposure to ambient pesticides and autism spectrum disorder.
Design Population based case-control study.
Setting California’s main agricultural region, Central Valley, using 1998-2010 birth data from the Office of Vital Statistics.
Population 2961 individuals with a diagnosis of autism spectrum disorder based on the Diagnostic and Statistical Manual of Mental Disorders, fourth edition, revised (up to 31 December 2013), including 445 with intellectual disability comorbidity, were identified through records maintained at the California Department of Developmental Services and linked to their birth records. Controls derived from birth records were matched to cases 10:1 by sex and birth year.
Exposure Data from California state mandated Pesticide Use Reporting were integrated into a geographic information system tool to estimate prenatal and infant exposures to pesticides (measured as pounds of pesticides applied per acre/month within 2000 m from the maternal residence). 11 high use pesticides were selected for examination a priori according to previous evidence of neurodevelopmental toxicity in vivo or in vitro (exposure defined as ever v never for each pesticide during specific developmental periods).
Main outcome measure Odds ratios and 95% confidence intervals using multivariable logistic regression were used to assess associations between pesticide exposure and autism spectrum disorder (with or without intellectual disabilities) in offspring, adjusting for confounders.
Results Risk of autism spectrum disorder was associated with prenatal exposure to glyphosate (odds ratio 1.16, 95% confidence interval 1.06 to 1.27), chlorpyrifos (1.13, 1.05 to 1.23), diazinon (1.11, 1.01 to 1.21), malathion (1.11, 1.01 to 1.22), avermectin (1.12, 1.04 to 1.22), and permethrin (1.10, 1.01 to 1.20). For autism spectrum disorder with intellectual disability, estimated odds ratios were higher (by about 30%) for prenatal exposure to glyphosate (1.33, 1.05 to 1.69), chlorpyrifos (1.27, 1.04 to 1.56), diazinon (1.41, 1.15 to 1.73), permethrin (1.46, 1.20 to 1.78), methyl bromide (1.33, 1.07 to 1.64), and myclobutanil (1.32, 1.09 to 1.60); exposure in the first year of life increased the odds for the disorder with comorbid intellectual disability by up to 50% for some pesticide substances.
Conclusion Findings suggest that an offspring’s risk of autism spectrum disorder increases following prenatal exposure to ambient pesticides within 2000 m of their mother’s residence during pregnancy, compared with offspring of women from the same agricultural region without such exposure. Infant exposure could further increase risks for autism spectrum disorder with comorbid intellectual disability.
Introduction
Autism spectrum disorder comprises severe developmental disorders characterized by atypical socialization, and restricted and repetitive behaviors and interests. Genetics have a role,12 with heritability estimates of 38%3 to 83%,4 but more information is needed about environmental factors operating in early development.3 Prenatal exposures to several types of pesticides have been associated with impaired neurodevelopment,5678 and the few studies that have considered autism spectrum disorder have suggested that organophosphates9 and organochlorines1011 could increase risk.
Experimental in vivo and in vitro studies of autism121314 suggested changes in neuroprotein levels, altered gene expression, and neurobehavioral abnormalities after exposure to certain pesticides.1214 For example, when the organophosphate chlorpyrifos was administered prenatally at subtoxic levels to a mouse model that displays several behavioral traits related to the autism spectrum, male offspring showed delayed motor function maturation and enhanced behavioral features associated with autism spectrum disorder.13
So far, knowledge about pesticide exposure in the real world and risk of autism spectrum disorder is scarce. In this large population based study, we assess prenatal and infant exposure to high use pesticides, which have been a priori selected on the basis of previous evidence for their experimental neurodevelopmental toxicity. Use of these pesticides in an agriculturally intensive region of California, United States, were recorded in the California state mandated Pesticide Use Reporting (CA-PUR) program. These records were integrated in our geographic information system tool, which links exposure records to addresses from birth records of the study population.
Methods
Study design and population
Records of autism spectrum disorder cases were retrieved from the registry maintained at the California Department of Developmental Services (DDS), based on diagnostic data collected by contracted regional centers (https://www.dds.ca.gov/RC/RCList.cfm). We included all individuals with a primary diagnosis of autistic disorder (code 299.00) reported on the DDS client development evaluation report, which implements criteria of the Diagnostic and Statistical Manual of Mental Disorders, fourth edition, revised (DSM-IV-R)15 up to 31 December 2013 (“autistic disorder” is the most severe diagnosis of autism spectrum disorder under DSM-IV criteria).16 Validation studies have established the reliability and validity of the DDS client development evaluation report in California.17 Eligibility for DDS services does not depend on citizenship or financial status, and services are available to all children. We used California birth records data from the Office of Vital Statistics to create a statewide case-control sample of 1998-2010 births. We matched DDS case records to birth records using a probabilistic linkage18 based on child and parental identifiers including first and last name, birth date, and sex. We estimated the probability that two records were for the same person by assigning total linkage scores generated for matches with the National Program of Cancer Registries Link Plus software (linkage rate 86.3%).19 We manually checked cases with borderline scores; the main reason for non-linkage was missing information on birth or DDS records.
Randomly selected controls from birth records were matched to each case 10:1 by birth year and sex. From the statewide sample (n=33 921 cases, n=339 210 controls), we excluded 3401 (10%) case records and 42 519 (12.5%) control records with missing, implausible, or non-viable gestational ages (included range 147-322 days) or birth weights (included range 500-6800 g), and non-singleton births. We also excluded 1296 (0.4%) controls who died before age 6 (identified by linkage to the California death registry).18 We restricted our sample to the eight major agricultural counties (San Joaquin, Stanislaus, Merced, Madera, Fresno, Kings, Tulare, and Kern); 38 331 participants (2961 cases and 35 370 controls) resided here at the time of birth and diagnosis. Although the CA-PUR covers the state of California, the mandatory reporting reflects agricultural use pesticides (see supplemental eMethods), which has a different spatial resolution from other pesticide use recorded in the Pesticide Use Reporting system. In urban areas (such as on structures and right of way applications or near roadway applications), non-agricultural pesticide use is most common but this is only reportable to the Pesticide Use Reporting at the county level (low spatial resolution); thus variables that estimate pesticide exposure for urban areas would be expected to result in markedly higher exposure misclassification.
We distinguished cases according to comorbid intellectual disability (in our study period recorded as “mental retardation” and diagnosed according to DSM-IV criteria corresponding to ICD-9 (international classification of diseases, 9th revision)). Information on pregnancy characteristics including gestational age, birth weight, pregnancy complications, and sociodemographics (maternal/paternal age, race/ethnicity, education) was retrieved from birth records.
Pesticide exposure
Residential birth addresses, as listed on birth certificates, were geocoded by our open source geocoder (historical address information was not available).20 CA-PUR21 includes information on all agricultural pesticide applications with the date, location, and amount of active ingredient applied (see supplemental eMethods). CA-PUR reports were combined with land use survey information from the California Department of Water Resources, which provides the location of specific crops, in a geographic information system-based computer model to estimate pesticide exposure from agricultural applications (technical details published elsewhere22). Briefly, for each pesticide, we summed pounds applied per acre (1 acre=4046.9 m2) per month within a 2000 m radius of each residential address. Our geographic information system tool generated calendar month averages, which we then used to generate developmental period-specific averages (for the three months before gestation, each month of gestation/gestation, and the first year of life) using weights according to the developmental period/gestational days covered by a calendar month. For sensitivity analyses, we also used a 2500 m radius in the same manner. The length of the gestational period for controls was truncated to the length of the matched cases to ensure comparable exposure periods. We defined exposure as any versus none to a specific substance during a specific developmental period; we chose this method to avoid making assumptions about the relative toxicity of agents, shape of the association, or the exposure potential due to presence at the time of application. It is, however, possible that this approach generates non-differential exposure error and underestimates effects.
We a priori decided to select from among 25 most used pesticide substances with peer reviewed published reports of neurodevelopmental interference, leaving 11 pesticides for analysis (classifications shown in eTable 1). These substances included glyphosate,23242526 chlorpyrifos,927 diazinon,282930 acephate,313233 malathion,333435 permethrin,69 bifenthrin,93336 methyl bromide,3738 imidacloprid,3940 avermectin,4142 and myclobutanil.1443
Statistical analysis
Tetrachoric/Spearman correlations (binary/continuous) of pesticide exposures were examined within and between developmental periods. Pesticide use over time was plotted; maps were drawn using ArcGIS 10.4 (ESRI). Odds ratios and 95% confidence intervals were estimated for associations between developmental period-specific pesticide exposures and autism spectrum disorder with unconditional logistic regression. We adjusted all models for the matching variables sex and year of birth, and selected potential confounders on the basis of previous knowledge.1044 These potential confounders included maternal age, indicators of socioeconomic status (that is, maternal race/ethnicity and education), and nitrogen oxides44 (NOx; pregnancy average) as a marker of traffic related air pollution. For air pollution assessment, we used the California Line Source (CALINE4) emissions model, a modified Gaussian dispersion model of local gasoline and diesel vehicles emissions estimated for 1500 m distance from the residential address based on traffic volume, roadway geometry, vehicle emission rates, and meteorological conditions (wind speed/direction, temperature, atmospheric stability, and mixing heights).454647
While we estimated parameters for each pesticide in separate models because of collinearities, we also explored multi-pesticide models for two or three selected pesticides for substances that showed associations with autism spectrum disorder in single pesticide models and belonged to different chemical classes. For those pesticides with more than one substance per class (organophosphates, pyrethroids), we selected a representative chemical (eg, chlorpyrifos for organophosphates) based on the strongest previous evidence for neurodevelopmental toxicity.48 To further adjust for coexposure, we adjusted for 11 pesticides in logistic models; in sensitivity analyses, a semi-Bayesian approach was used as described elsewhere.49 There was little difference in effect estimates between the fully adjusted conventional logistic and the hierarchical modeling approach, so we present the logistic modeling results only.495051
We also stratified analyses by autism spectrum disorder with or without comorbid intellectual disability to assess risk in more severely impaired individuals separately. We conducted sensitivity analyses adjusting for additional variables including maternal birth place (US v non-US); residence in urban or rural areas52; socioeconomic status categories based on census data related to income, education, and occupation53; source of payment for delivery (indicator of socioeconomic status); and preterm birth. None of these variables changed the estimates of interest by more than 5%, thus they were not retained in final models.54 Sensitivity analyses also included restricting to term births, and stratifying by sex. Analyses were conducted with SAS 9.3.
Patient and public involvement
No patients were directly involved in setting the research question or the outcome measures, nor were they involved in developing plans for design or implementation of the study. However, the study responds to concerns by the families of patients with autism that environmental toxic exposures in early life are suspected to contribute to risks for autism spectrum disorder. There are plans to disseminate the results of the research to the relevant patient community. Affected families are thanked in the acknowledgments.
Results
Baseline characteristics and exposure
In our sample, individuals with autism spectrum disorder were mainly male (>80%), had older mothers, and had mothers who had completed more years of education than control mothers (table 1). Correlations between several pesticides in the same or across developmental periods were moderate to high (rt=0.45-0.85; eTable 2). In figure 1, we present a map of the study area showing pesticide applications for the most used substance glyphosate as an example.
Table 1
Study population characteristics by autism spectrum disorder status and population controls in the Central Valley, CA*
Fig 1
Pesticide application of glyphosate in Central Valley, CA, 1998-2010
Association between autism spectrum disorder and exposure to pesticides, coadjusted for developmental period exposures
For all cases of autism spectrum disorder combined, coadjusted for developmental period-specific exposures (three months before pregnancy, during pregnancy, and during the first year of life), odds ratios were increased for pregnancy exposure to most substances. Associations were strongest for chlorpyrifos (1.15; 95% confidence interval 1.02 to 1.29), diazinon (1.14; 1.02 to 1.28), and avermectin (1.14; 1.03 to 1.26). Related to first year of life exposure, most odds ratios were close to one, and only the odds ratios for bifenthrin, malathion, and glyphosates were slightly raised (table 2). For autism spectrum disorder with intellectual disability comorbidity, coadjustment for the exposures in all three periods resulted in attenuated effect estimates during and before pregnancy, while odds ratios became more pronounced for exposures in the first year of life, particularly for glyphosate (1.60; 1.09 to 2.34), diazinon (1.45; 1.11 to 1.89), malathion (1.29; 1.00 to 1.65), and bifenthrin (1.33; 1.03 to 1.72; table 2). Exposure in the three months before pregnancy (indicating exposure just before or around conception) had weaker associations with autism spectrum disorder than exposure during pregnancy or the first year of life, after exposure period coadjustment (table 2, eTable 3). We saw variation in exposure between developmental periods to each pesticide considered, likely due to annual and seasonal changes in application rates (eg, for permethrin, among the controls, 1.5% were solely exposed in the three months before pregnancy, 4.8% were exposed only during pregnancy, 7.6% were exposed only in the first year of life, and 12.1% were exposed in all three periods; eTable 4). For exposures by trimester, no clear patterns were identified (data not shown).
Table 2
Odds ratios and 95% confidence intervals* for association between pesticide exposure and all cases of autism spectrum disorder (ASD) combined and those with intellectual disability comorbidity, coadjusted for developmental period of pesticide exposure, by pesticide substance
Association between prenatal or infant exposure to pesticides and autism spectrum disorder
For all cases of autism spectrum disorder, considering the pregnancy and infant exposures separately, exposure during pregnancy was associated with about a 10% increase in adjusted odds ratios for glyphosate (1.16; 95% confidence interval 1.06 to 1.27), chlorpyrifos (1.13; 1.05 to 1.23), diazinon (1.11; 1.01 to 1.21), malathion (1.11; 1.01 to 1.22), avermectin (1.12; 1.04 to 1.22), and permethrin (1.10; 1.01 to 1.20). Also adjusting for all 11 pesticides resulted in attenuation of associations. However, odds ratios for glyphosate and avermectin remained elevated for exposure during pregnancy, while odds ratios for the remaining pesticides were close to one, and the odds ratio for imidacloprid fell below one (table 3).
Table 3
Odds ratios and 95% confidence intervals for association between all cases of autism spectrum disorder combined and pesticide exposure during pregnancy and first year of life in logistic regression models, by pesticide substance
Association between prenatal or infant exposure to pesticide and autism spectrum disorder with intellectual disability
Among cases of autism spectrum disorder with intellectual disability, odds ratios had greater increases (by 30-40%) in pregnancy and infancy for glyphosate, chlorpyrifos, diazinon, permethrin, methyl bromide, and myclobutanil when considering the pregnancy and infant periods separately (table 4). Among cases without intellectual disability (about 85% of cases), estimated odds ratios were similar to those reported for the models analyzing all cases of autism spectrum disorder (eTable 5).
Table 4
Odds ratios and 95% confidence intervals for association between autism spectrum disorder with intellectual disability comorbidity and exposure to pesticides during pregnancy and first year of life in logistic regression models
Multi-pesticide models
In multi-pesticide models with two or three pesticides, most odds ratios were above one for all cases of autism spectrum disorder combined even though several confidence intervals widened (table 5). For autism spectrum disorder with intellectual disability and pesticide exposure during the first year of life, estimated associations were pronounced for glyphosate (odds ratio 1.34; 95% confidence interval 1.03 to 1.74) and permethrin (1.31; 1.07 to 1.62); also including chlorpyrifos or myclobutanil changed little in the associations for glyphosate and permethrin, whereas the estimated odds ratios for chlorpyrifos or myclobutanil were null (table 5).
Table 5
Multi-pesticide models of association among all cases of autism spectrum disorder combined and those with intellectual disability comorbidity, and exposure of selected pesticides from different chemical classes during pregnancy and the first year of life*
Sensitivity analyses: buffer size, sex stratification, area type, and term birth restriction
In sensitivity analyses, we examined associations between autism spectrum disorder and pesticide exposure within a 2500 m distance from home; findings were similar or slightly stronger than those for the 2000 m distance (eTable 6). Stratifying by sex, associations among male individuals were similar as seen for the entire sample, with increased odds ratios for glyphosate, chlorpyrifos, diazinon, permethrin, and avermectin. Among female individuals, the findings were similar but the 95% confidence intervals were wider due to the smaller number of cases (eTable 7). Restricting to term births only or adjusting for area type (urban, rural) did not change our findings appreciably (data not shown).
Discussion
To our knowledge, this study is the largest to investigate pesticide exposure and autism spectrum disorder so far, and the first to also consider the disorder with intellectual disability comorbidity. Our results indicate small to moderately increased risks for the disorder in offspring with prenatal exposure to the organophosphates chlorpyrifos, diazinon, and malathion, the pyrethroids permethrin and bifenthrin, as well as to glyphosate, avermectin, and methyl bromide compared with offspring of women without such exposure within 2000 m of their residence. For autism spectrum disorder with comorbid intellectual disability, risks were more pronounced for exposures during the first year of life. Importantly, the pesticides considered for analysis were selected a priori on the basis of experimental evidence indicating neurodevelopmental toxicity. Thus, our findings support the hypotheses that prenatal and infant pesticide exposures to these substances increase the risks for autism spectrum disorder, and exposures in infancy could contribute to risks for more severely impaired phenotypes with comorbid intellectual disability.
Comparison with other studies
Environmental toxicants have been suspected to increase the risk of autism spectrum disorder, with available research suggesting associations between air pollution and the disorder.44555657 Studies examining pesticides and the disorder are rare. In a California study of DDS case records (n=465) linked to birth records from 1996-98, researchers assigned exposures during pregnancy using CA-PUR, similar to our approach; findings suggested that grouped organochlorines were strongly associated with risks of pregnancy (odds ratio 6.1 (95% confidence interval 2.4 to 15.3)).10 Another study included 486 cases of autism spectrum disorder and assigned pounds per active ingredient in aggregated chemical classes (organophosphates, organochlorines, pyrethroids, carbamates), also derived from CA-PUR data for applications within 1250-1750 m from the home address9; findings suggested a 60% increased risk for the disorder related to organophosphate exposures during pregnancy. Children of mothers living near agricultural pyrethroid applications just before conception or during their third trimester also were at greater risk for autism spectrum disorder and general developmental disability (odds ratios ranging from 1.7 to 2.3).9 In a smaller case-control study measuring organochlorines and polychlorinated biphenyls in banked mid-pregnancy serum (from 2000 to 2003), higher concentrations for several compounds in cases than in general population controls were seen.11
We did not consider organochlorines because many have been banned from use in California for decades. In a high risk, mother-child study of 46 cases of autism spectrum disorder, prenatal urinary dimethylthiophosphate was associated with the disorder in girls but not in boys58; in our study, we saw little evidence of a sex difference in effects. Overall, the few earlier studies corroborate our findings for most of the pesticides we examined. While all the 11 pesticides were a priori selected among high use substances, based on prior evidence for neurodevelopmental toxicity, odds ratios were increased for several but not all substances in our analyses. Possible explanations could include different mechanisms related to the development of autism spectrum disorder, bioavailability of the chemical (eg, in homes resulting from ambient applications and based on chemical properties), and the application practices in these real world scenarios. Different combinations of substances or mixture exposures might also result in synergistic effects, including those leading to a selective survival of the fetus.59
Although environmental exposure studies considering autism spectrum disorder are rare, organophosphates and pyrethroids have been related to neurodevelopmental and cognitive impairments in children in previous studies.576061 Decrements in IQ scores at age 7 have been associated with prenatal residential proximity to agricultural use of organophosphates and pyrethroids, acephate, chlorpyrifos, and diazinon,5 in line with our findings. Pyrethroid metabolites in maternal urine during pregnancy and in child urine were associated with worse behavioral scores assessed in 6 year old children.62 Thus, human studies corroborate the adverse effect of early developmental exposure to ambient pesticides on child neurodevelopment, consistent with our findings.
Additional evidence is provided by experimental studies. Mice exposed in utero to chlorpyrifos showed postnatal deficits in social behavior and restricted interests while the behavior of the dams (maternal mice) was not affected.63 Prenatal exposure to chlorpyrifos enhanced brain oxidative stress and prostaglandin E2 synthesis in a mouse model of autism.64 Oxidative stress and dysregulated immune responses are implicated in organophosphate related toxicity and pathogenesis of autism spectrum disorder, suggesting a possible mode of action.13 Coexposing mice shortly after birth to cypermethrin (a pyrethroid) and endosulfan altered levels of neuroproteins and resulted in neurobehavioral abnormalities.12 Gene expression of mouse cortical neurons was altered by certain fungicides and resembled transcriptional changes thought to underlie development of autism spectrum disorder.214 Translational research connecting toxicological and animal studies with findings from epidemiological studies is needed to identify the specific modes of action of pesticides relevant for the pathogenesis of autism spectrum disorder.6566676869
Residential proximity to pesticide applications during pregnancy has been shown to be a valid indicator of prenatal exposure.70717273 Pesticides, including organophosphates, have been identified in serum, indoor air, and dust in homes in agricultural areas in California.7475 Elevated levels in five of seven pesticides applied within 1250 m of homes according to Pesticide Use Reporting records were also measured in dust from such homes.76 Our exposure assessment method using the geographic information system tool has been validated against serum concentrations of organochlorines,77 and specific methylation patterns found among those with organophosphate exposure,78 and can be considered a valid proxy for prenatal exposures.
Strengths and limitations of our study
A strength of our study was our pesticide exposure assessment tool; it can estimate exposures for multiple substances with short half-lives for which frequent measurements of metabolites would be necessary but not feasible in a population based study of the size needed to investigate the risk of autism spectrum disorder. California’s mandatory Pesticide Use Reporting program is recognized as the most detailed and comprehensive worldwide. Thus, we were able to rely on agricultural application records of specific pesticides with high spatial and temporal resolution, which we believe is a strength that could have reduced exposure misclassification, because we relied on Pesticide Use Reporting information based on the date of application using a relatively fine spatial scale (a buffer of 2000 m) around the residential address. We also relied on the gestational age and birth date to construct individual exposure estimates corresponding to different developmental periods. We still have to assume that individuals were present at their residences around the application dates and that these applications resulted in exposures in the targeted periods only and did not get trapped in or around homes over extended periods of time. Our registry based design avoided participation bias due to self selection and recall bias of parents (which is an issue in case-control studies that rely on self reports of past exposures).
Although our ability to pinpoint one or more specific substances was limited by the collinearity of pesticide exposure owing to agricultural practices, we could capture the real life scenario of populations living in agricultural areas; typically, a variety of substances are used over several weeks or months. Sensitivity analyses using the 2500 m radius buffer further corroborated and even strengthened our results. Simultaneous exposures to frequently used pesticides are likely in residences near agricultural applications, and some of our findings could reflect adverse effects of typical exposure mixtures or coexposures. Multi-pesticide models coadjusted for all pesticides or for two or three substances were generally consistent with our single pesticide models. We present results from real world exposure scenarios while being cognizant of issues of collinearity, sparse data, or overly restrictive modeling assumptions.
A limitation was that we only had birth addresses available and that 9-30% of families could have moved during pregnancy.79 However, most moves in pregnancy have been found to be local (<10 km), and misclassification would be expected to be non-differential because moving residence would happen before diagnosis; thus any bias would likely be toward the null. We also lacked exposure information on pesticides from other sources such as diet or occupation, potentially resulting in underestimation of total exposure if these were associated with residential exposures (eg, women who work and live on farms); however, this would have been similar for cases and controls and most likely to have resulted in attenuation of risk estimates toward the null.54 We also lacked information about passive and active smoking. However, pregnancy smoking rates are very low in California (<2%),80 and smoking in public places has been banned since the 1990s. Even though we had detailed information on potential confounders, and sensitivity analyses did not change our findings, uncontrolled residual confounding always remains a concern.
Conclusions
Our findings suggest that risk of autism spectrum disorder increases with prenatal and infant exposure to several common ambient pesticides that have been shown to affect neurodevelopment in experimental studies. Further research should be translational and integrate experimental and epidemiological approaches to further elucidate underlying mechanisms in the development of the disorder. However, from a public health and preventive medicine perspective, our findings support the need to avoid prenatal and infant exposure to pesticides to protect early brain development.
What is already known on this topic
Common pesticides have been previously shown to cause neurodevelopmental impairment in experimental research
Environmental exposures during early brain development are suspected to increase risk of autism spectrum disorders in children
What this study adds
Prenatal or infant exposure to a priori selected pesticides—including glyphosate, chlorpyrifos, diazinon, and permethrin—were associated with increased odds of developing autism spectrum disorder
Exposure of pregnant women and infants to ambient pesticides with a potential neurodevelopmental toxicity mode of action should be avoided as a preventive measure against autism spectrum disorder
This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.
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NO NOT MRS. CARDENAS!!!
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TV Show Questions
rules: pick 5 shows and answer the following questions (don’t cheat) & tag people
All Rise
Marvel’s Daredevil
New Amsterdam
Prodigal Son
How to Get Away with Murder
Who is your favorite character in 2?
Benjamin “Dex” Poindexter/Bullseye
Who’s your least favorite character in 1?
What’s your favorite episode of 4?
Silent Night (1.10)
What’s your favorite season of 5?
Season 3
Who is your favorite couple in 3?
Not a couple, but Helen Sharpe & Max Goodwin I love their friendship
Who is your favorite couple in 2?
Ben Poindexter and Ray Nadeem but in all seriousness probably Matt/ Elektra
What is your favorite episode of 1?
I can’t just pick one. I have two or three but I’ll pick two.
How to Succeed in Law Without Really Re-Trying (1.09)
Bye Bye Bernie (1.14)
What is your favorite episode of 5?
Wes (3.15)
What is your favorite season of 2?
Season 2, loved Frank Castle/The Punisher storyline. 3 is a close second
How long have you watched 1?
Since it’s pilot back in September
How did you become interested in 3?
I’d followed Ryan Eggold(Max Goodwin) from another show I watch called the Blacklist. I loved him as Tom Keen on that show, and when his character died and I heard Ryan would be on a new show, I gave New Amsterdam a try and I got hooked.
Who is your favorite actor in 4?
Micheal Sheen, he’s just amazing and I love his portrayal of Martin Whitly he plays the character so well and I just enjoy it every time he is on screen.
Which do you prefer 1, 2, or 5?
2, hands down I miss Daredevil so much don’t get me wrong I love HTGWM and All Rise, but I’d give anything for season 4 of DD
Which shows have you seen more episodes of 1 or 3?
New Amsterdam, as it has had a season already, and is on season 2
If you could be anyone from 4, who would you be?
I’d probably love to be Martin Whitly, cause he gets to sit in a nice cell all day. Or Jessica she seems so strong, despite everything she’s been through and I’d love to live in her big house.
Would a crossover between 3 and 4 work?
I think so, maybe Dr. Whity consults on a case for one of the doctors at New Amsterdam or Gil and the team helps the doctors solve a murder.
Pair two characters in 1 who would make an unlikely but okay couple
Mark Callan and Amy Quinn, I mean is it really unlikely? I love them
Overall, which has a better storyline: 3 or 5?
Probably 5, but I’ll say
HTGWM is good and I really enjoy it but I find a lot of stuff is recycled or it’s always the same storyline or they drag a storyline out more than it needs to be. But NA tackles a lot of real-world issues, and it’s interesting.
Which has better theme music 2 or 4
Daredevil hands down! Plus PS doesn’t really have theme music
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Sep 3, 2023. ETH. Short
Nice trade at NY level MSB + rejection
Price took buy-side liquidity and rejected from 1h bearish OB.
Entry after MSB on 5m TF at 18:15.
Limit order placed at NY levels + FVG.
Risk = 0.32, RR = 1.42 (1 / 1.09)
Limit order filled at 18:30 (15m). DD - 1h 10m. Trade duration - 3h. Price hit TP.
View from 1h TF:
#crypto#day trading#market structure#orderblock#smart money concept#smc#daytrading#take profit#trading strategy#tradingstrategy#technical analysis#supply and demand#scalping
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VA - DANGER ZONE 7: Killer Trucks (2017) (DTN 038) [FREE]
Hardcore | Mainstream | Industrial | Gabber
Free download (direct links for MP3 or FLAC):
http://hpg-detonator.com/download/VA_DANGER_ZONE_07_Killer_Trucks_2017_DTN_038_MP3.zip
http://hpg-detonator.com/download/VA_DANGER_ZONE_07_Killer_Trucks_2017_DTN_038_FLAC.zip
Official release page:
http://hpg-detonator.com/music/dtn_038.html
SoundCloud Previews:
http://soundcloud.com/hpg-detonator/sets/dtn-038
DRIVING ZONE 7A:
1.01 - HATEBUSTERS - Begin
1.02 - TERMINAL & VAVACULO - Dangerous Man (RELAPSE Remix)
1.03 - KRIMINAL - Here To Help
1.04 - N-VITRAL & SEI2RE - Noise Pumper (MECCANO TWINS Remix)
1.05 - SAMURAI RESISTANCE - What Up
1.06 - DVBBS - 24K (ROUGHBLAST Hardcore Bootleg)
1.07 - PHOENIX - Red
1.08 - SKISM x HABSTRAKT x MEGALODON - Jaguar (MENTAL CORRUPTED Bootleg)
1.09 - PSYCHOWEAPON - Sick People
1.10 - DD vs. ALAPACA - Hardcore City FuckUshima (FALCHION Remix)
1.11 - BORN TO DIE - Z Day
1.12 - CONSTRUCTION OF NOISE - Last World
1.13 - MENTHALQUAKE - Creation
1.14 - ESOX - Planned Obsolescence
1.15 - ZEOM - The Day Of Death
1.16 - BONE N SKIN - Brain Stuff (MENTAL CORRUPTED Bootleg)
1.17 - R-4IN - The Power Of The Street
TECHNICAL ZONE 7B:
2.01 - BRAINTUNE - We Create It
2.02 - KRIMINAL - Beastial
2.03 - CELLMAC - Nachtmensch
2.04 - SUMMA - Don't You Know Fantasia
2.05 - ENGAGE BLUE - Absolute Revenge
2.06 - DEEP SPHERE - Hatred
2.07 - MOKUSHI - Deep Sea Mysteries
2.08 - BRAINTUNE - Scream Of The Devil
2.09 - T-RAVE - Hellraiser
2.10 - R-4IN - The Origin Of Evil
2.11 - MELTMUTE - Black Market
2.12 - CELLMAC - Edge Of The Limit
2.13 - DEEP SPHERE - Madness v2
2.14 - BRAINTUNE - Run Away
© H.P.G. Detonator Label | DTN 038
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Calcitonin gene-related peptide monoclonal antibodies for migraine prevention: comparisons across randomized controlled studies.
PubMed: Calcitonin gene-related peptide monoclonal antibodies for migraine prevention: comparisons across randomized controlled studies. Curr Opin Neurol. 2017 Feb 24;: Authors: Mitsikostas DD, Reuter U Abstract PURPOSE OF REVIEW: The results of phase 2 randomized controlled trials for the prevention of episodic and chronic migraine demonstrating the efficacy and safety of four mAbs targeting the calcitonin gene-related peptide (CGRP) pathway [ALD403 (eptinezumab), AMG334 (erenumab), LY2951742 (galcanezumab) and TEV48125 (fremanezumab)] have been published recently, and phase 3 trials are in process. This development will change headache management fundamentally. We aim to summarize and compare the phase 2 data. RECENT FINDINGS: The change from baseline in the number of migraine days at the end of treatment in high-frequency episodic migraine was -1 (at weeks 5-8), -1.1 (at weeks 9-12), -1.2 (at weeks 9-12) and -2.6 (at weeks 9-12) days for ALD403, AMG344, LY2951742 and TEV48125 (225 mg), respectively. Number needed to treats for responders and odds ratio for any adverse event were 4.7, 6.2, 4.0 and 4.0 and 1.09, 0.96, 1.07 and 1.05, respectively. SUMMARY: All four CGRP antibodies display comparable efficacy that does not differ significantly from that of the currently available oral antimigraine drugs. However, their safety and tolerability profiles as well as low frequency of administration looks promising but remains to be verified in long-term and large-scale trials. Considerations related to pregnancy, risk for cardiovascular effects and cost are subject for further evaluation. PMID: 28240610 [PubMed - as supplied by publisher] http://dlvr.it/NVfhbS
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Bimatoprost CAS#: 155206-00-1
IdentificationPhysical DataSpectraRoute of Synthesis (ROS)Safety and HazardsOther Data
Identification
Product NameBimatoprostIUPAC Name(Z)-7-cyclopentyl]-N-ethylhept-5-enamideMolecular Structure
CAS Registry Number 155206-00-1Synonymsbimatoprost, lumigan; (Z)-7-cyclopentenyl]-5-N-ethylheptenamide, Bimatoprost, cyclopentane N-ethyl-heptenamide-5-cis-2-(3α-hydroxy-5-phenyl-1-trans-pentenyl)-3,5-dihydroxy, , (5Z)-7-cyclopentyl]-N-ethylhept-5-enamide, (5Z)-N-ethyl-7--cyclopentyl]hept-5-enamide, (Z)-7-((1R,2R,3R,5S)-3,5-dihydroxy-2-((3S,E)-3-hydroxy-5-phenylpent-1-en-1-yl)cyclopentyl)-N-ethylhept-5-enamideMolecular FormulaC25H37NO4Molecular Weight415.573InChIInChI=1S/C25H37NO4/c1-2-26-25(30)13-9-4-3-8-12-21-22(24(29)18-23(21)28)17-16-20(27)15-14-19-10-6-5-7-11-19/h3,5-8,10-11,16-17,20-24,27-29H,2,4,9,12-15,18H2,1H3,(H,26,30)/b8-3-,17-16+/t20-,21+,22+,23-,24+/m0/s1 InChI KeyAQOKCDNYWBIDND-FTOWTWDKSA-NCanonical SMILESCCNC(=O)CCCC=CCC1C(CC(C1C=CC(CCC2=CC=CC=C2)O)O)OIsomeric SMILESCCNC(=O)CCC/C=CC1(C(1/C=C/(CCC2=CC=CC=C2)O)O)O
Patent InformationPatent IDTitlePublication DateKR2017/25682Novel method for preparing Prostaglandin derivatives2017US2015/51410PROCESSES AND INTERMEDIATES FOR THE PREPARATIONS OF ISOMER FREE PROSTAGLANDINS2015US2015/158837Compound And Method2015US2015/31898PROCESS FOR PREPARATION OF PROSTAGLANDIN F2 ALPHA ANALOGUES2015US2014/135503NOVEL PROCESSES FOR THE PREPARATION OF PROSTAGLANDIN AMIDES2014WO2013/133730PROCESS FOR PREPARATION OF PROSTAGLANDIN F2α ANALOGUES2013WO2012/11128PREPARATION OF PROSTAGLANDIN DERIVATIVES2012WO2012/112451ESTER DERIVATIVES OF BIMATOPROST COMPOSITIONS AND METHODS2012WO2011/46569PROCESS FOR THE PREPARATION OF F-SERIES PROSTAGLANDINS2011WO2011/55377A NOVEL PROCESS FOR THE PREPARATION OF PROSTAGLANDINS AND INTERMEDIATES THEREOF 2011US2007/286890Eyelash applicator and method2007US2005/69507Method for imparting artificial tan to human skin2005US2005/58614Methods for the treatment of gray hair using cyclopentane(ene) heptan(en)oic acid amides 2005
Physical Data
AppearanceWhite powderWater SolubilitySlightly soluble(1.87e-02 g/L)
Melting Point, °C Solvent (Melting Point) 65.7 - 72.7ethyl acetate, tert-butyl methyl ether71.9 - 72.5water7875.9diethyl ether77.2methanol72.9water, ethanol62.1acetonitrile
log POWTemperature (Partition octan-1-ol/water (MCS)), °CpH3.4122.425~ 7.4
Spectra
Description (NMR Spectroscopy)Nucleus (NMR Spectroscopy)Solvents (NMR Spectroscopy)Frequency (NMR Spectroscopy), MHzOriginal Text (NMR Spectroscopy)Chemical shifts1Hchloroform-d1 4001H NMR (400 MHz; CDCl3) δH=1.09 (t, J=7.1 Hz, 3H, CH3), 1.42-2.40 (m, 14H, 6×CH2, 2×CH), 2.67 (m, 2H, CH2), 3.22 (dq, J=7.1, 6.3 Hz, 2H, CH2NH), 3.41 (broad s, 3H, 3×OH), 3.80-4.30 (broad m, 3H, 3×CHOH), 5.37 (m, 2H, 2×═CH), 5.47 (dd, J=15.2, 7.9 Hz, 1H, ═CH), 5.59 (dd, J=15.2, 7.9 Hz, 1H, ═CH), 5.90 (broad s, 1H, NH), 7.17 (m, 3H, ArCH's), 7.26 (m, 2H, ArCH's) Chemical shifts 1Hchloroform-d16001H NMR (CDC13, 600 MHz, 25° C.) ö (ppm): 1.10 (t, J=7.2 Hz, 3H, —-CH2CH3), 1.46 (m, 1H, CH-i of cyclopentyl ring),1.62 (m, 1 H, one proton of CH2-3 group of a chain), 1.68 (m,H, one proton of CH2-3 group of a chain), 1.74 (m, 1 H, one proton of CH2-4 group of cyclopentyl ring), 1.78 (m, 1H, one proton of CH2-4 group of ca chain), 1.90 (m, 1H, one proton of CH2-4 group of w chain), 2.02-2.06(m, 2H, one proton of CH2-4 group and one of CH2-7 group of a chain), 2.11-2.15 (m, 3H, CH2-2 of a chain and one proton of CH2-4 group ofchain), 2.21 (m, 1H, one proton of CH2-4 group of cyclopentyl ring), 2.29 (m, 1H, one proton of CH2-7 group of a chain), 2.34 (m, 1H, CH-2cyclopentyl ring), 2.67 (m, 2H, CH2-S of w chain), 3.22 (m, 2H, -CCH3), 3.55 (s, 3H, three —-OH groups), 3.91 (m, 1H, CH-3 of cyclopentyl group), 4.08 (m, 1H, CH-3 of w chain), 4.12 (m, 1H, CH-S of cyclopentyl ring), 5.34 (m, 1H, CH-S of a chain), 5.41 (m, 1H, CH-6 of a chain), 5.47 (dd, J=9.0 and 15.3 Hz, 1H, CH-i ofw chain), S.S9 (dd, J=7.3 Hz and 1S.3 Hz, 1H, CH-2 ofw chain), S.98 (t, J=S.i Hz, 1H, >NH), 7.17 (m, 1H, H-4 aromatic), 7.18 (m, 2H, H-2 andH-6 aromatic), 7.26 (m, 2H, H-3 and H-S aromatic). Chemical shifts13Cchloroform-d115013C NMR (150 MHz, CDC13, 2S° C.) ö(ppm): 14.77 (——CH2CH3), 2S.38 (C-7 of a chain), 2S.63 (C-3 of a chain), 26.70 (C-4 of a chain), 31.88 (C-S of w chain), 34.40 (-CH2CH3), 3S.82 (C-2 of a chain), 38.7S(C-4 of w chain), 42.93 (C-4 of cyclopentyl ring), 50.19 (C-i of cyclopentyl ring), 55.47 (C-2 of cyclopentyl ring), 72.25 (C-3 of w chain), 72.33 (C-5 of cyclopentyl ring), 77.67 (C-3 of cyclopentyl ring), i25.77 (C-4 aromatic), i28.35 (2C, C-3 andC-5 aromatic), i28.35 (2C, C-2 andC-6 aromatic), 142.0 (C-i aromatic), i29.i8 (C-6 of a chain), i29.66 (C-5 of a chain), 133.20 (C-i of w chain), 135.12 (C-2 of w chain), 173.42 (CrrrO). Chemical shifts 13Cchloroform-d110013C NMR (100 MHz; CDCl3) δC=14.8 (CH3), 25.4 (CH2), 25.6 (CH2), 26.7 (CH2), 31.9 (CH2), 34.4 (CH2NH), 35.8 (CH2C═O), 38.8 (CH2), 42.9 (CH2), 50.2 (CH), 55.5 (CH), 72.3 (CHOH), 72.4 (CHOH), 77.7 (CHOH), 125.8 (ArCH), 128.4 (2×ArCH), 128.5 (2×ArCH), 129.1 (═CH), 129.7 (═CH), 133.7 (═CH), 135.1 (═CH), 142.0 (ArC), 173.4 (C═O)
Description (IR Spectroscopy)Solvent (IR Spectroscopy)Original Text (IR Spectroscopy)Bandspotassium bromideFT-IR (KBr) Vmax (cm’): 3420, 3327,3084, 3011,2914, 2865, 2933, 1620,1546,1496,1456,1372, 1317,1290,1249, 1151, 1097,1055,1027,976,920,698. in KBrIR: 3415.03cm-1, 3326.69cm-1, 3085.84cm-1, 2914.06cm-1, 2865.73cm-1, 1619.61cm-1, 1546.22cm-1, 1496.54cm-1, 1455.38cm-1, 1372.19cm-1, 1346.23cm-1, 1317.02cm-1, 1290.25cm-1, 1248.99cm-1, 1151.57cm-1, 1097.45cm-1, 1054.67cm-1, 1027.58cm-1, 975.78cm-1, 920.47cm-1, 859.10cm-1, 698.34cm-1 and 596.08cm-1 paraffin oil (nujol)IR (Nujol): 3418.5, 3328.2, 3085.2, 3062.4, 2953.1, 2925.4, 2854.7, 1619.6, 1545.3, 1496.3, 1456.5, 1376.5, 1346.2, 1316.5, 1290.0, 1261.0, 1248.7, 1229.1, 1203.3, 1151.1, 1122.6, 1097.5, 1054.6, 1027.1, 975.9, 961.0, 920.3, 768.1, 721.8, 697.8, 595.7 and 545.4 cm-1
Bimatoprost CAS#: 155206-00-1 IR
Chromatographic dataOriginal stringTLC (Thin layer chromatography)R&f%On a silica TLC (MTBE:EtOH 9:1 ): 0.35LC (Liquid chromatography)Rt=22.56 mm.HPLC (High performance liquid chromatography)HPLC-MS (ESI): Kinetex XB-C 18, 2.7 μηι, kolumna 150 x 4.6 mm, (600 μ ΝΗ&3%· H&2%0 : 500 μ CH&3%COOH : 1 dm%3& H&2%0) : CH&3%CN (8:2, phase A)/ (600 μ ΝΗ&3% · H&2%0 : 500 μ CH&3%COOH : 1 dm%3& H&2%0) : CH&3%CN (8: 1, phase B) in concentration gradient 100percent - 75percent, 1.0 ml/min, R&t% = 21.79 min. (m/z = 416.3 %+& for (15i?)-(+)-7b), R&t% = 22.33 min. (m/z = 416.3 %+& for (5E, 15S)-(+)-7a), R&t% = 22.91 min. (m/z = 416.3 %+& dla (155)- (+)-10a).
Type (Optical Rotatory Power)Concentration (Optical Rotatory Power)Enantiomeric excess, %eeSolvent (Optical Rotatory Power)Optical Rotatory Power, degWavelength (Optical Rotatory Power), nmTemperature (Optical Rotatory Power), °C1 g/100mlmethanol32.5589221 g/100mlchloroform39.07589200.35 g/100mldichloromethane41.1589221.0 weight percent98.72dichloromethane39.0758920
Route of Synthesis (ROS)
Route of Synthesis (ROS) of Bimatoprost CAS 155206-00-1
ConditionsYieldStage #1: (3aR,4R,5R,6aS)-hexahydro-4--2H-cyclopentafuran-2,5-diol With potassium tert-butylate In tetrahydrofuran at 0℃; for 0.666667h;
Stage #2: (5-(ethylamino)-5-oxopentyl)triphenylphosphonium bromide In tetrahydrofuran at 0℃;65%Stage #1: (5-(ethylamino)-5-oxopentyl)triphenylphosphonium bromide With potassium tert-butylate In tetrahydrofuran at 0℃ for 0.666667h Inert atmosphere
Stage #2: (3aR,4R,5R,6aS)-hexahydro-4--2H-cyclopentafuran-2,5-diol In tetrahydrofuran at 0 – 20℃ for 1h Inert atmosphere 41%Stage #1: (5-(ethylamino)-5-oxopentyl)triphenylphosphonium bromide With potassium tert-butylate In tetrahydrofuran at 0℃ for 1h
Stage #2: (3aR,4R,5R,6aS)-hexahydro-4--2H-cyclopentafuran-2,5-diol In tetrahydrofuran at -17℃ for 20.5h Wittig Reaction Stage #1: (5-(ethylamino)-5-oxopentyl)triphenylphosphonium bromide With potassium tert-butylate In tetrahydrofuran at 0℃; for 0.666667h; Wittig Olefination; Schlenk technique; Inert atmosphere;
Stage #2: (3aR,4R,5R,6aS)-hexahydro-4--2H-cyclopentafuran-2,5-diol In tetrahydrofuran at 20℃; for 1h; Wittig Olefination; Schlenk technique; Inert atmosphere;82.6mgStage #1: (5-(ethylamino)-5-oxopentyl)triphenylphosphonium bromide With potassium tert-butylate In tetrahydrofuran at 0℃; for 0.666667h; Inert atmosphere;
Stage #2: (3aR,4R,5R,6aS)-hexahydro-4--2H-cyclopentafuran-2,5-diol In tetrahydrofuran at 0 - 20℃; for 2h; Inert atmosphere;108 mg
Safety and Hazards
Pictogram(s)
SignalDangerGHS Hazard StatementsH302: Harmful if swallowed
H312: Harmful in contact with skin
H319: Causes serious eye irritation
H340: May cause genetic defects
H360: May damage fertility or the unborn child
H361: Suspected of damaging fertility or the unborn child
Information may vary between notifications depending on impurities, additives, and other factors. Precautionary Statement CodesP201, P202, P264, P270, P280, P281, P301+P312, P302+P352, P305+P351+P338, P308+P313, P312, P322, P330, P337+P313, P363, P405, and P501
(The corresponding statement to each P-code can be found at the GHS Classification page.)
Other Data
TransportationNot dangerous goodsUnder 2-8℃ for shipmentHS Code293750StorageUnder -20℃ for long time storageShelf Life1 yearMarket PriceUSD 450/g
Use PatternBimatoprost CAS#: 155206-00-1 can be used in pharmaceuticalsBimatoprost CAS#: 155206-00-1 can be used for treating hair loss of the eyebrowsBimatoprost CAS#: 155206-00-1 is used for treating hypotrichosis associated with chemotherapy treatment regimensBimatoprost CAS#: 155206-00-1 is used for treating hypotrichosis of the eyelashestreatment of dry-eye and related symptomstreating or preventing skin diseases or disorderstreating or preventing vitreous adhesions
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Dialogue (Part B)
Location: Dunkin Donuts
Mo (DD Worker): Hi welcome to Dunkin Donuts what can I get for you?
Jill (student): Hi um can I have a medium coffee
Mo: Hot or iced?
Jill: Hot, can you put it in this (puts metal cup on table)
Mo: (rolls eyes because he just took out a styrofoam cup) Sure.
Jill: (under breath) okay then.
Mo: $2.20.
Jill: (hands over money, it’s all in coins)
Mo: (about to take the money)
Jill: Wait actually-
Mo: What?
Jill: Can I have a plain bagel with cream cheese? I don’t want coffee anymore.
Mo: ...sure. $1.09
Jill: (hands over money)
Mo: (takes money and puts it in cash register). Have a good day. (oven beeps and he puts it in a paper bag).
Jill: (leaves without saying thank you)
Mo: Rude.
Location: 3rd Floor Lounge
Terry: (studying) Man, I don’t even know what I’m doing anymore. I have a midterm in two hours and I don’t know anything.
Greg: (typing) Have you been to class though?
Terry: No. I haven’t been to class since the first lecture
Greg: Then how do you expect to do well on this exam, no offense.
Terry: I don’t know? Luck???
Greg: Ok then.
Terry: (sarcastically) Thanks for being supportive. True friend right there.
Greg: I’m just being realistic? (checks phone) I gotta go. I have class, good luck.
Terry: Ok bye then
Location: Liberty St Bus Stop
Jae (in the car, listening to hip hop): Yo this song is so LIT am I right?
Carri: (next to him) Oh my god please watch where you’re going you’re going to kill someone if you don’t look while you’re driving
Jae: Relax babe I got this. I’m a great driver (police siren wails)
Carri: Great driver hm? This guy is about to pull you over.
Jae: (pulls over, rolls down window). What seems to be the problem officer?
Officer Kyle: Sir, your music is way too loud. You almost hit a pedestrian crossing the street and it was a red light at that.
Jae: Oh I’m so sorry sir.
Officer Kyle: Have you ever been pulled over before
Jae: No sir
Officer Kyle: Ok I’m going to let you go with a warning then. Just drive safer next time
Jae: Any time, thanks sir.
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