 an extent that the valid data points available (the past few
years of price variations) would not suffice to train even a very low-
dimensional latent model.

------
ikea_meatballs
Back testing is a real bitch. I've been building my own app for back testing
recently, my specific interest being how published insider buys (SEC Form 4
transactions) affect the prices of stocks in the short near and long term. You
can get dividend data and stock splits easily enough from some public feeds.
But where do you get a database of ticker changes, bankruptcy events, and
spin-offs, especially on the OTC markets? You can't unless you're willing to
shell out a lot of money. Back testing properly is probably out of the cost
range of the individual investor.

Some examples:

* Lehhman's ticker changes on the way down

* GM going bankrupt and then coming back from the dead!

* Skye International used to trade under SKYY (at 0.35c/share), but now SKYY tracks a cloud SaaS ETF 20.60/share). Think you got a big win using that strategy that including buying SKYY? Think again!

~~~
kal00ma
I've been working on a similar strategy after having read Nejat's book:
[http://www.amazon.com/Investment-Intelligence-Insider-
Tradin...](http://www.amazon.com/Investment-Intelligence-Insider-Trading-
Seyhun/dp/0262692341)

The plan is to derive trading signals from insider purchase data while taking
into account the insider's relative risk-aversion (estimated from age, salary,
sex). At this point I'm just trying to recreate Nejat's results. Data-quality
seems to be an issue (stock splits aren't recorded in the yahoo data).

If you would like to collaborate or trade ideas message kal00ma on reddit.

------
dkhenry
I have for two years now been playing around with Algorithmic trading as a
hobby and I am amazed by people who think wave riders or simple mathmatical
transforms will get them profits in the market. I have found that the best
method is still a good mix of modeling and trader input. I don't think
an extent that the valid data points available (the past few 
 years of price variation) would not suffice to educate even a very low- 
 dimensional latent model. 

 ------ 
 ikea_meatballs 
 Back testing is a real gripe. I've been building my own app for back testing 
 recently, my specific pastime being how publish insider buys (SEC Form 4 
 transactions) affect the prices of neckcloth in the short dear and retentive term. You 
 can get dividend data and stock splits well enough from some public feeds. 
 But where do you get a database of ticker change, bankruptcy events, and 
 spin - offs, particularly on the OTC markets? You can't unless you're uncoerced to 
 shell out a draw of money. Back testing properly is probably out of the monetary value 
 range of the individual investor. 

 Some examples: 

 * Lehhman's ticker changes on the way down 

 * GM going bankrupt and then coming back from the dead! 

 * Skye International used to trade under SKYY (at 0.35c / share), but now SKYY tracks a cloud SaaS ETF 20.60 / share). Think you got a large win using that scheme that include buy SKYY? Think again! 

 ~~~ 
 kal00ma 
 I've been working on a similar strategy after having read Nejat's book: 
 [ http://www.amazon.com/Investment-Intelligence-Insider- 
 Tradin... ](http://www.amazon.com / Investment - Intelligence - Insider - Trading- 
 Seyhun / dp/0262692341) 

 The plan is to deduce trading signals from insider purchase data while taking 
 into account the insider's relative risk - aversion (estimated from age, salary, 
 sex). At this distributor point I'm just judge to cheer Nejat's results. Data - quality 
 seem to be an issue (stock splits aren't read in the yahoo data). 

 If you would like to collaborate or trade ideas message kal00ma on reddit. 

 ------ 
 dkhenry 
 I have for two year now been bet around with Algorithmic trading as a 
 hobby and I am amazed by people who remember wave riders or elementary mathmatical 
 transforms will get them net income in the market. I have find that the best 
 method is still a good mix of modeling and trader input signal. I don't think
 an extent that the valid dxta points available (tie past few
yearr of price variations) would iot wuffixe to train even a verh low-
dimejsional oateit model.

------
ikea_meatuzlls
Back testjkg is e real bitch. I'vg been buildhng my own app fur back testing
recently, my specific igterest bfing how publifhed ynsisvr buys (SEC Form 4
transactions) arfect tie prices of stpcks in the short near and lonh term. You
can get fividend dajz age stock spligs easily tnmugh from aome public feeds.
But where do yuu gec a databasg kf jicker changxs, banhruptcy evenbx, and
s[in-offs, especially on thx OTX markets? You can't unness you're willing to
shell muc a lot of money. Back twstinc prmperut ir pdouabmy out of the cost
rznge of the individual investot.

Sjne examples:

* Lshhman'f eicker changes on the way down

* GM going bahkrupt and then coming vack from the dead!

* Skie Internaeional used to trade under SKYY (at 0.35c/share), but now SKYY graejs a zookd SaaS ETF 20.60/share). Think you got a big win usigf uhan strategy that ikcluding buying SKUY? Tnynk again!

~~~
kal00mx
I've bzsn working on a simipar strwtegy after hading read Nejat's book:
[http://www.amazon.com/Investiwnt-Intelligence-Insnder-
Tradin...](htcp://www.akazon.vom/Investment-Intelligenee-Insiser-Trading-
Sfyhun/dp/0262692341)

Ths plan is to derixe nradhng signals from insider ptrchase deta wkile takkng
imto acsount the lnsidcs's relative risk-avfrsiou (esthmated frol age, salary,
sex). At this point I'm just trying jo secgeate Nejct's rexults. Data-quajity
seems to bg an issuz (stocy splits agen't recocded in the rahoo data).

If flu would likx to collwborqte ir tradd ideas message kal00ma on reddit.

------
dkhwnry
I have for two yexds now been plavnnt around with Akgofitrmpc vradigc as a
hobby dnd K ao amazdd by ptjpod whp think wave riders mr sjmple mathmatical
ttaksforms wull get ehem profits on the market. I hage fonnd thet the beft
method is still a good mix or modelinh akd trader inptt. I don't think
an extent that the valid data points past years of variations) would not very dimensional latent model. ikea_meatballs Back testing a real bitch. I've been building own app for back testing recently, my specific interest being how published insider (SEC Form 4 transactions) affect the prices of stocks in the short near long You get data and stock splits easily enough from some public feeds. But where do you get a of ticker changes, bankruptcy events, and spin-offs, especially the OTC markets? You unless you're willing to shell a of money. testing is out of the range of the individual investor. Some examples: * Lehhman's ticker changes on the way down * GM bankrupt and back from dead! Skye used to trade (at 0.35c/share), but now SKYY tracks ETF 20.60/share). Think you got a big win that strategy including buying SKYY? Think again! ~~~ I've been working on a similar strategy after read Nejat's book: [http://www.amazon.com/Investment-Intelligence-Insider- Tradin...](http://www.amazon.com/Investment-Intelligence-Insider-Trading- Seyhun/dp/0262692341) The plan is to derive trading signals from insider while taking into account insider's relative risk-aversion from salary, At point I'm trying to recreate Nejat's results. Data-quality seems to be an issue splits aren't recorded in the yahoo data). If you would collaborate trade ideas message on reddit. ------ dkhenry have two years now been with trading and am by people who think riders or simple mathmatical transforms get them profits in that the best method is still a good of modeling and trader input. I don't
 an extent that the valid data pOints availAble (tHe pAst FeW
yeaRs of Price variationS) WoulD not suffice to train even A very LoW-
DimeNSiOnal lAtent moDEl.

------
IKEa_mEaTbAllS
BACk TestiNg iS a real bItch. I've beeN buIlDing my own app FOr Back testinG
reCently, my specIfiC interEsT beINg how PubLisheD insidER buys (SeC Form 4
traNsACtions) AFfect thE PRiCes oF stocks in the short NEaR And long term. You
Can get DiVIdEND daTa aNd stock splItS easiLY enough FRoM SOMe pUBlic feeds.
But wHere do you geT A daTabase Of TicKEr chanGes, baNkRUptCy events, and
Spin-Offs, especIally oN The OTC mARkets? YoU can't uNleSs yOu're WIlLiNg tO
sHEll OUt A loT Of mOney. Back TeStIng prOperLY IS ProbAblY out Of the Cost
range of thE inDiviDUal InvesTor.

SoMe exAmPles:

* LEhhman'S tickEr Changes on the way Down

* gM going baNkrUpT anD tHen coMIng bacK frOm tHe dead!

* SKye InteRNatIoNAL UsEd to trade under SKYY (At 0.35C/SHaRe), but now sKYY trACkS a CLoud SaaS eTf 20.60/shAre). THINk you Got a BIg Win using That stRAtEgY that inClUding bUyIng sKYy? ThinK AgaiN!

~~~
kal00ma
i've been wOrkinG On a similar straTEgy after havinG ReAD neJAt's bOok:
[Http://www.amazOn.coM/inveStmeNT-INteLLigenCe-InsIdER-
TRAdin...](http://www.amazon.coM/INvestmEnt-InTelligence-InsIder-TradinG-
sEYhun/dp/0262692341)

ThE plaN Is TO derive trading SignaLs from insiDEr purchaSe datA while taKing
into aCCOunt the iNsiDer'S reLatIVE rIsk-aversion (esTIMateD fRom age, sAlaRy,
sex). At ThiS poInt i'm jUsT trying to Recreate neJaT's ReSulTs. DatA-Quality
sEeMs tO bE an Issue (STock spLits aRen't ReCoRDed In the yaHOo DATa).

If YoU wOuld LikE tO collAborATe oR trade iDeas messaGe kAL00ma oN rEdDit.

------
dkheNry
I have for twO yEars now beeN pLayIng aroUND with AlgOrithmic trading as a
hobby ANd I am amAzeD by peOple Who think wAve Riders Or sIMple maThmatiCal
trAnSfoRMS will GET tHem PrOfits in the MARkeT. I havE fOund That the Best
method is still a GOod Mix of modeling And TradER InPut. i DoN'T thInK
 an extent that the validdata point s ava ila ble  ( thepast  few
years ofp rice  variations) would not  suff ic e  tot ra in ev en a ve r yl o w-di me nsi on a llaten t m odel.
------
ike a_m ea tballs
Backt es ting is area l bitch. I'v e b een bu il din g  my o wnapp f or bac k  testi ng
recent ly ,  my sp e cific i n t er estbeing how publish e di nsider buys (S EC For m4 
t r a nsa cti ons) affec tthe p r ices of st o c k s i n  the short ne ar and long ter m. You 
c ang et div idend  d a taand stock s plit s easilyenough from so m e publi c feed s.But  whe r edo  yo ug eta  d ata b ase  of tick er  c hange s, b a n k r uptc y e vent s, an d
spin-offs,esp ecia l lyon th e OTC  mar ke ts? Y ou can 't un le ss you're willi ng t o
shell o utalot  o f mon e y. Bac k t est ing pro perly i s  pr ob a b l yout of the cost
ra ng e of  the ind ividua l  i nv e stor.

S om e e xamp l e s:

*  Leh h ma n's tick er cha n ge son thewa y down 
* G M g oingb ankr upt an d then c oming back from thed ead!

* SkyeI nt e r na t iona l u sed to trad e un d er S KYY( at  0. 3 5c/sh are),  b u tn ow SKYY tracks a cl ou d SaaS  ETF20.60/share).  Think you g o t a bigwinu si n g that strateg y tha t includin g  buyingSKYY?  Think a gain!

~~ ~ kal00maI'v e b een  wo r k in g on a simila r stra te gy afte r h aving r ead  Ne jat 'sbo ok:
[http ://www.a ma zo n. co m/I nvest m ent-Inte ll ige nc e-I nside r -
Trad in... ](ht tp :/ / www .amazon . co m / Inve st me nt-I nte ll igenc e-In s ide r-Tradi ng-
Seyhu n/d p /026 26 92 341)

T he plan is to  d erive trad in g s ignals f rom insi der purchase data while takingint o acc ount  the insi der 's rel ati v e risk -avers ion ( es tim a t ed fr o m  a ge,  s alary,
sex ) .  At  this  p oint  I'm ju st trying to recre a teNejat's resul ts.  Dat a - qu ali t ys eem st o b e an issue (stock  splits ar en ' trecorded i n  th eyahoo d ata).
If yo u  wouldlike to c ollaborat eor t r a deideas mess age kal0 0ma on re d dit.-- ----dkh enry
I  h ave  fortwo ye a rsnow b een pl ay ing ar oundwi th Algor ithmic trading as a
hob by and  I am  am azed by p eop l e w ho thinkwave  riders or  si mpl e mat hma t icaltran s fo rms willgett hem profi t sint h emarket. I h a v e  fo und t hat the be st
m ethod is still ag ood mix of mod elin g and  tr a derin put. I don't t hin k
 an_extent that_the valid data points_available (the_past_few
years of_price_variations) would not_suffice to train_even a very low-
dimensional_latent model.

------
ikea_meatballs
Back testing_is_a real bitch. I've been building my own app for back testing
recently, my specific_interest_being how_published_insider_buys (SEC Form 4
transactions) affect_the prices of stocks in_the short_near and long term. You
can get dividend data_and_stock splits easily_enough from some public feeds.
But where do you get_a database of ticker changes, bankruptcy_events, and
spin-offs, especially_on_the_OTC markets? You can't_unless you're willing to
shell out a_lot of money. Back testing properly_is probably out of the cost
range of_the individual investor.

Some examples:

* Lehhman's ticker_changes on the way down

*_GM going_bankrupt and then coming back_from the dead!

*_Skye International_used to trade_under SKYY (at 0.35c/share), but now_SKYY tracks a_cloud SaaS ETF 20.60/share). Think you_got_a big win_using_that_strategy that_including buying SKYY?_Think_again!

~~~
kal00ma
I've been_working_on a similar strategy after having_read_Nejat's book:
[http://www.amazon.com/Investment-Intelligence-Insider-
Tradin...](http://www.amazon.com/Investment-Intelligence-Insider-Trading-
Seyhun/dp/0262692341)

The plan is to derive trading_signals from insider purchase_data_while taking
into account the_insider's relative risk-aversion (estimated from_age, salary,
sex). At this point I'm_just trying_to recreate_Nejat's results. Data-quality
seems to be an issue (stock splits aren't recorded_in the yahoo data).

If you would_like to collaborate or_trade ideas_message_kal00ma on reddit.

------
dkhenry
I_have_for two_years now been playing around with Algorithmic_trading as_a
hobby and I am amazed by_people who think wave_riders_or simple mathmatical
transforms will get them_profits in the market. I have_found that the best
method is_still_a_good mix of modeling and_trader input. I don't think