#dd 1.11
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Hercules k 180 bedienungsanleitung galaxy
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deutsch Betriebsanleitung MERCEDES - Benz C Klasse C 180 C 200 Ausgabe E 3/ 1994 Hercules K 125 BW V1 & V2 Betriebsanleitung, Bedienungsanleitung,SACHS HERCULES L50 L50 Luxus Mofa 25 Moped Bedienungsanleitung Verkauft Siehe ähnliche Artikel EUR 14,90 Sofort-Kaufen, EUR 2,50 Versand, Bauer, S 180, 3000, 20, 0.9. Bauer, HS 180 A, 3000, 20, 0.9 FMB, Galactic (400 SAV), 3420, 27, 0.9 FMB, Herkules, 4120, 34, 1.1. piggyback-mount. I. main telescope tube. J. RA axis scale. K. Flexible DEC slow motion cable. L. T bolt for elevation adjustment. Euracom 180 dito. Beschreibung. 24 dito. Bedienungsanleitung. 104 dito. Einbauanleitung. 14. Programmierung (- Version 1.11). Vor Gebrauch der Batterie die Bedienungsanleitung sorgfältig 1130 K. 09. RNUYTZ14S. 12. 11,2. 150,4 x 85,6 x 112,3 FXR 180 DD SP (M08). K. L. M. N. P. Q. R. S. T. U. H. V. Z. W. Y. Geschwindigkeit (km/h). 50. 100 110 120 130 140 150 160 170 180 190 200 210 240 >240 270 300.
https://poxififenif.tumblr.com/post/691651771224981504/fiat-45-66-dt-werkstatthandbuch, https://poxififenif.tumblr.com/post/691651771224981504/fiat-45-66-dt-werkstatthandbuch, https://poxififenif.tumblr.com/post/691651771224981504/fiat-45-66-dt-werkstatthandbuch, https://poxififenif.tumblr.com/post/691651771224981504/fiat-45-66-dt-werkstatthandbuch, https://poxififenif.tumblr.com/post/691651771224981504/fiat-45-66-dt-werkstatthandbuch.
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Maybe that was God’s plan all along - why He created him, allowed him to fall from grace - to become a symbol to be feared, a warning to us all…to tread the path of the righteous.
Father Lantom, DD 1.11 The Path of the Righteous
You can just see the puzzle pieces coming together in Matt’s head when he hears this.
#netflix daredevil#dd 1.11 the path of the righteous#father lantom#religion#belief#speculation#the devil#god's plan#not quite dead blogging
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1/11/2019 - 3/04/2019: QuixolMC Changelog #26
Happy Monday!
That’s right, we actually got around to publishing a changelog post on the first monday of the month again. Yay us! Quixol’s staff have been hard at work over the past 2 months, fixing bugs and keeping up with the business of the server- we’ve gotten around 100 new players since we last checked in with you all in one of these posts. Wow!
One of the most significant changes to mention in this post is the re-introduction of a marketplace to QuixolMC- Port Tourmaline! Ghalea finally has a place to go to buy & sell goods, and we couldn’t be more excited. It looks like you all were pretty excited, too, because it took just a week for every last player shop to be rented out... wowee! Apologies to anyone who’s been looking to open a player shop, we’ve been doing our best to make sure players are actually actively maintaining their shops.
Not server-related news, but we also have a new page on our blog, the market guide. Check it out if you’re curious how the economy works on QuixolMC-- we recommend you do, because it’s pretty fun earning & spending shells on stuff!
Anyhow, let’s move on with this post. Click below to see all of the changes that’ve been made in the past ~2 months!
Changes are separated into one of four categories: Gameplay, Server Builds / Locations, Technical / Bug fixes, and Plugin / Software updates. They are sorted chronologically within these categories, from oldest to most recent. Dates are formatted mm/dd/yy.
Key:
+ Feature added
- Feature removed
% Feature changed/bug fix
^ Feature updated (usually plugin updates)
# Comment (for… comments.)
Notable changes from 1/11/2019 - 3/04/2019:
- Gameplay -
% [1/15/19] Upped mob-spawn-range value to 6 (previously 4)
# This increases mob spawns on the server overall
% [1/18/19] You can now use the random teleport sign at Orsus's Information Center, like before in 1.12!
% [1/18/19] Phantoms no longer spawn around creative mode players
% [1/23/19] Quickshops now remove LWC locks from containers when created
# You can /lock them again later if you're concerned
% [1/23/19] Quickshops now display the name of the item on the sign if you've renamed it on an anvil
% [1/23/19] Quickshops are now more strict with what items you’re able to sell in a shop, i.e. if an item has different durability/enchantments you need separate shops for each different item
^ [1/26/19] Mob Heads Datapack -> 1.1
# Changed drop chances of a few heads
# + Mooshroom head: 0.2% base -> 2% base , 0.1% looting -> 1% looting
# + Ocelot head: 4% base -> 5% base
# + Witch head: 0.2% base -> 0.5% base
# % Updated skin for the Bat head!
# https://i.imgur.com/Sy6lGPc.png
% [2/11/19] Reduced minimum payment limit for all jobs to 0.01 (previously 0.1)
% [2/17/19] Reset money for all players (back to default 1000)
% [2/17/19] Changed prices of Admin sell shops
# - Gold ingot: 35 -> 5
# - Iron ingot: 5 -> 3.5
# + Coal: 0.25 -> 0.50
# - Lapis lazuli: 7.5 -> 6
# - Cobblestone: 0.10 -> 0.05
# - Netherrack: 0.10 -> 0.08
# + Soul sand: 0.15 -> 0.20
# - Rotten flesh: 0.40 -> 0.20
# + Bone: 0.40 -> 0.50
# - String: 0.65 -> 0.60
# - Gunpowder: 1.00 -> 0.65
% [2/17/19] Changed prices of Admin buy shops
# + Totem of undying: 40,000 -> 35,000
- [2/17/19] Removed Admin buy shops for parrot, polar bear spawn eggs
# Gotta be honest this was a sleep-deprived mistake on our part
- [2/17/19] Removed Admin sell shops for andesite, granite, diorite
+ [2/17/19] Added admin sell shops for beetroot seeds, sugarcane, kelp, cactus
# Prices:
# beetroot seeds: 0.8
# sugarcane: 0.7
# kelp: 0.7
# cactus: 0.7
+ [2/17/19] Added admin buy shops for nautilus shells, dolphin spawn eggs
# Prices:
# nautilus shell: 2250
# dolphin spawn egg: 5000
% [3/02/19] Increased amount of shop chests players can create
# The amount of shops you can create is as follows: Scouts: 30, Members: 40, Pioneers: 50, Veterans: 60
+ [3/04/19] Added color splash tickets for purchase at Port Tourmaline
# They cost 4k per ticket, same as last time
- Server Builds / Locations -
+ [1/16/19] Added more labels to Orsus map
% [1/16/19] Split Orsus' Gourd Street into West Gourd Street & East Gourd Street
% [1/16/19] Added rule to noobis land to ask permission from staff before making any large-scale changes to it
% [1/18/19] Slightly updated signs in info center
+ [2/03/19] Added a hologram at the spawnpoint again
+ [2/07/19] Added addresses to more Orsus buildings
% [2/07/19] Updated signboard 2/5 in the tutorial to reflect updated changes to server mechanics
+ [2/09/19] Introduced a new server warp, /warp fortnite! A pvp arena just for funsies
+ [2/17/19] Introduced a new server warp, Port Tourmaline, our new server market!
# Get there with /warp market
+ [2/17/19] Introduced another new server warp, /warp decoshop!
# A shop made by admin skyler for selling loads of different decorative heads
% [2/27/19] Removed the excess chests from postal office @ orsus
% [2/27/19] Removed the chests from CandyLand (just cleaning up from the secret snowflake)
% [3/02/19] Updated signs at Amanita explaining that it's no longer maintained
+ [3/02/19] Added (some) admin buy shops back to Amanita, because why not.
- Technical / Bug fixes -
% [1/19/19] Fixed issues of faction powerboosts being applied to players incorrectly (hopefully??)
% [1/21/19] Fixed /f show not showing offline players
% [1/21/19] Reduced some clutter/unused stuff from /f show
% [1/30/19] Cleaned up more unused stuff in /f show
% [2/03/19] Additions to word filter
+ [2/03/19] Moderators (and up) no longer count towards the total # of players needed to sleep through the night
% [2/08/19] Fixed the pressure plate at the end of the tutorial rubberbanding you, causing chunks to turn invisible for new players
% [2/09/19] Fixed bug where players lost exp when dying in safezone/warzone
% [2/09/19] Fixed bug where players with no nickname could potentially get their nickname set to <none>
% [2/9/19] Fixed bug where players setting their biography description would send the message in the chat
% [2/11/19] Fixed bug where breaking shulker boxes with items in inventory while in creative mode destroys, instead of drops, the shulker box
% [2/11/19] Fixed visual inconsistency with itemstack entities
% [2/11/19] Fixed all cases where a player could potentially be "voided"
% [2/11/19] Anti-xray works better now ;)
% [2/11/19] Fixed Explorer job not saving explored chunks data to disk!
% [2/11/19] Fixed spectator mode players getting paid for explorer job, miner job (blast mining)
% [2/17/19] Fixed potential exploit
% [2/20/19] Reduced entity-activation-range for animals to 20 (previously 24 blocks)
% [3/02/19] Shop regions now prevent mushroom growth, vine growth, and ice melting
% [3/02/19] Players will now be informed that their shop is about to be unrented on login if they have 2 days or less time before the shop rent is up, also gives more warnings to online players
% [3/03/19] Increased radius in which /qs find searches to 80 blocks (previously 50)
# It now covers the entirety of the market from the center where you spawn in!
% [3/04/19] Fixed the shield block sound not playing
% [3/04/19] Disconnecting from server while you are passenger of a non-player entity should make that entity also disappear with you once again
% [3/04/19] Fixed drowned conversion happening immediately & never showing the zombie shake
% [3/04/19] Lots of fixes to block logging
- Plugin / Software Updates -
^ [1/15/19] AreaShop -> 2.6.0
+ [1/18/19] Re-added RandomTeleport (v1.7.4-b26)
^ [1/19/19] Easyalias -> 1.8.1
^ [1/19/19] FactionExtras -> 1.0.3
^ [1/23/19] Craftbook -> 3.10-beta-02 (Build 4480)
+ [1/23/19] Added Quickshop ReRemake 1.3.5.1
^ [1/30/19] QuickshopReRemake -> 1.3.5.2
^ [2/09/19] Deathpenalty -> 1.1.2
^ [2/09/19] easycommands -> 1.9.1
^ [2/11/19] Updated server .jar, Paper 1.13.2 b498 -> Paper 1.13.2 b521
^ [2/11/19] Jobs -> 4.10.0
^ [2/11/19] QuickshopRR -> 1.3.5.4
^ [2/17/19] Updated server .jar, Paper 1.13.2 b521 -> Paper 1.13.2 b525
^ [2/20/19] Jobs -> 4.10.1
^ [2/20/19] QuickShopRR -> 1.3.5.6
^ [3/04/19] Updated server .jar, Paper 1.13.2 b525 -> Paper 1.13.2 b557
^ [3/04/19] Coreprotect -> 2.16.0
^ [3/04/19] Jobs -> 4.10.3
List of known bugs/issues:
! Player inventories saved in 1.11 - 1.12 (in the world Protos) may have shulker box data corrupted. undyed shulker boxes turn into purple shulker boxes, and any items inside shulker boxes may lose all of their NBT data.
! When you are killed by a mob you recently hurt, the death message just says you were killed by [a bunch of hearts], due to a mcMMO bug
! mcMMO doesn’t give you alchemy exp for making slow falling potions
! Changing factions permissions for containers / doors does not seem to work
! Several other bugs in Factions
! Peculiar bug with items in cursor slot disappearing in the inventory? Has only been reported once; unable to reproduce
! Some players have reported their mcMMO levels being reset/lowered? Has only been reported once; unable to reproduce
! Being killed by another player counts as a “killed by [mob]” in statistics, but killing another player will not increase this statistic, only dying to other players counts
! Peculiar bug with mobs (usually animals) held in pens suffocating in surrounding blocks. Potentially related to mob pathfinding
! Standing still on top of a boat in water will deplete your hunger very quickly for some reason(???)
! New player jingle is broken
! When making a quickshop into a double chest, then breaking it to make two single chests, it still thinks the shop is there, regardless of whether its told to be deleted with commands or manually broken in survival.
Things to come:
• Update to QuixolMC 1.13.2 Public Release!
• Releasing more custom advancements
• Other updates to QuixolMC’s custom datapack
• Fixing bugs, hopefully…
• Updates to qChat, other custom scripts
last changelog post (#25)
about changelogs
That about covers all the changes! As you can see, we’re still rocking that new format for these changelog posts. It’s not quite as meaty as our previous one, but still a lot of text to read through. If you have any suggestions for how we can improve it, let us know!
The staff worked very hard on Tourmaline, and we really hope you’re all enjoying it. Of course, if you have any feedback to give about the market, or anything else on the server, we’d love to hear it- whether it be in the #feedback channel in our discord or elsewhere.
While we’re here, we figure it might be good to give a reminder: running a shop at the market is a big responsibility, and it’s up to you to read the market guide and understand how everything works (as well as the rules for running a shop!) before you rent. You also need to make sure you’re active on the server so you can keep up with renting the shop, respond to messages about your shops, restock everything you sell, etc.
Thanks for reading, and see you next time! - Quixol Staff
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Stock daily Filter Report for 2021/05/20 05-40-10
*******************Part 1.0 Big Cap Industry Overiew********************* big_industry_uptrending_count tickers industry Basic Materials 18 Communication Services 8 Consumer Cyclical 5 Consumer Defensive 17 Energy 18 Financial Services 42 Healthcare 27 Industrials 11 Real Estate 5 Technology 9 Utilities 3 unknown 4 **************************************** big_industry_downtrending_count tickers industry Basic Materials 2 Communication Services 25 Consumer Cyclical 16 Consumer Defensive 6 Energy 1 Financial Services 5 Healthcare 17 Industrials 5 Real Estate 1 Technology 42 Utilities 3 *******************Part 1.1 Big Cap Long Entry SPAN MACD********************* big_long_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 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 86 CP 4 -1.58 big-cap Short NaN -4.0 2.269994e+08 -4.0 Industrials 123 PCAR 1 0.00 big-cap Short NaN -1.0 1.655480e+08 -3.0 Industrials Mean Return: -1.58 Mean Day/Week: 5.0 Accuracy:1.0 *******************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 3 WMT 2 0.01 big-cap Long NaN 41.0 1.627784e+09 5.0 Consumer Defensive 154 YUMC 3 2.42 big-cap Long NaN 16.0 2.107730e+08 5.0 Consumer Cyclical Mean Return: 1.2149999999999999 Mean Day/Week: 2.5 Accuracy:1.0 *******************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 86 CP 4 -1.58 big-cap Short NaN -4.0 2.269994e+08 -4.0 Industrials 123 PCAR 1 0.00 big-cap Short NaN -1.0 1.655480e+08 -3.0 Industrials 246 SQM 1 0.00 big-cap Short NaN -7.0 1.362776e+08 -3.0 Basic Materials Mean Return: -1.58 Mean Day/Week: 6.0 Accuracy:1.0 *******************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 27 C 5 1.57 big-cap Long NaN 5.0 1.778621e+09 78.0 Financial Services 37 INTU 1 0.00 big-cap Long NaN 1.0 5.497157e+08 7.0 Technology 216 STX 4 1.86 big-cap Long NaN 4.0 4.604141e+08 78.0 Technology 55 CME 5 -1.58 big-cap Long NaN 5.0 3.255341e+08 78.0 Financial Services 161 AVB 3 -0.80 big-cap Long NaN 3.0 2.477961e+08 74.0 Real Estate 269 NLY 2 0.54 big-cap Long NaN 2.0 1.423128e+08 48.0 Real Estate 65 BAM 3 0.17 big-cap Long NaN 3.0 1.407119e+08 78.0 Financial Services 263 LNT 1 0.00 big-cap Long NaN 1.0 1.043387e+08 48.0 Utilities 152 FMX 3 2.19 big-cap Long NaN 3.0 5.585835e+07 47.0 Consumer Defensive 76 STLA 3 -1.80 big-cap Long NaN 3.0 5.536462e+07 11.0 unknown 215 BPY 1 0.00 big-cap Long NaN 1.0 4.877528e+07 78.0 Real Estate 13 TM 3 0.40 big-cap Long NaN 3.0 3.984767e+07 6.0 Consumer Cyclical Mean Return: 0.2833333333333333 Mean Day/Week: 3.7777777777777777 Accuracy:0.6666666666666666 *******************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 86 CP 4 -1.58 big-cap Short NaN -4.0 2.269994e+08 -4.0 Industrials 123 PCAR 1 0.00 big-cap Short NaN -1.0 1.655480e+08 -3.0 Industrials 150 HRL 2 -0.76 big-cap Short NaN -2.0 1.328051e+08 -7.0 Consumer Defensive 239 ATHM 1 0.00 big-cap Short NaN -1.0 8.681972e+07 -52.0 Communication Services 172 WISH 5 14.24 big-cap Short NaN -5.0 6.563304e+07 -106.0 Consumer Cyclical Mean Return: 3.966666666666667 Mean Day/Week: 4.333333333333333 Accuracy:0.6666666666666666 *******************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 44 CVS 16 17.13 big-cap Long NaN 25.0 1.770082e+09 40.0 Healthcare 7 BAC 10 -0.05 big-cap Long NaN 10.0 1.742013e+09 78.0 Financial Services 5 PG 8 -0.75 big-cap Long NaN 8.0 1.330450e+09 33.0 Consumer Defensive 2 JNJ 9 0.95 big-cap Long NaN 12.0 9.407681e+08 23.0 Healthcare 26 MS 10 -0.79 big-cap Long NaN 12.0 8.722046e+08 78.0 Financial Services 74 NEM 13 13.58 big-cap Long NaN 50.0 8.571655e+08 34.0 Basic Materials 15 ABBV 15 4.46 big-cap Long NaN 25.0 7.281849e+08 24.0 Healthcare 12 PFE 6 0.36 big-cap Long NaN 50.0 6.776362e+08 29.0 Healthcare 100 GOLD 13 10.46 big-cap Long NaN 48.0 5.775834e+08 25.0 Basic Materials 228 NUE 12 13.06 big-cap Long NaN 12.0 5.573701e+08 67.0 Basic Materials 22 LIN 9 -0.92 big-cap Long NaN 9.0 5.573023e+08 51.0 Basic Materials 134 MPC 10 1.19 big-cap Long NaN 12.0 5.486576e+08 78.0 Energy 60 FDX 15 4.22 big-cap Long NaN 15.0 5.332978e+08 45.0 Industrials 21 AZN 19 7.64 big-cap Long NaN 46.0 5.298471e+08 25.0 Healthcare 79 COF 16 8.92 big-cap Long NaN 16.0 5.183005e+08 78.0 Financial Services 24 PM 28 5.36 big-cap Long NaN 28.0 5.086959e+08 75.0 Consumer Defensive 111 ALXN 24 7.02 big-cap Long NaN 24.0 4.833223e+08 25.0 Healthcare 30 CHTR 18 4.28 big-cap Long NaN 24.0 4.760194e+08 25.0 Communication Services 125 SLB 6 -0.09 big-cap Long NaN 11.0 4.350150e+08 13.0 Energy 78 AON 14 0.36 big-cap Long NaN 14.0 4.349073e+08 78.0 Financial Services 49 MDLZ 16 2.40 big-cap Long NaN 50.0 3.835426e+08 45.0 Consumer Defensive 139 BNTX 9 9.07 big-cap Long NaN 34.0 3.648523e+08 36.0 Healthcare 20 UPS 17 10.12 big-cap Long NaN 38.0 3.634148e+08 38.0 Industrials 108 TROW 11 1.44 big-cap Long NaN 11.0 3.441600e+08 78.0 Financial Services 54 CL 14 3.10 big-cap Long NaN 44.0 3.393656e+08 28.0 Consumer Defensive 271 DVN 6 -0.74 big-cap Long NaN 11.0 3.282580e+08 19.0 Energy 128 EOG 10 5.10 big-cap Long NaN 10.0 3.254008e+08 78.0 Energy 102 DOW 10 -0.62 big-cap Long NaN 10.0 2.951366e+08 75.0 Basic Materials 113 ALL 39 17.10 big-cap Long NaN 55.0 2.777950e+08 57.0 Financial Services 68 DD 11 4.17 big-cap Long NaN 12.0 2.633936e+08 26.0 Basic Materials 72 NOC 15 5.27 big-cap Long NaN 56.0 2.541685e+08 53.0 Industrials 122 KMI 15 6.75 big-cap Long NaN 15.0 2.533988e+08 78.0 Energy 124 PRU 26 9.19 big-cap Long NaN 26.0 2.410383e+08 78.0 Financial Services 231 DPZ 23 8.34 big-cap Long NaN 46.0 2.293123e+08 32.0 Consumer Cyclical 214 CFG 13 4.58 big-cap Long NaN 13.0 2.275249e+08 78.0 Financial Services 143 WLTW 15 5.53 big-cap Long NaN 15.0 2.166606e+08 78.0 Financial Services 176 SYF 13 5.10 big-cap Long NaN 13.0 2.117669e+08 78.0 unknown 119 AIG 10 1.11 big-cap Long NaN 10.0 2.098879e+08 78.0 Financial Services 153 CERN 8 1.41 big-cap Long NaN 43.0 2.084861e+08 15.0 Healthcare 174 FITB 6 1.89 big-cap Long NaN 13.0 2.001754e+08 78.0 Financial Services 71 ITUB 8 2.20 big-cap Long NaN 45.0 1.936721e+08 20.0 Financial Services 147 WMB 13 5.06 big-cap Long NaN 13.0 1.821499e+08 78.0 Energy 148 SU 6 -1.72 big-cap Long NaN 11.0 1.807345e+08 78.0 Energy 198 ET 16 17.76 big-cap Long NaN 16.0 1.750936e+08 75.0 Energy 207 HES 13 7.67 big-cap Long NaN 13.0 1.735432e+08 78.0 Energy 126 HSY 14 4.80 big-cap Long NaN 14.0 1.720814e+08 52.0 Consumer Defensive 43 GSK 10 3.51 big-cap Long NaN 49.0 1.694134e+08 25.0 Healthcare 99 KHC 10 1.07 big-cap Long NaN 10.0 1.678100e+08 78.0 Consumer Defensive 181 IP 39 14.97 big-cap Long NaN 39.0 1.614490e+08 57.0 Consumer Cyclical 201 MTB 11 1.06 big-cap Long NaN 11.0 1.595921e+08 78.0 Financial Services 234 ALLY 17 6.67 big-cap Long NaN 17.0 1.579314e+08 78.0 Financial Services 206 KEY 15 3.90 big-cap Long NaN 15.0 1.559762e+08 78.0 Financial Services 29 RY 15 5.23 big-cap Long NaN 15.0 1.551843e+08 78.0 Financial Services 193 AKAM 10 2.50 big-cap Long NaN 42.0 1.540660e+08 11.0 Technology 136 ADM 18 10.91 big-cap Long NaN 18.0 1.521249e+08 78.0 Consumer Defensive 151 FRC 19 3.90 big-cap Long NaN 19.0 1.488917e+08 78.0 Financial Services 245 IT 20 16.05 big-cap Long NaN 20.0 1.468782e+08 78.0 Technology 200 AEM 8 5.31 big-cap Long NaN 45.0 1.429561e+08 13.0 Basic Materials 223 EXPD 11 5.15 big-cap Long NaN 11.0 1.423643e+08 57.0 Industrials 236 CE 13 3.00 big-cap Long NaN 13.0 1.388502e+08 63.0 Basic Materials 132 LYB 10 -3.21 big-cap Long NaN 10.0 1.386540e+08 78.0 Basic Materials 254 NLOK 6 5.31 big-cap Long NaN 6.0 1.357761e+08 35.0 Technology 82 EPD 6 1.40 big-cap Long NaN 15.0 1.344373e+08 57.0 Energy 103 BCE 15 3.62 big-cap Long NaN 15.0 1.307397e+08 53.0 Communication Services 182 WPM 13 9.01 big-cap Long NaN 48.0 1.295256e+08 25.0 Basic Materials 168 EFX 24 21.42 big-cap Long NaN 42.0 1.289334e+08 35.0 Industrials 36 TD 15 4.14 big-cap Long NaN 15.0 1.270868e+08 78.0 Financial Services 195 APO 21 8.90 big-cap Long NaN 26.0 1.248557e+08 26.0 Financial Services 281 KL 9 5.62 big-cap Long NaN 46.0 1.235711e+08 25.0 Basic Materials 179 GWW 25 12.69 big-cap Long NaN 60.0 1.171876e+08 45.0 Industrials 159 FNV 29 9.26 big-cap Long NaN 49.0 1.147016e+08 35.0 Basic Materials 135 NTR 6 -0.05 big-cap Long NaN 12.0 1.146960e+08 12.0 Basic Materials 75 BMO 15 6.17 big-cap Long NaN 15.0 1.126020e+08 78.0 Financial Services 274 LKQ 18 10.20 big-cap Long NaN 18.0 1.100334e+08 76.0 Consumer Cyclical 177 K 10 -2.16 big-cap Long NaN 10.0 1.084247e+08 46.0 Consumer Defensive 276 UHS 30 13.93 big-cap Long NaN 50.0 1.036003e+08 34.0 Healthcare 129 MSI 8 -0.73 big-cap Long NaN 8.0 1.025793e+08 76.0 Technology 277 ICLR 19 4.14 big-cap Long NaN 38.0 1.010109e+08 25.0 Healthcare 283 TXT 36 18.92 big-cap Long NaN 50.0 1.005900e+08 78.0 Industrials 18 UL 14 3.13 big-cap Long NaN 49.0 9.909542e+07 28.0 Consumer Defensive 142 CNQ 9 -3.36 big-cap Long NaN 9.0 9.820118e+07 78.0 Energy 16 NVO 8 4.87 big-cap Long NaN 27.0 9.586948e+07 23.0 Healthcare 59 BNS 6 1.42 big-cap Long NaN 9.0 9.292612e+07 78.0 Financial Services 242 CINF 13 4.14 big-cap Long NaN 13.0 9.115112e+07 78.0 Financial Services 23 BUD 23 9.23 big-cap Long NaN 45.0 8.879566e+07 29.0 Consumer Defensive 213 LNG 14 7.64 big-cap Long NaN 14.0 8.641108e+07 78.0 Energy 249 DVA 14 4.99 big-cap Long NaN 47.0 8.252722e+07 24.0 Healthcare 286 CBOE 13 4.59 big-cap Long NaN 13.0 8.070510e+07 66.0 Financial Services 189 NTRS 17 5.55 big-cap Long NaN 17.0 8.014568e+07 76.0 Financial Services 104 CM 15 5.93 big-cap Long NaN 15.0 7.299673e+07 78.0 Financial Services 247 EMN 15 7.03 big-cap Long NaN 15.0 7.099230e+07 78.0 Basic Materials 173 CCEP 25 9.41 big-cap Long NaN 25.0 6.920041e+07 78.0 Consumer Defensive 248 BEN 11 -0.38 big-cap Long NaN 11.0 6.802404e+07 78.0 Financial Services 155 MPLX 12 5.66 big-cap Long NaN 13.0 6.475360e+07 78.0 Energy 81 ABEV 10 7.23 big-cap Long NaN 10.0 6.260389e+07 26.0 Consumer Defensive 41 DEO 6 2.92 big-cap Long NaN 30.0 5.990909e+07 56.0 Consumer Defensive 28 SNY 9 2.01 big-cap Long NaN 49.0 5.274773e+07 29.0 Healthcare 175 DB 6 3.18 big-cap Long NaN 15.0 5.141678e+07 19.0 Financial Services 64 EQNR 11 2.60 big-cap Long NaN 16.0 5.075540e+07 27.0 Energy 287 WLK 19 7.05 big-cap Long NaN 25.0 4.575910e+07 53.0 Basic Materials 243 LBTYK 15 0.29 big-cap Long NaN 15.0 4.016325e+07 51.0 unknown 112 CRH 10 -0.12 big-cap Long NaN 10.0 3.496882e+07 39.0 Basic Materials 232 PBA 15 3.23 big-cap Long NaN 16.0 3.322495e+07 55.0 Energy 127 BBVA 9 2.97 big-cap Long NaN 15.0 3.086787e+07 18.0 Financial Services 145 TU 8 0.33 big-cap Long NaN 15.0 2.272214e+07 11.0 Communication Services 70 SAN 6 2.58 big-cap Long NaN 16.0 1.966764e+07 78.0 Financial Services 117 LYG 15 6.26 big-cap Long NaN 15.0 1.856427e+07 78.0 Financial Services 87 AMX 9 0.38 big-cap Long NaN 9.0 1.790325e+07 30.0 Communication Services 237 IMO 15 13.64 big-cap Long NaN 15.0 1.774240e+07 78.0 Energy 95 NGG 14 4.44 big-cap Long NaN 47.0 1.480961e+07 34.0 Utilities 205 GRFS 9 3.76 big-cap Long NaN 45.0 1.076314e+07 26.0 Healthcare 162 RCI 13 2.72 big-cap Long NaN 28.0 9.132474e+06 34.0 Communication Services 225 KB 9 -1.71 big-cap Long NaN 9.0 8.256258e+06 53.0 Financial Services 288 RDY 15 4.69 big-cap Long NaN 38.0 6.200680e+06 25.0 Healthcare 85 AMOV 8 -0.25 big-cap Long NaN 9.0 6.722885e+04 22.0 Communication Services 212 VAR 14 0.27 big-cap Long NaN 14.0 0.000000e+00 54.0 Healthcare Mean Return: 5.022413793103448 Mean Day/Week: 13.905172413793103 Accuracy:0.853448275862069 *******************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 8 -3.00 big-cap Short NaN -8.0 3.483015e+09 -48.0 Consumer Cyclical 46 NIO 6 -0.10 big-cap Short NaN -6.0 2.295204e+09 -60.0 Consumer Cyclical 34 ABNB 10 -10.11 big-cap Short NaN -59.0 2.139037e+09 -31.0 Communication Services 8 NFLX 11 -1.59 big-cap Short NaN -20.0 1.580143e+09 -21.0 Communication Services 9 INTC 7 0.65 big-cap Short NaN -27.0 1.212743e+09 -19.0 Technology 19 JD 12 -7.07 big-cap Short NaN -59.0 1.100689e+09 -52.0 Consumer Cyclical 48 BIDU 18 -13.04 big-cap Short NaN -57.0 9.503348e+08 -40.0 Communication Services 14 AVGO 8 1.45 big-cap Short NaN -23.0 8.923155e+08 -14.0 Technology 42 MELI 9 -10.91 big-cap Short NaN -9.0 8.294256e+08 -56.0 Consumer Cyclical 40 UBER 10 6.33 big-cap Short NaN -21.0 8.131971e+08 -15.0 Technology 1 TSM 14 -3.51 big-cap Short NaN -25.0 7.947483e+08 -48.0 Technology 170 DKNG 10 -16.65 big-cap Short NaN -39.0 7.545681e+08 -27.0 Consumer Cyclical 180 VIPS 7 -15.10 big-cap Short NaN -39.0 7.462695e+08 -35.0 Consumer Cyclical 10 PDD 7 1.51 big-cap Short NaN -7.0 6.924522e+08 -54.0 Consumer Cyclical 47 SNAP 12 -2.94 big-cap Short NaN -12.0 6.760460e+08 -23.0 Communication Services 62 TWLO 12 -12.60 big-cap Short NaN -12.0 6.393401e+08 -15.0 Communication Services 96 TWTR 13 -2.82 big-cap Short NaN -23.0 6.023757e+08 -16.0 Communication Services 33 NOW 7 -4.52 big-cap Short NaN -13.0 5.049714e+08 -15.0 Technology 83 CVNA 8 -5.52 big-cap Short NaN -9.0 5.001602e+08 -11.0 Consumer Cyclical 107 TTD 10 -13.75 big-cap Short NaN -10.0 4.392886e+08 -14.0 Technology 89 PINS 7 -1.58 big-cap Short NaN -22.0 4.146341e+08 -16.0 Communication Services 149 ETSY 11 -9.32 big-cap Short NaN -22.0 4.129378e+08 -23.0 Consumer Cyclical 61 SPOT 14 -13.17 big-cap Short NaN -14.0 4.034544e+08 -59.0 Communication Services 101 TDOC 13 -16.79 big-cap Short NaN -13.0 3.370761e+08 -55.0 Healthcare 109 RNG 11 -15.18 big-cap Short NaN -11.0 3.291555e+08 -55.0 Technology 88 DOCU 11 -3.26 big-cap Short NaN -11.0 3.238871e+08 -15.0 Technology 220 QS 17 -25.79 big-cap Short NaN -40.0 3.205041e+08 -137.0 Consumer Cyclical 58 ILMN 14 -1.45 big-cap Short NaN -14.0 3.174515e+08 -39.0 Healthcare 192 DISCK 6 0.10 big-cap Short NaN -38.0 3.145414e+08 -32.0 Communication Services 141 ZS 12 -5.98 big-cap Short NaN -12.0 3.106146e+08 -55.0 Technology 84 TME 15 -19.28 big-cap Short NaN -40.0 3.085219e+08 -40.0 Communication Services 211 PENN 17 -15.60 big-cap Short NaN -45.0 3.019894e+08 -34.0 Consumer Cyclical 116 Z 16 -22.06 big-cap Short NaN -59.0 2.959101e+08 -44.0 Communication Services 94 BILI 10 -0.34 big-cap Short NaN -10.0 2.922109e+08 -45.0 Communication Services 221 SEDG 12 7.28 big-cap Short NaN -12.0 2.819544e+08 -16.0 Technology 235 LYFT 10 5.21 big-cap Short NaN -40.0 2.718218e+08 -15.0 Technology 140 SPLK 15 -12.33 big-cap Short NaN -15.0 2.698673e+08 -137.0 Technology 194 NVAX 10 -16.80 big-cap Short NaN -11.0 2.680206e+08 -15.0 Healthcare 199 IQ 37 -18.23 big-cap Short NaN -40.0 2.591226e+08 -39.0 Communication Services 244 RUN 12 -1.48 big-cap Short NaN -12.0 2.582810e+08 -63.0 Technology 233 WIX 6 -1.76 big-cap Short NaN -10.0 2.491517e+08 -14.0 Technology 163 COUP 10 -3.72 big-cap Short NaN -10.0 2.481801e+08 -55.0 Technology 98 U 9 -0.25 big-cap Short NaN -9.0 2.348377e+08 -72.0 Technology 166 MDB 11 0.97 big-cap Short NaN -11.0 2.302767e+08 -54.0 Technology 278 BILL 8 -0.79 big-cap Short NaN -13.0 2.071649e+08 -16.0 Technology 257 SPCE 33 -49.52 big-cap Short NaN -61.0 1.950983e+08 -53.0 Industrials 178 FTCH 14 -21.12 big-cap Short NaN -60.0 1.875129e+08 -41.0 Consumer Cyclical 272 BYND 12 -17.78 big-cap Short NaN -12.0 1.869538e+08 -50.0 Consumer Defensive 186 TXG 7 9.84 big-cap Short NaN -11.0 1.790517e+08 -10.0 Healthcare 240 OPEN 10 -17.59 big-cap Short NaN -10.0 1.775162e+08 -41.0 Real Estate 130 EDU 8 -20.46 big-cap Short NaN -8.0 1.697334e+08 -47.0 Consumer Defensive 260 CHGG 12 -9.97 big-cap Short NaN -12.0 1.645329e+08 -55.0 Consumer Defensive 90 VEEV 7 0.26 big-cap Short NaN -9.0 1.627397e+08 -13.0 Healthcare 158 EXAS 12 -17.55 big-cap Short NaN -12.0 1.588441e+08 -15.0 Healthcare 171 HOLX 15 -5.38 big-cap Short NaN -16.0 1.587338e+08 -22.0 Healthcare 270 RGEN 8 0.86 big-cap Short NaN -11.0 1.553115e+08 -12.0 Healthcare 80 TAL 11 -15.92 big-cap Short NaN -11.0 1.500053e+08 -50.0 Consumer Defensive 258 FSLY 12 -25.95 big-cap Short NaN -12.0 1.470217e+08 -64.0 Technology 252 AVLR 11 -5.14 big-cap Short NaN -11.0 1.375022e+08 -62.0 Technology 144 SIRI 6 0.71 big-cap Short NaN -16.0 1.330645e+08 -13.0 Communication Services 97 CHWY 11 -4.69 big-cap Short NaN -11.0 1.317199e+08 -55.0 Consumer Cyclical 160 STNE 10 -3.53 big-cap Short NaN -10.0 1.261297e+08 -54.0 Technology 284 NRG 22 -8.64 big-cap Short NaN -45.0 1.259715e+08 -45.0 Utilities 259 CRSP 9 3.10 big-cap Short NaN -9.0 1.245985e+08 -62.0 Healthcare 250 ESTC 11 -1.81 big-cap Short NaN -11.0 1.231978e+08 -56.0 Technology 169 MKTX 11 -2.70 big-cap Short NaN -18.0 1.231065e+08 -22.0 Financial Services 289 PLAN 9 0.43 big-cap Short NaN -9.0 1.161391e+08 -59.0 Technology 255 CREE 14 -6.37 big-cap Short NaN -15.0 1.146538e+08 -15.0 Technology 264 CABO 10 0.89 big-cap Short NaN -10.0 1.086198e+08 -69.0 Communication Services 202 PAGS 12 0.26 big-cap Short NaN -12.0 1.061489e+08 -52.0 Technology 210 CTXS 13 -6.59 big-cap Short NaN -23.0 1.031814e+08 -15.0 Technology 164 PAYC 11 -5.96 big-cap Short NaN -11.0 9.917523e+07 -14.0 Technology 185 CTLT 10 -5.51 big-cap Short NaN -11.0 9.872644e+07 -14.0 Healthcare 92 RKT 10 -10.24 big-cap Short NaN -42.0 9.718035e+07 -26.0 Financial Services 25 SNE 11 -1.79 big-cap Short NaN -19.0 8.656694e+07 -16.0 Technology 219 GH 7 -4.52 big-cap Short NaN -12.0 7.977503e+07 -12.0 Healthcare 265 FFIV 8 -1.06 big-cap Short NaN -25.0 7.010007e+07 -16.0 Technology 266 DT 6 4.96 big-cap Short NaN -14.0 7.000428e+07 -13.0 Technology 229 APPN 15 -41.12 big-cap Short NaN -63.0 6.660962e+07 -55.0 Technology 224 BMRN 7 0.99 big-cap Short NaN -7.0 5.708467e+07 -64.0 Healthcare 238 MASI 12 -4.42 big-cap Short NaN -12.0 5.577596e+07 -62.0 Healthcare 261 IPGP 12 5.28 big-cap Short NaN -12.0 5.288057e+07 -14.0 Technology 273 LW 7 -1.24 big-cap Short NaN -12.0 5.077733e+07 -12.0 Consumer Defensive 115 ZG 16 -22.73 big-cap Short NaN -59.0 5.007643e+07 -44.0 Communication Services 183 GDRX 10 -15.01 big-cap Short NaN -10.0 4.924561e+07 -54.0 Healthcare 184 ZI 11 -11.29 big-cap Short NaN -11.0 4.734839e+07 -13.0 Technology 218 LSXMK 6 -1.26 big-cap Short NaN -25.0 4.412834e+07 -13.0 Communication Services 227 CGC 13 -10.85 big-cap Short NaN -63.0 4.224183e+07 -44.0 Healthcare 280 PCTY 9 -3.55 big-cap Short NaN -11.0 4.063679e+07 -13.0 Technology 267 KC 11 -12.34 big-cap Short NaN -11.0 3.855784e+07 -45.0 Technology 217 LSXMA 8 -2.88 big-cap Short NaN -25.0 3.231797e+07 -13.0 Communication Services 253 ERIE 14 -7.95 big-cap Short NaN -14.0 1.355644e+07 -54.0 Financial Services 268 ENIA 14 -0.86 big-cap Short NaN -26.0 1.272405e+07 -20.0 Utilities 285 BCH 6 -12.04 big-cap Short NaN -38.0 3.873352e+06 -9.0 Financial Services Mean Return: -7.389787234042552 Mean Day/Week: 11.24468085106383 Accuracy:0.7978723404255319 ************************************** ************************************** ************************************** *******************Part 2.0 Small Cap Industry Overiew********************* small_industry_uptrending_count tickers industry Basic Materials 10 Communication Services 3 Consumer Cyclical 6 Consumer Defensive 3 Energy 5 Financial Services 15 Healthcare 6 Industrials 8 Real Estate 6 Technology 2 Utilities 4 unknown 1 **************************************** small_industry_downtrending_count tickers industry Communication Services 5 Consumer Cyclical 7 Consumer Defensive 2 Financial Services 4 Healthcare 24 Industrials 5 Real Estate 3 Technology 32 Utilities 4 *******************Part 2.1 Small Cap Long Entry SPAN MACD********************* small_long_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 2.2 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 424 MPLN 2 -0.26 small-cap Long NaN 30.0 1.133372e+07 5.0 Healthcare Mean Return: -0.26 Mean Day/Week: 2.0 Accuracy:0.0 *******************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 394 HOG 3 -5.86 small-cap Long NaN 3.0 1.072078e+08 36.0 Consumer Cyclical 351 ADT 1 0.00 small-cap Long NaN 1.0 7.414111e+07 28.0 Industrials 374 VRT 3 1.42 small-cap Long NaN 3.0 5.547206e+07 34.0 Industrials 410 PBCT 4 -2.13 small-cap Long NaN 4.0 4.679201e+07 78.0 Financial Services 301 MRVI 2 0.26 small-cap Long NaN 2.0 4.296976e+07 64.0 Healthcare 302 KOF 4 0.50 small-cap Long NaN 4.0 9.241146e+06 32.0 Consumer Defensive 359 BSMX 2 -0.34 small-cap Long NaN 2.0 1.114807e+06 38.0 Financial Services Mean Return: -1.0250000000000001 Mean Day/Week: 3.1666666666666665 Accuracy:0.5 *******************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 428 BPMC 1 0.00 small-cap Short NaN -1.0 2.148873e+07 -14.0 Healthcare 433 CERT 5 -0.05 small-cap Short NaN -5.0 1.251971e+07 -14.0 Healthcare 441 ABCM 1 0.00 small-cap Short NaN -1.0 6.227866e+06 -12.0 Healthcare Mean Return: -0.05 Mean Day/Week: 7.0 Accuracy:1.0 *******************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 349 STLD 17 12.90 small-cap Long NaN 17.0 1.589294e+08 74.0 Basic Materials 309 GFI 9 12.68 small-cap Long NaN 9.0 1.486978e+08 34.0 Basic Materials 303 CF 15 7.39 small-cap Long NaN 16.0 1.363204e+08 78.0 Basic Materials 379 DXC 19 15.21 small-cap Long NaN 36.0 1.258346e+08 39.0 Technology 402 MTZ 17 9.41 small-cap Long NaN 17.0 1.249189e+08 78.0 Industrials 414 CCJ 9 -3.61 small-cap Long NaN 11.0 1.208341e+08 78.0 Energy 332 CMA 14 2.68 small-cap Long NaN 14.0 1.204351e+08 78.0 Financial Services 307 LNC 9 0.60 small-cap Long NaN 9.0 9.232200e+07 78.0 Financial Services 317 JAZZ 9 0.40 small-cap Long NaN 10.0 8.838048e+07 11.0 Healthcare 330 NLSN 9 -1.59 small-cap Long NaN 9.0 8.324553e+07 78.0 Industrials 372 SEE 29 17.43 small-cap Long NaN 52.0 8.273914e+07 52.0 Consumer Cyclical 297 IRM 9 2.13 small-cap Long NaN 9.0 7.817459e+07 78.0 Real Estate 337 ATH 25 14.46 small-cap Long NaN 25.0 7.570362e+07 78.0 Financial Services 386 OSK 11 2.61 small-cap Long NaN 11.0 7.065544e+07 78.0 Industrials 397 PAA 8 2.92 small-cap Long NaN 15.0 6.796954e+07 19.0 Energy 357 RGLD 10 4.32 small-cap Long NaN 37.0 6.146387e+07 35.0 Basic Materials 313 PHYS 8 2.12 small-cap Long NaN 33.0 5.964465e+07 13.0 unknown 383 TRGP 17 12.22 small-cap Long NaN 17.0 5.870118e+07 78.0 Energy 340 JLL 12 6.54 small-cap Long NaN 12.0 5.869310e+07 78.0 Real Estate 376 TFII 17 11.87 small-cap Long NaN 17.0 5.615339e+07 78.0 Industrials 353 CLR 7 -1.25 small-cap Long NaN 11.0 5.502259e+07 18.0 Energy 296 HSIC 21 9.08 small-cap Long NaN 47.0 5.400936e+07 29.0 Healthcare 368 BERY 12 5.56 small-cap Long NaN 12.0 4.877876e+07 72.0 Consumer Cyclical 334 AFG 10 1.42 small-cap Long NaN 10.0 4.272593e+07 78.0 Financial Services 405 PRGO 7 0.72 small-cap Long NaN 25.0 4.163180e+07 10.0 Healthcare 360 SC 31 28.22 small-cap Long NaN 31.0 3.895895e+07 76.0 Financial Services 407 CFR 16 3.19 small-cap Long NaN 16.0 3.550712e+07 78.0 Financial Services 427 LEG 26 12.95 small-cap Long NaN 26.0 3.497723e+07 58.0 Consumer Cyclical 413 JHG 21 11.02 small-cap Long NaN 34.0 3.156655e+07 33.0 Financial Services 421 ORI 33 16.21 small-cap Long NaN 70.0 2.894894e+07 78.0 Financial Services 420 VNT 6 1.50 small-cap Long NaN 26.0 2.697419e+07 10.0 Technology 391 POST 39 9.62 small-cap Long NaN 53.0 2.541756e+07 57.0 Consumer Defensive 422 PSXP 10 2.13 small-cap Long NaN 59.0 2.486150e+07 55.0 Energy 406 SON 15 3.07 small-cap Long NaN 15.0 2.051996e+07 53.0 Consumer Cyclical 311 VEDL 14 10.15 small-cap Long NaN 14.0 1.970965e+07 78.0 Basic Materials 398 SRCL 16 11.09 small-cap Long NaN 33.0 1.495062e+07 28.0 Industrials 436 KT 7 4.59 small-cap Long NaN 7.0 1.036315e+07 68.0 Communication Services 324 PSO 9 1.72 small-cap Long NaN 9.0 3.294374e+06 78.0 Communication Services 362 ICL 13 1.11 small-cap Long NaN 13.0 1.158584e+06 78.0 Basic Materials Mean Return: 6.789487179487178 Mean Day/Week: 15.025641025641026 Accuracy:0.9230769230769231 *******************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 365 MSTR 24 -39.20 small-cap Short NaN -60.0 6.824087e+08 -41.0 Technology 380 IOVA 7 -37.53 small-cap Short NaN -7.0 6.049573e+08 -62.0 Healthcare 352 LITE 6 11.81 small-cap Short NaN -13.0 1.766474e+08 -14.0 Technology 356 FCEL 17 -17.53 small-cap Short NaN -64.0 1.548364e+08 -46.0 Industrials 435 APPS 10 1.07 small-cap Short NaN -53.0 1.487696e+08 -24.0 Technology 295 GWRE 9 -3.29 small-cap Short NaN -9.0 1.403973e+08 -62.0 Technology 377 CCIV 13 -4.26 small-cap Short NaN -58.0 1.192881e+08 -37.0 Financial Services 381 PSTG 17 -16.16 small-cap Short NaN -60.0 1.166806e+08 -44.0 Technology 403 CLGX 11 -0.16 small-cap Short NaN -53.0 1.116121e+08 -36.0 Technology 290 BLDP 12 -4.70 small-cap Short NaN -12.0 1.062259e+08 -55.0 Industrials 322 LMND 10 0.36 small-cap Short NaN -10.0 1.053757e+08 -56.0 Financial Services 305 IIVI 14 -2.17 small-cap Short NaN -14.0 1.052120e+08 -55.0 Technology 323 OHI 6 2.71 small-cap Short NaN -11.0 9.257115e+07 -12.0 Real Estate 336 FVRR 15 -21.17 small-cap Short NaN -15.0 8.723903e+07 -44.0 Communication Services 292 NVTA 13 -19.06 small-cap Short NaN -13.0 8.669642e+07 -66.0 Healthcare 326 SPWR 15 -14.84 small-cap Short NaN -64.0 8.645328e+07 -53.0 Technology 439 ARRY 8 -37.48 small-cap Short NaN -8.0 8.064426e+07 -65.0 Technology 373 NCNO 13 -10.68 small-cap Short NaN -13.0 7.940251e+07 -61.0 Technology 299 BFAM 14 -7.14 small-cap Short NaN -29.0 7.824551e+07 -23.0 Consumer Cyclical 329 IONS 10 -5.22 small-cap Short NaN -10.0 7.193582e+07 -55.0 Healthcare 344 YY 16 -11.77 small-cap Short NaN -53.0 7.054464e+07 -38.0 Communication Services 314 TWST 12 -16.76 small-cap Short NaN -12.0 6.925059e+07 -59.0 Healthcare 385 PACB 14 -18.42 small-cap Short NaN -61.0 6.342431e+07 -40.0 Healthcare 350 API 15 -21.14 small-cap Short NaN -15.0 6.193441e+07 -45.0 Technology 343 RDFN 10 -0.77 small-cap Short NaN -10.0 5.992247e+07 -47.0 Real Estate 319 FATE 10 -0.94 small-cap Short NaN -10.0 5.767991e+07 -55.0 Healthcare 318 WEX 8 -4.29 small-cap Short NaN -15.0 5.698516e+07 -15.0 Technology 327 NYT 31 -15.01 small-cap Short NaN -54.0 5.670364e+07 -50.0 Communication Services 392 BE 10 -4.34 small-cap Short NaN -10.0 5.647162e+07 -59.0 Industrials 312 SMAR 17 -14.47 small-cap Short NaN -22.0 5.176655e+07 -55.0 Technology 355 QTWO 10 -0.87 small-cap Short NaN -10.0 4.912231e+07 -56.0 Technology 399 HUYA 15 -17.84 small-cap Short NaN -57.0 4.742344e+07 -41.0 Communication Services 411 TNDM 8 1.74 small-cap Short NaN -9.0 4.398743e+07 -12.0 Healthcare 366 ONEM 8 -11.35 small-cap Short NaN -8.0 4.318131e+07 -54.0 Healthcare 426 UPWK 15 -12.37 small-cap Short NaN -23.0 4.266661e+07 -40.0 Industrials 440 SAIL 12 -6.74 small-cap Short NaN -12.0 4.263928e+07 -47.0 Technology 335 AMWL 11 -13.43 small-cap Short NaN -11.0 4.224134e+07 -62.0 Healthcare 415 VRNS 13 -11.86 small-cap Short NaN -13.0 4.135103e+07 -45.0 Technology 434 BIGC 10 3.17 small-cap Short NaN -10.0 3.958501e+07 -67.0 Technology 408 HAE 23 -25.65 small-cap Short NaN -23.0 3.897076e+07 -54.0 Healthcare 423 NATI 13 -1.58 small-cap Short NaN -14.0 3.815456e+07 -14.0 Technology 425 INSP 8 -1.73 small-cap Short NaN -11.0 3.726629e+07 -11.0 Healthcare 363 RCM 11 -0.48 small-cap Short NaN -11.0 3.690916e+07 -13.0 Healthcare 369 TGTX 13 -27.89 small-cap Short NaN -40.0 3.668719e+07 -48.0 Healthcare 341 CHDN 10 -4.74 small-cap Short NaN -41.0 3.663522e+07 -20.0 Consumer Cyclical 401 SLAB 7 -2.16 small-cap Short NaN -15.0 3.506631e+07 -15.0 Technology 347 MLCO 9 -5.79 small-cap Short NaN -41.0 3.503193e+07 -15.0 Consumer Cyclical 387 CYBR 11 1.68 small-cap Short NaN -11.0 3.490438e+07 -17.0 Technology 430 SDC 11 -4.09 small-cap Short NaN -11.0 3.483082e+07 -65.0 Healthcare 378 ORA 11 -1.78 small-cap Short NaN -11.0 3.402716e+07 -59.0 Utilities 417 FROG 10 -9.24 small-cap Short NaN -10.0 3.387426e+07 -62.0 Technology 333 EXPI 26 -24.73 small-cap Short NaN -59.0 3.278937e+07 -41.0 Real Estate 437 VNET 16 -16.26 small-cap Short NaN -66.0 3.141064e+07 -54.0 Technology 418 CRUS 13 2.07 small-cap Short NaN -14.0 3.098358e+07 -14.0 Technology 384 SDGR 11 -7.73 small-cap Short NaN -11.0 2.969657e+07 -54.0 Healthcare 348 SHC 12 -6.05 small-cap Short NaN -12.0 2.925766e+07 -44.0 Healthcare 346 BL 10 -0.50 small-cap Short NaN -10.0 2.872337e+07 -62.0 Technology 345 ADPT 10 -5.30 small-cap Short NaN -10.0 2.768647e+07 -56.0 Healthcare 358 ANGI 10 -2.20 small-cap Short NaN -13.0 2.487441e+07 -20.0 Communication Services 367 TTEK 7 -2.81 small-cap Short NaN -24.0 2.282844e+07 -18.0 Industrials 396 NEO 11 -9.71 small-cap Short NaN -11.0 2.191001e+07 -17.0 Healthcare 444 POWI 8 -1.20 small-cap Short NaN -12.0 2.188058e+07 -54.0 Technology 390 MDLA 9 -5.91 small-cap Short NaN -9.0 1.826025e+07 -53.0 Technology 371 KOD 10 -21.07 small-cap Short NaN -10.0 1.811894e+07 -65.0 Healthcare 400 CWEN 9 -2.59 small-cap Short NaN -9.0 1.686199e+07 -64.0 Utilities 364 DCT 14 -10.16 small-cap Short NaN -14.0 1.429313e+07 -44.0 Technology 395 FIZZ 15 -8.12 small-cap Short NaN -15.0 1.429186e+07 -49.0 Consumer Defensive 354 ALLK 10 -0.23 small-cap Short NaN -10.0 1.406303e+07 -62.0 Healthcare 432 APPF 11 -1.36 small-cap Short NaN -11.0 1.351537e+07 -59.0 Technology 416 ALGM 15 -4.89 small-cap Short NaN -15.0 1.157117e+07 -59.0 Technology 443 ENIC 15 -12.02 small-cap Short NaN -17.0 7.234522e+06 -17.0 Utilities Mean Return: -8.821408450704226 Mean Day/Week: 12.225352112676056 Accuracy:0.8873239436619719 ************************************** ************************************** ************************************** *******************Part 3.0 Penny Cap Industry Overiew********************* penny_industry_uptrending_count tickers industry Basic Materials 28 Communication Services 6 Consumer Cyclical 22 Consumer Defensive 12 Energy 21 Financial Services 37 Healthcare 13 Industrials 29 Real Estate 9 Technology 12 Utilities 2 unknown 2 **************************************** penny_industry_downtrending_count tickers industry Basic Materials 6 Communication Services 12 Consumer Cyclical 24 Consumer Defensive 8 Energy 2 Financial Services 19 Healthcare 96 Industrials 20 Real Estate 18 Technology 66 Utilities 1 unknown 2 *******************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 901 PLCE 3 -1.57 penny-cap Long NaN 3.0 9.067042e+07 4.0 Consumer Cyclical 583 VG 1 0.00 penny-cap Long NaN 1.0 3.064799e+07 5.0 Communication Services 760 MGRC 3 -1.49 penny-cap Long NaN 3.0 9.365924e+06 5.0 Industrials 707 ENLC 4 0.99 penny-cap Long NaN 4.0 8.153300e+06 4.0 Energy 825 PACK 2 0.18 penny-cap Long NaN 2.0 6.927393e+06 3.0 Consumer Cyclical Mean Return: -0.47250000000000003 Mean Day/Week: 3.25 Accuracy:0.5 *******************Part 3.2 Penny Cap Short Entry SPAN MACD********************* penny_short_signal_entry_span_macd Symbol Day Return Market Cap Long/Short score MACD Signal Count Market Value Span Signal Count industry 774 HRTX 4 1.03 penny-cap Short NaN -5.0 1.846091e+07 -5.0 Healthcare Mean Return: 1.03 Mean Day/Week: 4.0 Accuracy:0.0 *******************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 901 PLCE 3 -1.57 penny-cap Long NaN 3.0 9.067042e+07 4.0 Consumer Cyclical 583 VG 1 0.00 penny-cap Long NaN 1.0 3.064799e+07 5.0 Communication Services 740 CRC 2 -0.28 penny-cap Long NaN 9.0 2.232818e+07 3.0 Energy 504 CIG 1 0.00 penny-cap Long NaN 39.0 1.957709e+07 5.0 Utilities 676 EQX 2 -2.38 penny-cap Long NaN 10.0 1.491595e+07 3.0 Basic Materials 718 RNST 4 -2.20 penny-cap Long NaN 11.0 9.429849e+06 5.0 Financial Services 760 MGRC 3 -1.49 penny-cap Long NaN 3.0 9.365924e+06 5.0 Industrials 707 ENLC 4 0.99 penny-cap Long NaN 4.0 8.153300e+06 4.0 Energy 825 PACK 2 0.18 penny-cap Long NaN 2.0 6.927393e+06 3.0 Consumer Cyclical 892 CLNC 5 3.25 penny-cap Long NaN 10.0 2.542622e+06 5.0 Real Estate Mean Return: -0.4375000000000001 Mean Day/Week: 3.375 Accuracy:0.375 *******************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 536 TLS 1 0.00 penny-cap Short NaN -31.0 6.546014e+07 -2.0 Technology 774 HRTX 4 1.03 penny-cap Short NaN -5.0 1.846091e+07 -5.0 Healthcare 886 ZUMZ 1 0.00 penny-cap Short NaN -63.0 6.987356e+06 -2.0 Consumer Cyclical 826 ASTE 2 -0.74 penny-cap Short NaN -27.0 6.406874e+06 -3.0 Industrials 906 ENTA 3 -2.73 penny-cap Short NaN -9.0 3.148316e+06 -3.0 Healthcare Mean Return: -0.8133333333333334 Mean Day/Week: 3.6666666666666665 Accuracy:0.6666666666666666 *******************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 477 AMC 1 0.00 penny-cap Long NaN 4.0 1.118023e+09 7.0 Communication Services 901 PLCE 3 -1.57 penny-cap Long NaN 3.0 9.067042e+07 4.0 Consumer Cyclical 658 GT 4 -4.94 penny-cap Long NaN 4.0 8.687148e+07 78.0 Consumer Cyclical 736 DDS 4 4.81 penny-cap Long NaN 4.0 7.757093e+07 19.0 Consumer Cyclical 756 ABR 1 0.00 penny-cap Long NaN 1.0 3.318079e+07 78.0 Real Estate 669 CIM 2 0.44 penny-cap Long NaN 2.0 3.166517e+07 78.0 Real Estate 583 VG 1 0.00 penny-cap Long NaN 1.0 3.064799e+07 5.0 Communication Services 633 WERN 1 0.00 penny-cap Long NaN 3.0 2.532474e+07 70.0 Industrials 908 MRUS 1 0.00 penny-cap Long NaN 2.0 2.416169e+07 7.0 Healthcare 799 CNR 2 3.98 penny-cap Long NaN 2.0 1.953500e+07 78.0 Industrials 713 PVG 3 -2.57 penny-cap Long NaN 3.0 1.822978e+07 13.0 Basic Materials 721 WAFD 3 -0.68 penny-cap Long NaN 3.0 1.757635e+07 78.0 Financial Services 771 VRTS 2 -2.18 penny-cap Long NaN 2.0 1.375604e+07 78.0 Financial Services 662 PRMW 4 -0.92 penny-cap Long NaN 4.0 1.251691e+07 34.0 Consumer Defensive 865 RAVN 2 -2.55 penny-cap Long NaN 3.0 1.248819e+07 21.0 Industrials 501 UBSI 5 -1.58 penny-cap Long NaN 5.0 1.070473e+07 78.0 Financial Services 661 COKE 4 3.59 penny-cap Long NaN 4.0 1.043693e+07 53.0 Consumer Defensive 680 PLXS 4 0.52 penny-cap Long NaN 4.0 1.002917e+07 78.0 Technology 760 MGRC 3 -1.49 penny-cap Long NaN 3.0 9.365924e+06 5.0 Industrials 772 PIPR 5 0.56 penny-cap Long NaN 5.0 8.804571e+06 73.0 Financial Services 649 EXG 2 -0.20 penny-cap Long NaN 2.0 8.337278e+06 78.0 Financial Services 707 ENLC 4 0.99 penny-cap Long NaN 4.0 8.153300e+06 4.0 Energy 725 WSBC 5 0.30 penny-cap Long NaN 5.0 8.022838e+06 78.0 Financial Services 879 FCF 5 -0.17 penny-cap Long NaN 5.0 7.843558e+06 78.0 Financial Services 825 PACK 2 0.18 penny-cap Long NaN 2.0 6.927393e+06 3.0 Consumer Cyclical 887 AMSF 1 0.00 penny-cap Long NaN 3.0 5.951837e+06 55.0 Financial Services 870 ARKO 1 0.00 penny-cap Long NaN 1.0 3.193988e+06 55.0 Consumer Cyclical 766 TOWN 4 -2.98 penny-cap Long NaN 4.0 3.128268e+06 78.0 Financial Services 910 SRCE 3 -1.20 penny-cap Long NaN 3.0 3.063941e+06 78.0 Financial Services 801 VCTR 2 -1.95 penny-cap Long NaN 2.0 2.674452e+06 53.0 Financial Services 628 TDI 3 0.38 penny-cap Long NaN 3.0 3.478182e+04 33.0 unknown Mean Return: -0.38458333333333344 Mean Day/Week: 3.625 Accuracy:0.4166666666666667 *******************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 586 TRN 2 1.32 penny-cap Short NaN -2.0 3.417176e+07 -44.0 Industrials 692 ACB 5 -0.72 penny-cap Short NaN -5.0 2.996241e+07 -46.0 Healthcare 470 BLI 5 9.37 penny-cap Short NaN -5.0 2.481418e+07 -137.0 Healthcare 774 HRTX 4 1.03 penny-cap Short NaN -5.0 1.846091e+07 -5.0 Healthcare 577 CPA 5 -0.63 penny-cap Short NaN -5.0 1.558868e+07 -7.0 Industrials 731 PRAX 5 6.45 penny-cap Short NaN -5.0 1.028920e+07 -59.0 Healthcare 900 PASG 4 -14.63 penny-cap Short NaN -4.0 9.440463e+06 -137.0 Healthcare 822 AROC 1 0.00 penny-cap Short NaN -1.0 8.596217e+06 -32.0 Energy 524 SAFE 1 0.00 penny-cap Short NaN -1.0 7.867734e+06 -43.0 Real Estate 839 TRIL 4 -0.50 penny-cap Short NaN -4.0 5.831247e+06 -137.0 Healthcare 732 ARCE 4 -3.21 penny-cap Short NaN -4.0 5.366715e+06 -71.0 Consumer Defensive 811 MLAB 1 0.00 penny-cap Short NaN -1.0 4.563488e+06 -63.0 Technology 872 PRPB 2 0.00 penny-cap Short NaN -2.0 4.394846e+06 -55.0 Financial Services 786 ARQT 3 -2.64 penny-cap Short NaN -5.0 4.079752e+06 -7.0 Healthcare 821 WMK 1 0.00 penny-cap Short NaN -1.0 3.925150e+06 -22.0 Consumer Defensive 909 IH 4 4.13 penny-cap Short NaN -4.0 2.948100e+05 -137.0 Consumer Defensive Mean Return: -0.002500000000000021 Mean Day/Week: 4.25 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 735 AR 9 18.73 penny-cap Long NaN 9.0 1.340683e+08 78.0 Energy 665 RRC 6 17.09 penny-cap Long NaN 13.0 1.125149e+08 13.0 Energy 529 HL 9 20.58 penny-cap Long NaN 11.0 1.034077e+08 11.0 Basic Materials 462 EQT 9 8.36 penny-cap Long NaN 15.0 9.506428e+07 18.0 Energy 769 UFS 18 34.50 penny-cap Long NaN 18.0 8.980238e+07 78.0 Basic Materials 449 BPOP 11 4.65 penny-cap Long NaN 11.0 8.153503e+07 78.0 Financial Services 457 AUY 10 3.95 penny-cap Long NaN 48.0 7.828522e+07 25.0 Basic Materials 642 SWN 8 14.13 penny-cap Long NaN 11.0 7.403686e+07 17.0 Energy 640 ELY 7 0.95 penny-cap Long NaN 21.0 7.012965e+07 10.0 Consumer Cyclical 488 XEC 11 1.67 penny-cap Long NaN 11.0 5.746712e+07 78.0 Energy 747 MUR 6 3.76 penny-cap Long NaN 10.0 5.638651e+07 18.0 Energy 564 IGT 7 12.89 penny-cap Long NaN 15.0 5.414954e+07 7.0 Consumer Cyclical 539 SGMS 17 11.01 penny-cap Long NaN 25.0 5.205953e+07 21.0 Consumer Cyclical 726 ASO 10 0.94 penny-cap Long NaN 10.0 5.075573e+07 78.0 Consumer Cyclical 903 SBLK 21 26.09 penny-cap Long NaN 21.0 5.068577e+07 78.0 Industrials 754 VSTO 6 5.65 penny-cap Long NaN 12.0 4.939373e+07 13.0 Consumer Cyclical 741 MTDR 12 7.28 penny-cap Long NaN 12.0 4.479316e+07 78.0 Energy 784 HOME 11 19.02 penny-cap Long NaN 18.0 4.382497e+07 78.0 Consumer Cyclical 584 UNVR 19 16.03 penny-cap Long NaN 38.0 4.102720e+07 53.0 Basic Materials 487 VVV 24 15.60 penny-cap Long NaN 24.0 3.908487e+07 78.0 Energy 491 CC 13 8.26 penny-cap Long NaN 37.0 3.902799e+07 38.0 Basic Materials 637 MED 8 7.78 penny-cap Long NaN 11.0 3.898851e+07 10.0 Consumer Cyclical 514 WCC 16 13.69 penny-cap Long NaN 16.0 3.855705e+07 78.0 Industrials 486 MSM 17 2.41 penny-cap Long NaN 17.0 3.855592e+07 53.0 Industrials 663 MIC 12 3.05 penny-cap Long NaN 12.0 3.579616e+07 31.0 Industrials 574 HRB 8 4.57 penny-cap Long NaN 8.0 3.306192e+07 76.0 Consumer Cyclical 600 COMM 9 -1.60 penny-cap Long NaN 9.0 3.231120e+07 78.0 Technology 703 NAVI 50 28.36 penny-cap Long NaN 50.0 3.109743e+07 78.0 Financial Services 562 AMN 15 11.96 penny-cap Long NaN 21.0 3.040215e+07 21.0 Healthcare 601 CHX 6 2.34 penny-cap Long NaN 11.0 2.925414e+07 21.0 Energy 534 AM 8 6.32 penny-cap Long NaN 11.0 2.893521e+07 21.0 Energy 862 FOE 7 -0.81 penny-cap Long NaN 7.0 2.880831e+07 71.0 Basic Materials 716 FBP 17 3.90 penny-cap Long NaN 17.0 2.832117e+07 78.0 Financial Services 476 JOBS 10 1.68 penny-cap Long NaN 32.0 2.829045e+07 12.0 Industrials 668 CMC 9 -1.82 penny-cap Long NaN 9.0 2.765623e+07 74.0 Basic Materials 660 TEX 12 2.47 penny-cap Long NaN 12.0 2.718345e+07 78.0 Industrials 623 ABCB 6 3.60 penny-cap Long NaN 11.0 2.602290e+07 78.0 Financial Services 446 INGR 7 0.27 penny-cap Long NaN 7.0 2.595932e+07 74.0 Consumer Defensive 877 CYH 11 -4.06 penny-cap Long NaN 11.0 2.507168e+07 14.0 Healthcare 603 SAFM 11 -0.12 penny-cap Long NaN 11.0 2.486484e+07 75.0 Consumer Defensive 749 EPC 10 2.51 penny-cap Long NaN 10.0 2.472644e+07 48.0 Consumer Defensive 654 SUM 9 3.21 penny-cap Long NaN 9.0 2.338073e+07 78.0 Basic Materials 597 PDCO 12 1.01 penny-cap Long NaN 13.0 2.299333e+07 19.0 Healthcare 636 RRR 16 4.25 penny-cap Long NaN 16.0 2.259712e+07 78.0 Consumer Cyclical 717 TDS 8 -0.48 penny-cap Long NaN 8.0 2.240736e+07 53.0 Communication Services 711 SGRY 18 5.25 penny-cap Long NaN 18.0 2.233255e+07 78.0 Healthcare 585 BHF 10 0.72 penny-cap Long NaN 10.0 2.218364e+07 78.0 Financial Services 651 CBT 13 10.10 penny-cap Long NaN 19.0 2.203417e+07 78.0 Basic Materials 469 HLI 6 4.30 penny-cap Long NaN 6.0 2.186596e+07 6.0 Financial Services 624 ARNC 13 18.28 penny-cap Long NaN 27.0 2.154718e+07 26.0 Industrials 677 PDCE 9 0.21 penny-cap Long NaN 9.0 2.137318e+07 78.0 Energy 625 NGVT 6 1.99 penny-cap Long NaN 15.0 2.126734e+07 21.0 Basic Materials 596 GOLF 10 -0.49 penny-cap Long NaN 10.0 2.096968e+07 10.0 Consumer Cyclical 648 BNL 15 3.52 penny-cap Long NaN 34.0 2.012109e+07 25.0 Real Estate 715 OI 19 17.57 penny-cap Long NaN 38.0 1.980046e+07 39.0 Consumer Cyclical 667 MD 9 -2.23 penny-cap Long NaN 9.0 1.959037e+07 34.0 Healthcare 778 PAGP 9 6.96 penny-cap Long NaN 15.0 1.935842e+07 19.0 Energy 556 BCPC 12 -1.29 penny-cap Long NaN 13.0 1.782659e+07 62.0 Basic Materials 701 MLHR 8 -0.02 penny-cap Long NaN 8.0 1.781572e+07 75.0 Consumer Cyclical 773 IRWD 9 9.64 penny-cap Long NaN 20.0 1.687222e+07 28.0 Healthcare 627 MIME 7 2.17 penny-cap Long NaN 26.0 1.560134e+07 9.0 Technology 767 RLGY 8 6.50 penny-cap Long NaN 15.0 1.543612e+07 15.0 Real Estate 742 PRFT 15 8.18 penny-cap Long NaN 15.0 1.513589e+07 78.0 Technology 613 NUS 8 -0.25 penny-cap Long NaN 28.0 1.495165e+07 10.0 Consumer Defensive 843 SAND 7 3.89 penny-cap Long NaN 52.0 1.464557e+07 34.0 Basic Materials 606 HWC 18 7.81 penny-cap Long NaN 18.0 1.380395e+07 78.0 Financial Services 746 CSTM 12 2.94 penny-cap Long NaN 18.0 1.366007e+07 53.0 Basic Materials 492 CEF 6 2.79 penny-cap Long NaN 29.0 1.244355e+07 13.0 unknown 878 RGR 7 3.36 penny-cap Long NaN 8.0 1.198483e+07 10.0 Industrials 646 AUB 8 3.10 penny-cap Long NaN 10.0 1.178656e+07 78.0 Financial Services 776 GOL 9 1.80 penny-cap Long NaN 32.0 1.175397e+07 20.0 Industrials 714 PBH 8 5.72 penny-cap Long NaN 8.0 1.129794e+07 13.0 Healthcare 898 ARCO 15 14.05 penny-cap Long NaN 24.0 1.071124e+07 18.0 Consumer Cyclical 730 CVA 8 -1.85 penny-cap Long NaN 14.0 1.040265e+07 14.0 Industrials 734 OR 13 9.35 penny-cap Long NaN 48.0 1.026385e+07 31.0 Basic Materials 699 BDC 9 -4.36 penny-cap Long NaN 11.0 1.011426e+07 11.0 Industrials 833 XPEL 8 11.37 penny-cap Long NaN 8.0 9.698121e+06 34.0 Consumer Cyclical 673 FCFS 19 10.60 penny-cap Long NaN 26.0 9.411034e+06 54.0 Financial Services 808 HNI 15 6.08 penny-cap Long NaN 15.0 9.381056e+06 57.0 Industrials 670 ANAT 10 22.55 penny-cap Long NaN 10.0 9.326225e+06 55.0 Financial Services 644 EXLS 11 0.72 penny-cap Long NaN 11.0 8.957766e+06 55.0 Technology 655 EVTC 12 5.86 penny-cap Long NaN 34.0 8.785266e+06 34.0 Technology 618 GHC 9 0.03 penny-cap Long NaN 9.0 8.528646e+06 34.0 Consumer Defensive 581 CWK 13 2.07 penny-cap Long NaN 13.0 8.389565e+06 53.0 Real Estate 848 CASH 15 0.98 penny-cap Long NaN 15.0 8.372206e+06 78.0 Financial Services 851 NMRK 9 0.15 penny-cap Long NaN 9.0 7.823055e+06 78.0 Real Estate 688 MTX 10 1.26 penny-cap Long NaN 10.0 7.815158e+06 78.0 Basic Materials 829 HSC 9 0.32 penny-cap Long NaN 26.0 7.557843e+06 12.0 Industrials 641 SCL 16 3.40 penny-cap Long NaN 19.0 7.361472e+06 53.0 Basic Materials 859 VLRS 17 5.53 penny-cap Long NaN 17.0 7.129972e+06 78.0 Industrials 678 ENBL 15 14.57 penny-cap Long NaN 15.0 6.685182e+06 69.0 Energy 856 WIRE 18 11.12 penny-cap Long NaN 18.0 6.375559e+06 78.0 Industrials 617 JJSF 15 2.28 penny-cap Long NaN 20.0 6.187593e+06 24.0 Consumer Defensive 814 ARGO 10 -1.10 penny-cap Long NaN 10.0 5.778640e+06 68.0 Financial Services 871 FDP 11 2.70 penny-cap Long NaN 11.0 5.284675e+06 58.0 Consumer Defensive 889 SBSI 12 2.07 penny-cap Long NaN 12.0 5.075450e+06 78.0 Financial Services 582 CNS 8 2.02 penny-cap Long NaN 34.0 4.330848e+06 19.0 Financial Services 635 USM 7 -0.72 penny-cap Long NaN 7.0 4.205802e+06 53.0 Communication Services 537 TIGO 6 1.31 penny-cap Long NaN 13.0 4.163406e+06 15.0 Communication Services 861 BPMP 11 2.13 penny-cap Long NaN 18.0 2.782356e+06 55.0 Energy 830 FFG 13 0.10 penny-cap Long NaN 13.0 1.203506e+06 13.0 Financial Services 612 BBU 15 6.55 penny-cap Long NaN 17.0 7.532254e+05 18.0 Industrials 456 SHI 8 1.67 penny-cap Long NaN 8.0 4.368088e+05 30.0 Energy Mean Return: 5.834563106796118 Mean Day/Week: 11.495145631067961 Accuracy:0.8543689320388349 *******************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 835 RIOT 8 -17.60 penny-cap Short NaN -41.0 9.936122e+08 -25.0 Technology 525 APHA 12 -5.87 penny-cap Short NaN -55.0 2.918416e+08 -38.0 Healthcare 634 TLRY 13 -9.46 penny-cap Short NaN -60.0 2.311761e+08 -40.0 Healthcare 506 TREE 10 3.36 penny-cap Short NaN -10.0 1.278489e+08 -58.0 Financial Services 490 NOVA 12 -10.63 penny-cap Short NaN -12.0 1.186326e+08 -56.0 Technology 837 SNDL 15 -19.12 penny-cap Short NaN -58.0 1.091198e+08 -39.0 Healthcare 650 TIGR 10 4.30 penny-cap Short NaN -10.0 9.174900e+07 -40.0 Financial Services 576 CSIQ 14 -10.28 penny-cap Short NaN -14.0 8.957781e+07 -55.0 Technology 464 SHAK 14 -24.03 penny-cap Short NaN -60.0 8.819401e+07 -30.0 Consumer Cyclical 507 MGNI 12 -26.48 penny-cap Short NaN -58.0 8.517400e+07 -40.0 Communication Services 666 NNDM 15 -20.06 penny-cap Short NaN -64.0 7.252791e+07 -55.0 Technology 630 EAT 7 -2.35 penny-cap Short NaN -42.0 6.265248e+07 -14.0 Consumer Cyclical 483 EH 15 -24.19 penny-cap Short NaN -64.0 5.631727e+07 -54.0 Industrials 445 ALLO 14 -7.58 penny-cap Short NaN -15.0 5.488743e+07 -16.0 Healthcare 467 EVBG 12 -6.47 penny-cap Short NaN -12.0 5.094342e+07 -53.0 Technology 458 NARI 8 -6.23 penny-cap Short NaN -12.0 4.981656e+07 -12.0 Healthcare 544 AMBA 14 -4.74 penny-cap Short NaN -59.0 4.753009e+07 -41.0 Technology 604 FRHC 7 -0.82 penny-cap Short NaN -28.0 4.463007e+07 -29.0 Financial Services 475 HASI 12 0.61 penny-cap Short NaN -12.0 4.267561e+07 -64.0 Real Estate 643 MAXR 12 1.83 penny-cap Short NaN -12.0 4.143755e+07 -46.0 Technology 523 MRCY 9 -3.62 penny-cap Short NaN -11.0 4.072880e+07 -11.0 Industrials 517 LOPE 8 -2.57 penny-cap Short NaN -36.0 3.975750e+07 -10.0 Consumer Defensive 566 IRBT 15 -15.82 penny-cap Short NaN -60.0 3.876131e+07 -49.0 Technology 588 NSTG 7 -11.27 penny-cap Short NaN -10.0 3.824795e+07 -12.0 Healthcare 541 EGHT 13 -27.45 penny-cap Short NaN -13.0 3.799421e+07 -15.0 Technology 542 VC 6 -3.85 penny-cap Short NaN -6.0 3.680983e+07 -44.0 Consumer Cyclical 509 PD 11 -8.81 penny-cap Short NaN -11.0 3.576023e+07 -55.0 Technology 876 TPGY 9 -19.76 penny-cap Short NaN -9.0 3.546315e+07 -55.0 Financial Services 571 CDLX 10 -5.09 penny-cap Short NaN -10.0 3.502507e+07 -15.0 Communication Services 493 CRNC 10 -6.59 penny-cap Short NaN -10.0 3.357436e+07 -46.0 Technology 682 TPIC 11 -5.78 penny-cap Short NaN -11.0 3.349437e+07 -58.0 Industrials 706 CLNE 15 -35.28 penny-cap Short NaN -60.0 3.347220e+07 -33.0 Energy 758 ACMR 15 -21.01 penny-cap Short NaN -58.0 3.329202e+07 -40.0 Technology 495 STMP 10 -6.21 penny-cap Short NaN -10.0 3.325650e+07 -62.0 Technology 700 REAL 9 -35.13 penny-cap Short NaN -9.0 3.300999e+07 -14.0 Consumer Cyclical 622 JKS 8 4.93 penny-cap Short NaN -8.0 3.190032e+07 -77.0 Technology 560 VCYT 16 -31.13 penny-cap Short NaN -24.0 3.050446e+07 -46.0 Healthcare 573 FLGT 15 -9.04 penny-cap Short NaN -61.0 2.954466e+07 -40.0 Healthcare 832 JRVR 10 -2.51 penny-cap Short NaN -17.0 2.918557e+07 -22.0 Financial Services 499 ENV 9 -1.16 penny-cap Short NaN -10.0 2.707585e+07 -13.0 Technology 629 GDOT 7 -2.77 penny-cap Short NaN -7.0 2.674803e+07 -137.0 Financial Services 455 SAGE 10 -0.40 penny-cap Short NaN -10.0 2.612898e+07 -12.0 Healthcare 521 SRNE 9 -2.64 penny-cap Short NaN -9.0 2.600368e+07 -49.0 Healthcare 705 YEXT 13 -7.23 penny-cap Short NaN -13.0 2.576917e+07 -54.0 Technology 460 ALRM 12 -8.59 penny-cap Short NaN -12.0 2.571355e+07 -16.0 Technology 563 EQC 10 0.27 penny-cap Short NaN -11.0 2.468739e+07 -11.0 Real Estate 516 CCXI 12 -62.82 penny-cap Short NaN -12.0 2.451863e+07 -54.0 Healthcare 548 VLDR 7 -7.58 penny-cap Short NaN -7.0 2.368956e+07 -66.0 Technology 453 SMTC 13 -7.19 penny-cap Short NaN -13.0 2.342259e+07 -17.0 Technology 533 INSM 11 -14.25 penny-cap Short NaN -11.0 2.282273e+07 -47.0 Healthcare 685 EPAY 11 -3.90 penny-cap Short NaN -11.0 2.267438e+07 -12.0 Technology 598 GBT 11 -8.60 penny-cap Short NaN -11.0 2.263090e+07 -59.0 Healthcare 485 BAND 7 -1.43 penny-cap Short NaN -7.0 2.245907e+07 -59.0 Technology 480 ARNA 11 -4.91 penny-cap Short NaN -11.0 2.137380e+07 -44.0 Healthcare 689 QTRX 12 -11.58 penny-cap Short NaN -12.0 2.100463e+07 -52.0 Healthcare 579 MOMO 12 -0.89 penny-cap Short NaN -12.0 2.079245e+07 -42.0 Communication Services 502 MTSI 15 -8.22 penny-cap Short NaN -15.0 2.062906e+07 -17.0 Technology 611 RVMD 16 -4.97 penny-cap Short NaN -40.0 2.053323e+07 -31.0 Healthcare 531 DIOD 10 0.69 penny-cap Short NaN -39.0 2.019812e+07 -15.0 Technology 518 GKOS 8 -2.73 penny-cap Short NaN -9.0 1.985868e+07 -10.0 Healthcare 508 AEIS 8 10.70 penny-cap Short NaN -14.0 1.940346e+07 -12.0 Industrials 593 VSAT 6 -1.31 penny-cap Short NaN -11.0 1.872216e+07 -28.0 Technology 607 ALXO 6 1.80 penny-cap Short NaN -6.0 1.869793e+07 -70.0 Healthcare 527 AHCO 26 -15.69 penny-cap Short NaN -27.0 1.862044e+07 -27.0 Healthcare 554 SBRA 6 2.14 penny-cap Short NaN -13.0 1.859890e+07 -14.0 Real Estate 824 ALX 6 0.18 penny-cap Short NaN -38.0 1.848084e+07 -20.0 Real Estate 452 WK 13 -2.25 penny-cap Short NaN -13.0 1.841238e+07 -44.0 Technology 621 LGND 16 -19.16 penny-cap Short NaN -64.0 1.797582e+07 -41.0 Healthcare 543 NKTR 7 -4.21 penny-cap Short NaN -52.0 1.781100e+07 -38.0 Healthcare 762 CSTL 11 -3.73 penny-cap Short NaN -11.0 1.774539e+07 -53.0 Healthcare 738 MTOR 7 -2.88 penny-cap Short NaN -43.0 1.757557e+07 -35.0 Consumer Cyclical 659 INDB 8 -1.63 penny-cap Short NaN -41.0 1.738126e+07 -25.0 Financial Services 494 DOYU 15 -19.11 penny-cap Short NaN -55.0 1.732995e+07 -41.0 Communication Services 722 LUNG 8 4.51 penny-cap Short NaN -8.0 1.716035e+07 -46.0 Healthcare 465 FTDR 12 1.05 penny-cap Short NaN -20.0 1.712960e+07 -20.0 Consumer Cyclical 652 DCPH 11 -11.10 penny-cap Short NaN -11.0 1.710159e+07 -13.0 Healthcare 614 PHR 12 -8.14 penny-cap Short NaN -12.0 1.704425e+07 -47.0 Healthcare 569 CRSR 6 1.67 penny-cap Short NaN -6.0 1.692441e+07 -59.0 Technology 561 NIU 11 -9.33 penny-cap Short NaN -11.0 1.674345e+07 -41.0 Consumer Cyclical 653 MATX 6 4.15 penny-cap Short NaN -43.0 1.672369e+07 -22.0 Industrials 450 GSHD 7 -11.95 penny-cap Short NaN -7.0 1.661416e+07 -44.0 Financial Services 602 NHI 6 2.05 penny-cap Short NaN -29.0 1.649570e+07 -12.0 Real Estate 698 EAR 10 -25.43 penny-cap Short NaN -10.0 1.627683e+07 -44.0 Healthcare 578 FORM 14 -13.39 penny-cap Short NaN -15.0 1.623681e+07 -15.0 Technology 503 ICUI 7 0.20 penny-cap Short NaN -9.0 1.620571e+07 -9.0 Healthcare 610 FN 6 7.59 penny-cap Short NaN -44.0 1.600520e+07 -14.0 Technology 565 SLQT 6 -3.65 penny-cap Short NaN -22.0 1.503121e+07 -8.0 Financial Services 847 GNOG 12 -9.86 penny-cap Short NaN -12.0 1.484602e+07 -111.0 Consumer Cyclical 759 COHU 7 0.17 penny-cap Short NaN -22.0 1.472238e+07 -15.0 Technology 558 CNNE 7 1.75 penny-cap Short NaN -16.0 1.458106e+07 -12.0 Consumer Cyclical 745 AZRE 14 -12.02 penny-cap Short NaN -14.0 1.443455e+07 -66.0 Utilities 823 SOL 12 -7.55 penny-cap Short NaN -12.0 1.423007e+07 -56.0 Technology 498 RXT 6 6.82 penny-cap Short NaN -19.0 1.413447e+07 -7.0 Technology 684 RMBS 14 -0.97 penny-cap Short NaN -58.0 1.367844e+07 -23.0 Technology 743 PBI 14 6.04 penny-cap Short NaN -14.0 1.325045e+07 -15.0 Industrials 530 PTCT 45 -36.89 penny-cap Short NaN -84.0 1.261257e+07 -70.0 Healthcare 500 KNSL 6 3.48 penny-cap Short NaN -6.0 1.253859e+07 -137.0 Financial Services 520 RSI 15 -12.61 penny-cap Short NaN -42.0 1.242820e+07 -43.0 Consumer Cyclical 816 OCUL 7 -12.08 penny-cap Short NaN -7.0 1.213838e+07 -14.0 Healthcare 551 OPCH 11 -3.92 penny-cap Short NaN -11.0 1.197146e+07 -17.0 Healthcare 482 JAMF 11 -12.01 penny-cap Short NaN -13.0 1.188779e+07 -13.0 Technology 552 CRON 15 -10.55 penny-cap Short NaN -61.0 1.153584e+07 -42.0 Healthcare 690 PUBM 10 -12.34 penny-cap Short NaN -52.0 1.142051e+07 -25.0 Technology 750 KN 7 3.41 penny-cap Short NaN -56.0 1.119341e+07 -12.0 Technology 712 KURA 12 -6.03 penny-cap Short NaN -12.0 1.116790e+07 -137.0 Healthcare 854 MODN 7 -3.04 penny-cap Short NaN -11.0 1.102521e+07 -15.0 Technology 691 SITM 13 -1.23 penny-cap Short NaN -13.0 1.102260e+07 -59.0 Technology 881 CERS 7 3.05 penny-cap Short NaN -7.0 1.092031e+07 -62.0 Healthcare 522 DAO 11 -0.82 penny-cap Short NaN -11.0 1.082630e+07 -44.0 Communication Services 836 CEVA 14 -24.40 penny-cap Short NaN -59.0 1.077419e+07 -41.0 Technology 594 AMRN 32 -32.97 penny-cap Short NaN -62.0 1.066941e+07 -44.0 Healthcare 693 AVNS 14 -9.07 penny-cap Short NaN -14.0 1.056303e+07 -44.0 Healthcare 796 PAYA 23 -15.05 penny-cap Short NaN -44.0 1.052080e+07 -64.0 Technology 657 ESE 6 -3.45 penny-cap Short NaN -27.0 1.017239e+07 -8.0 Technology 671 MXL 6 7.54 penny-cap Short NaN -12.0 1.011821e+07 -14.0 Technology 761 SVC 7 -0.56 penny-cap Short NaN -41.0 1.010654e+07 -14.0 Real Estate 783 LTC 10 -3.95 penny-cap Short NaN -42.0 1.000844e+07 -14.0 Real Estate 802 UPLD 10 -4.29 penny-cap Short NaN -11.0 9.809713e+06 -12.0 Technology 609 ROCK 7 -2.53 penny-cap Short NaN -11.0 9.809496e+06 -11.0 Industrials 855 ICPT 11 -10.15 penny-cap Short NaN -11.0 9.525630e+06 -62.0 Healthcare 615 UNIT 7 1.64 penny-cap Short NaN -7.0 9.464300e+06 -56.0 Real Estate 519 OPK 16 -13.46 penny-cap Short NaN -63.0 9.455573e+06 -50.0 Healthcare 557 SEER 12 -25.53 penny-cap Short NaN -12.0 9.307806e+06 -114.0 Healthcare 842 VERI 13 -17.41 penny-cap Short NaN -13.0 9.254863e+06 -53.0 Technology 817 MRSN 8 -6.25 penny-cap Short NaN -8.0 9.210362e+06 -137.0 Healthcare 459 CVET 6 1.61 penny-cap Short NaN -6.0 9.174508e+06 -46.0 Healthcare 897 DHC 12 -18.93 penny-cap Short NaN -40.0 9.126005e+06 -22.0 Real Estate 605 VNE 16 -3.16 penny-cap Short NaN -16.0 9.022859e+06 -32.0 Consumer Cyclical 572 CORT 12 -3.20 penny-cap Short NaN -12.0 8.973822e+06 -56.0 Healthcare 570 TSEM 14 -5.96 penny-cap Short NaN -23.0 8.787190e+06 -41.0 Technology 752 DEA 7 -0.50 penny-cap Short NaN -7.0 8.780429e+06 -59.0 Real Estate 687 STRA 15 -2.33 penny-cap Short NaN -26.0 8.493493e+06 -23.0 Consumer Defensive 807 MASS 8 -22.95 penny-cap Short NaN -8.0 8.422251e+06 -104.0 Healthcare 532 VICR 8 1.27 penny-cap Short NaN -8.0 8.245206e+06 -44.0 Technology 782 SLP 12 -7.68 penny-cap Short NaN -12.0 8.130391e+06 -46.0 Healthcare 838 KROS 8 -0.59 penny-cap Short NaN -11.0 7.665615e+06 -14.0 Healthcare 739 PRO 13 -13.90 penny-cap Short NaN -13.0 7.637392e+06 -16.0 Technology 553 SPSC 7 -2.00 penny-cap Short NaN -12.0 7.598090e+06 -17.0 Technology 567 RCKT 6 -2.79 penny-cap Short NaN -6.0 7.379445e+06 -44.0 Healthcare 792 CDXS 10 -3.67 penny-cap Short NaN -13.0 7.198499e+06 -15.0 Healthcare 704 ADCT 8 -9.57 penny-cap Short NaN -8.0 7.119651e+06 -137.0 Healthcare 845 PI 8 3.06 penny-cap Short NaN -59.0 7.004393e+06 -38.0 Technology 787 ATSG 10 -0.12 penny-cap Short NaN -28.0 6.995266e+06 -21.0 Industrials 857 ADVM 15 -9.23 penny-cap Short NaN -15.0 6.963703e+06 -56.0 Healthcare 810 VCRA 14 -8.18 penny-cap Short NaN -14.0 6.910644e+06 -48.0 Technology 813 NRIX 9 -5.55 penny-cap Short NaN -9.0 6.840497e+06 -44.0 Healthcare 764 PGEN 11 -0.74 penny-cap Short NaN -11.0 6.466128e+06 -46.0 Healthcare 724 SGMO 11 2.40 penny-cap Short NaN -11.0 6.370636e+06 -69.0 Healthcare 884 PHAT 12 -7.48 penny-cap Short NaN -12.0 6.055290e+06 -37.0 Healthcare 805 IMGN 11 -12.69 penny-cap Short NaN -54.0 5.935239e+06 -37.0 Healthcare 647 XNCR 10 2.49 penny-cap Short NaN -10.0 5.859211e+06 -44.0 Healthcare 675 BTRS 10 -16.39 penny-cap Short NaN -10.0 5.745118e+06 -40.0 Technology 894 WPRT 12 -13.63 penny-cap Short NaN -60.0 5.656907e+06 -41.0 Consumer Cyclical 841 EVER 14 -11.82 penny-cap Short NaN -56.0 5.613051e+06 -48.0 Communication Services 728 SRRK 12 -7.18 penny-cap Short NaN -40.0 5.535194e+06 -40.0 Healthcare 681 TTGT 8 1.37 penny-cap Short NaN -11.0 5.088405e+06 -41.0 Communication Services 753 ADUS 11 2.70 penny-cap Short NaN -12.0 5.038614e+06 -15.0 Healthcare 797 BCAB 8 -9.67 penny-cap Short NaN -37.0 4.829316e+06 -30.0 Healthcare 595 MMYT 11 0.59 penny-cap Short NaN -40.0 4.449856e+06 -31.0 Consumer Cyclical 831 CVGW 6 1.55 penny-cap Short NaN -18.0 4.358518e+06 -14.0 Consumer Defensive 723 STTK 6 -10.78 penny-cap Short NaN -6.0 4.349987e+06 -62.0 Healthcare 907 VITL 10 -3.31 penny-cap Short NaN -10.0 4.255864e+06 -43.0 Consumer Defensive 867 ATRI 16 -6.47 penny-cap Short NaN -16.0 4.172644e+06 -20.0 Healthcare 846 FARO 14 -4.48 penny-cap Short NaN -43.0 4.093550e+06 -15.0 Technology 880 DBD 7 9.78 penny-cap Short NaN -9.0 4.035073e+06 -15.0 Technology 885 MORF 11 -4.57 penny-cap Short NaN -38.0 4.015752e+06 -27.0 Healthcare 905 SRG 12 -6.65 penny-cap Short NaN -42.0 3.999662e+06 -29.0 Real Estate 834 KRYS 8 -1.17 penny-cap Short NaN -12.0 3.923640e+06 -13.0 Healthcare 478 HCM 11 0.55 penny-cap Short NaN -11.0 3.606322e+06 -62.0 Healthcare 780 PNTG 16 -26.50 penny-cap Short NaN -86.0 3.595935e+06 -62.0 Healthcare 875 QNST 12 -9.66 penny-cap Short NaN -12.0 3.568828e+06 -43.0 Communication Services 791 SBTX 17 -18.04 penny-cap Short NaN -40.0 3.498099e+06 -37.0 Healthcare 696 GTH 12 -2.80 penny-cap Short NaN -60.0 3.370430e+06 -41.0 Healthcare 893 AKRO 10 1.96 penny-cap Short NaN -10.0 3.329898e+06 -14.0 Healthcare 683 BCAT 8 0.33 penny-cap Short NaN -8.0 3.313010e+06 -66.0 unknown 888 HQH 6 3.59 penny-cap Short NaN -12.0 3.165891e+06 -11.0 Financial Services 757 SPNS 11 -7.41 penny-cap Short NaN -12.0 3.141866e+06 -12.0 Technology 793 LKFN 7 -1.07 penny-cap Short NaN -42.0 2.948501e+06 -17.0 Financial Services 812 KNSA 12 -11.58 penny-cap Short NaN -45.0 2.875420e+06 -42.0 Healthcare 873 FDMT 10 -19.61 penny-cap Short NaN -10.0 2.314592e+06 -44.0 Healthcare 674 CANG 33 -36.53 penny-cap Short NaN -63.0 2.267193e+06 -48.0 Communication Services 852 CLLS 11 -7.43 penny-cap Short NaN -11.0 2.090823e+06 -65.0 Healthcare 866 LXRX 16 -16.61 penny-cap Short NaN -66.0 1.938241e+06 -41.0 Healthcare 781 SYX 7 -0.17 penny-cap Short NaN -14.0 1.791473e+06 -11.0 Industrials 869 RMAX 22 -1.67 penny-cap Short NaN -44.0 1.696321e+06 -27.0 Real Estate 818 BATRK 7 -1.35 penny-cap Short NaN -43.0 1.527173e+06 -19.0 Communication Services 540 MOR 8 3.48 penny-cap Short NaN -8.0 8.928816e+05 -64.0 Healthcare 815 BATRA 7 -0.51 penny-cap Short NaN -42.0 7.666765e+05 -22.0 Communication Services 874 MESO 17 -2.84 penny-cap Short NaN -17.0 6.602379e+05 -137.0 Healthcare 788 HLG 41 -23.52 penny-cap Short NaN -41.0 1.237100e+05 -137.0 Consumer Defensive 695 GB 10 0.21 penny-cap Short NaN -28.0 1.692824e+04 -66.0 Technology 804 MSC 14 5.42 penny-cap Short NaN -48.0 3.992670e+03 -137.0 Consumer Cyclical Mean Return: -7.1301562500000015 Mean Day/Week: 11.244791666666666 Accuracy:0.7708333333333334 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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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DuPont beats 1Q profit forecasts
WILMINGTON, Del. (AP) _ DuPont Co. (DD) on Tuesday reported first-quarter earnings of $1.11 billion.
DuPont beats 1Q profit forecasts
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Circulating Levels of Insulin-like Growth Factor 1 and Insulin-like Growth Factor Binding Protein 3 Associate With Risk of Colorectal Cancer Based on Serologic and Mendelian Randomization Analyses.
Related Articles
Circulating Levels of Insulin-like Growth Factor 1 and Insulin-like Growth Factor Binding Protein 3 Associate With Risk of Colorectal Cancer Based on Serologic and Mendelian Randomization Analyses.
Gastroenterology. 2019 Dec 26;:
Authors: Murphy N, Carreras-Torres R, Song M, Chan AT, Martin RM, Papadimitriou N, Dimou N, Tsilidis KK, Banbury B, Bradbury KE, Besevic J, Rinaldi S, Riboli E, Cross AJ, Travis RC, Agnoli C, Albanes D, Berndt SI, Bézieau S, Bishop DT, Brenner H, Buchanan DD, Onland-Moret NC, Burnett-Hartman A, Campbell PT, Casey G, Castellví-Bel S, Chang-Claude J, Chirlaque MD, Chapelle A, English D, Figueiredo JC, Gallinger SJ, Giles GG, Gruber SB, Gsur A, Hampe J, Hampel H, Harrison TA, Hoffmeister M, Hsu L, Huang WY, Huyghe JR, Jenkins MA, Keku TO, Kühn T, Kweon SS, Le Marchand L, Li CI, Li L, Lindblom A, Martín V, Milne RL, Moreno V, Newcomb PA, Offit K, Ogino S, Ose J, Perduca V, Phipps AI, Platz EA, Potter JD, Qu C, Rennert G, Sakoda LC, Schafmayer C, Schoen RE, Slattery ML, Tangen CM, Ulrich CM, van Duijnhoven FJ, Van Guelpen B, Visvanathan K, Vodicka P, Vodickova L, Vymetalkova V, Wang H, White E, Wolk A, Woods MO, Wu AH, Zheng W, Peters U, Gunter MJ
Abstract
BACKGROUND AIMS: Human studies examining associations between circulating levels of insulin-like growth factor 1 (IGF1) and insulin-like growth factor binding protein 3 (IGFBP3) and colorectal cancer risk have reported inconsistent results. We conducted complementary serologic and Mendelian randomization (MR) analyses to determine whether alterations in circulating levels of IGF1 or IGFBP3 are associated with colorectal cancer development.
METHODS: Serum levels of IGF1 and other proteins were measured in blood samples collected from 397,380 participants from the UK Biobank, from 2006 through 2010. Incident cancer cases and cancer cases recorded first in death certificates were identified through linkage to national cancer and death registries. Complete follow up was available through March 31, 2016. For the MR analyses, we identified genetic variants associated with circulating levels of IGF1 and IGFBP3. The association of these genetic variants with colorectal cancer was examined with 2-sample MR methods using genome-wide association study consortia data (52,865 cases with colorectal cancer and 46,287 individuals without [controls]) RESULTS: After a median follow-up period of 7.1 years, 2665 cases of colorectal cancer were recorded. In a multivariable-adjusted model, circulating level of IGF1 level associated with colorectal cancer risk (hazard ratio per 1 standard deviation increment of IGF1, 1.11; 95% CI, 1.05-1.17). Similar associations were found by sex, follow-up time, and tumor subsite. In the MR analyses, a 1 standard deviation increment in IGF1 level, predicted based on genetic factors, was associated with a higher risk of colorectal cancer risk (odds ratio, 1.08; 95% CI, 1.03-1.12; P=3.3 x 10-4). Level of IGFBP3, predicted based on genetic factors, was associated with colorectal cancer risk (odds ratio per 1 standard deviation increment, 1.12; 95% CI, 1.06-1.18; P =4.2 x 10-5). Colorectal cancer risk was associated with only 1 variant in IGFBP3 (rs11977526), which also associated with anthropometric traits and circulating level of IGF2.
CONCLUSIONS: In an analysis of blood samples from almost 400,000 participants in the UK Biobank, we found an association between circulating level of IGF1 and colorectal cancer. Using genetic data from 52,865 cases with colorectal cancer and 46,287 controls, a higher level of IGF1, determined by genetic factors, was associated with colorectal cancer. Further studies are needed to determine how this signaling pathway might contribute to colorectal carcinogenesis.
PMID: 31884074 [PubMed - as supplied by publisher] http://dlvr.it/RMCWXK
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Then why did He put the Devil in me…clawing to be let out…if that’s not part of God’s plan?
Matt Murdock, DD 1.11 The Path of the Righteous
Love this moment of questioning, in the context of how unyielding his adherence to his faith normally is.
#netflix daredevil#dd 1.11 the path of the righteous#matt murdock#quotes#faith#doubt#god's plan#the devil#not quite dead blogging
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VA - DANGER ZONE 7: Killer Trucks (2017) (DTN 038) [FREE]
Hardcore | Mainstream | Industrial | Gabber
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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
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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
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