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    Title $SLS Part 2 and FINAL (Deepest Due Diligence for REGAL Trial) (From a Deep Value Investor) (Predicting BAT mOS from Predictive Model)
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    ID 30625527
    External ID 1r8rb45
    Author Confident-Web-7118
    Published Date Feb. 18, 2026, 11:58 p.m.
    Summary Hey everyone, this is the follow-up (part 2 and final) to my first deep due diligence for REGAL. For anyone new, since this is part 2, I’ve been a deep value investor for several years.  I own 805K sh
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    URL https://www.reddit.com/r/pennystocks/comments/1r8rb45/sls_part_2_and_final_deepest_due_diligence_for/
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    Content ID 37845184
    Key daf16afa-1e0c-4842-9d38-02bc5d7f673e
    Content Hey everyone, this is the follow-up (part 2 and final) to my first deep due diligence for REGAL. For anyone new, since this is part 2, I’ve been a deep value investor for several years.  I own 805K shares here (and am continuously accumulating every week). In Part 1, shared extensive deep due diligence on the cure rate model for the REGAL trial I coded and built.  The reason I continued on from the cure survival model is because the results from the model, and stress test results, allowed me to have the data I need to build a predictive model that predicts with 90% accuracy what BAT mOS in the trial is, given the constraints of 60 Events as of Jan 2025, and 72 Events as of Dec 26, 2025. For context, I have years of experience in machine learning/statistics, and below I do my best to distill the logic of the predictive model and results in as simple of a way as I can. These are the results predicted by the model, and below you'll see exactly how these results are predicted and why they are: 91% accuracy that BAT mOS is 10-14 80% accuracy that it is 10-13 Within that 10-13, 99% accuracy it is 11.4 months for BAT median OS The model is a constrained parametric mixture-cure model and the methodology for predicting the BAT mOS is Bayesian evidence synthesis. Explained simply, there are two parts: At its simplest explanation: we're taking the hard data (72 events out of 126 patients at Month 58) and reverse-engineered the only mathematical curve that fits those constraints without breaking the biological reality. For the Bayesian evidence synthesis, we took a "prior" (the 7 published literature sources that put BAT mOS at 8-10 months) and updated it using the "likelihood" (the hard trial constraints and Monte Carlo simulations) to generate a "posterior" probability distribution (the 11.4-month biological identity point). For the constrained mixture-cure model, we modeled the survival curve by splitting the GPS arm into a "cured" fraction (the plateau) and an "uncured" fraction (the exponential decay), and locked the degrees of freedom using the trial's exact event count. I explain more later on so don't worry The first post clearly showed why there are 99.9999% chances of success for the REGAL trial, and if BAT mOS is under 18 to 20, the trial is successful.  And essentially 16 or below for BAT mOS, makes GPS the groundbreaking standard of care in AML CR2 (not eligible for transplant). But, I was curious to solve for what BAT mOS is in the trial, with a high degree of statistical accuracy of at least 90%+.  I’ve been a deep value investor for years, and have used these skills in business & work for so many years, and I am glad to be able to use them here to solve this and to share with everyone.  I’ll touch on this again at the end of the post, but SLS is the rarest asymmetric opportunity with insane margin of safety that I’ve ever come across in my life thus far. And I wanted to follow-up and do this quickly, since the results of the model, all of the code, parameters, tuning, etc. are all fresh in my brain.   For anyone new here, the pre-read DD resource I would recommend is Part 1 that I posted. Moving on, here is a quick recap.  And prepare yourself for some deep due diligence, it is the only way to go over this properly and to share the model results with you clearly. # Quick recap (for those who missed Part 1) * **REGAL** is a Phase 3 trial in AML (acute myeloid leukemia) patients in second remission. 126 patients, 63 per arm: GPS vaccine vs Best Available Therapy. * **72 of 80 required events** have occurred. 54 patients still “alive” (don’t worry, censoring stress tests have been performed extensively) at month 58. * **Event deceleration signal:** only 12 deaths in 12 months from 66 at risk. The survival curve has flatlined. The only mathematical shape that explains this is a **cure-fraction model** on the GPS arm. * **Original model:** roughly 64% of GPS patients may be functionally cured (under the unconstrained two-constraint fit). Expected topline HR: **0.35-0.50**, with trial threshold at 0.636. **TL;DR (although I recommend reading all of this deep due diligence and everything related to the predicted BAT mOS and stress tests, put a ton of effort into this):** * **I ran 5 independent stress tests** trying to break the REGAL cure-fraction model: censoring bias, BAT long-survivors, vaccine delay, BAT mOS uncertainty, and combined worst case. **Every single one cleared the trial threshold.** * **BAT median OS estimate: 11.4 months.** Five independent evidence streams (literature, biological plausibility, biological identity point, IDMC behavior, Phase 2 consistency) all converge on 10-13 months. 91% of the Bayesian posterior mass sits in the 10-14 month range. * **Expected topline Cox HR: 0.35-0.50.** The model-derived HRs in the tables below are lower (0.13-0.30), but those reflect the cure-fraction plateau distortion. The actual stratified Cox HR in the press release will be higher because it averages across the full curve. Either way, the trial threshold is 0.636 -- not close. * **Posterior-weighted P(trial success) = 99.9%**, integrating over ALL uncertainty in BAT mOS. This is not conditional on any single assumption. * **The only way this fails:** BAT mOS above 20 months (no CR2 AML population has ever achieved this), OR the 60/72 event counts are fabricated, OR survival curves can decelerate without a cure fraction (mathematically impossible). **Important distinction: "Cured" does not mean "alive right now."** The 54 patients still alive at month 58 are a mix of two populations: (1) the **cured plateau** \-- GPS patients the math says will “never relapse” from AML -- and (2) **uncured responders** who are still alive but will eventually decline, plus BAT patients surviving on their own timeline. The cure rate (roughly 64%) refers strictly to GPS patients who have reached the permanent mathematical plateau, not simply everyone who is currently breathing. Some of those 54 alive are uncured GPS patients still at risk. Others are BAT arm patients. The cure fraction is the structural parameter that explains **why the death rate is decelerating** \-- not a head count of survivors. **A note on the Hazard Ratios in this analysis.** Some of the tables below show model-derived Cox HRs as low as 0.13 or 0.20. If your first reaction is "that is impossibly low for an oncology trial," good -- that instinct is correct for a typical drug study. These numbers come from 300 Monte Carlo trial simulations using the cure-fraction parameters. In a cure-fraction setting, the proportional hazards assumption is massively violated: once the cured patients hit the plateau, GPS events stop almost entirely, and nearly all remaining deaths come from the BAT arm. Cox regression is forced to summarize a fundamentally non-proportional situation with a single coefficient, which produces an extremely low number. **The actual trial topline will not report a 0.13 HR.** The press release will use a **stratified log-rank test** and a **stratified Cox model** adjusted for the 4 randomization stratification factors (MRD status, CR1 duration, geographic region, disease status at entry). That stratified Cox HR will also be pulled toward 1.0 by the early period when GPS has not yet fully separated from BAT and by the inherent noise of a 126-patient trial. I expect the reported topline Cox HR to land in the range of **0.35 to 0.50** \-- still a blowout by any oncology standard (the threshold for statistical significance is HR < 0.636, one-sided alpha = 0.025). The model HRs in the tables below are useful for **relative comparisons** between stress tests -- seeing how much each scenario degrades the result -- not as literal predictions of the headline number. # Stress Test #1: What if patients are disappearing? In clinical trials, "censoring" simply means a patient dropped out or was lost to follow-up before the trial ended -- they moved away, chose to stop participating, or the data cutoff arrived before they had an event. "Censoring bias" is the fear that sick patients on the GPS arm are dropping out *because* they are dying, meaning their deaths happen off the books and artificially keep the survival curve looking high. **The concern:** Censoring bias. Some commenters asked: what if patients on the GPS arm are dropping out of the trial because they are sick, and their deaths are not being counted? That would make GPS look better than it really is. The "54 alive" might include people who are actually dead but just stopped being tracked. This is a legitimate concern. In smaller trials, differential dropout can absolutely distort results. **What I did:** I ran 300 Monte Carlo simulations per scenario. I took the model's "alive" GPS patients and forcibly converted a percentage of them into deaths -- as if they had actually died at some random point during their follow-up window. This is the worst-case mode: every single dropout is assumed to be a hidden GPS death. Zero dropout from BAT. I swept this across BAT mOS from 10-18 months and dropout rates from 0-30%. **Selected results:** |**BAT mOS**|**Dropout %**|**Median HR**|**95% CI**|**P(success)**| |:-|:-|:-|:-|:-| |10m|0%|0.129|\[0.07, 0.22\]|100%| |10m|10%|0.165|\[0.10, 0.26\]|100%| |10m|30%|0.233|\[0.15, 0.35\]|100%| |12m|0%|0.204|\[0.11, 0.33\]|100%| |12m|10%|0.250|\[0.14, 0.39\]|100%| |12m|30%|0.339|\[0.22, 0.50\]|100%| |14m|0%|0.294|\[0.16, 0.47\]|100%| |14m|10%|0.346|\[0.21, 0.54\]|99%| |14m|30%|0.455|\[0.31, 0.67\]|96%| |16m|0%|0.393|\[0.23, 0.63\]|98%| |16m|10%|0.451|\[0.28, 0.69\]|92%| |16m|30%|0.578|\[0.39, 0.85\]|71%| |18m|0%|0.498|\[0.30, 0.82\]|84%| |18m|10%|0.570|\[0.35, 0.90\]|71%| |18m|30%|0.711|\[0.48, 1.07\]|26%| https://preview.redd.it/695x5puy4ekg1.png?width=1600&format=png&auto=webp&s=1ce4b6cc4ed0dfcbfaf51b4dee0c7bb54b262292 At realistic BAT values (10-14 months), even 30% worst-case GPS dropout barely dents the result. At BAT=12m with 30% of GPS "alive" patients secretly dead, HR is still 0.34 with P(success) = 100%. The first real threat appears around BAT=16m + 30% worst-GPS dropout: HR 0.58, P(success) 71%. But that requires both an extreme BAT assumption AND an absurd level of one-sided censoring. Neither is likely. Together, the probability is effectively zero. **Bottom line: censoring bias is a non-issue for any realistic scenario.** # Stress Test #2: What if BAT patients are secretly surviving? **The concern:** Even in control arms, some patients survive a long time. AML biology is heterogeneous. Some patients carry favorable mutations (NPM1 without FLT3-ITD, for instance) that give them years of remission even without active therapy. Maybe BAT has its own pool of long-term survivors, and the model is wrong to assume a clean exponential. This is probably the most dangerous critique, because it directly attacks the model's core mechanic. If BAT patients are also surviving long-term, the GPS cured pool shrinks to compensate. **What I tested:** I gave the BAT arm a 20% cure fraction. For context, realistically, based on modern data, Ven+Aza 3 year survival rate in CR2 (not eligible for transplant) is likely only 0% to 5%, but let’s stress test anyway under impossible scenarios.  Continuing on, QUAZAR AML-001 (azacitidine maintenance Phase 3) showed roughly 15-20% of placebo patients alive at 3 years in CR1. In CR2, published rates are more like 5-15%, so 20% is genuinely aggressive. Here is the math: with 20% of BAT patients “immortal”, those patients contribute heavily to the 54 alive at month 58. That means GPS needs fewer long-term survivors to make the total work. The GPS cure fraction drops accordingly -- it is a **survivor budget** problem. |**BAT mOS**|**GPS Cure (Std)**|**GPS Cure (BAT 20%)**|**HR (Std)**|**HR (BAT 20%)**|**P(success)**| |:-|:-|:-|:-|:-|:-| |12m|68%|39%|0.20|0.36|99%| |14m|65%|46%|0.29|0.44|96%| |16m|61%|48%|0.39|0.52|82%| |18m|58%|47%|0.50|0.62|54%| https://preview.redd.it/1x0i7puy4ekg1.png?width=1600&format=png&auto=webp&s=c23845eddbd19df4551b99f7a8c24e4e014e73c9 Yes, the GPS cure fraction drops 10-30 percentage points. That is the math working correctly -- when BAT carries more survivors, GPS needs fewer to hit the same total. But look at the HRs. At BAT=12m: HR goes from 0.20 to 0.36. P(success) = 99%. At BAT=14m: 0.44, P(success) = 96%. **GPS still wins in every realistic scenario.** # Stress Test #3: The vaccine delay problem This one produced the most surprising result. **The concern:** GPS is a vaccine. It does not work instantly. The dosing protocol involves 6 biweekly priming doses over the first 3 months, followed by monthly boosters. During that ramp-up period, GPS patients are essentially unprotected -- they are dying at the same rate as BAT. For the first 3-4 months, HR = 1.0. GPS only starts separating from BAT after the immune response is established. **What I tested:** I forced GPS to follow BAT's survival curve identically for the first 4 months. After month 4, GPS switches to the cure-fraction model. The solver must find a cure fraction that still produces 60 events at month 46 and 72 at month 58. **The surprise:** At BAT = 12 months, there is **no mathematical solution** for a 4-month delay. The solver does not produce a "weak" answer -- it produces **no answer at all**. The equations have no valid solution. Here is why. At BAT = 12m, roughly 24% of GPS patients (15 out of 63) would die during the 4-month delay period, following BAT's exponential survival. That leaves about 48 survivors. To still match the 72 total events at month 58, those 48 survivors would need an impossibly high cure fraction. The math breaks. I tested delay sensitivity at BAT=12m: |**Delay (months)**|**Conditional Cure %**|**Status**| |:-|:-|:-| |0|68%|Clean solution| |1|69%|Clean solution| |2|71%|Clean solution| |3|57%|Solver straining| |4|\--|**NO SOLUTION**| |5|\--|**NO SOLUTION**| |6|\--|**NO SOLUTION**| https://preview.redd.it/yjqeppuy4ekg1.png?width=1600&format=png&auto=webp&s=a1209b14aa2129c02e1b65b51f478df6e13b4a4c **What this tells us:** The data itself constrains the maximum possible delay to about 2-3 months. GPS *must* be working before month 4. If it were not, the observed event pattern would be mathematically impossible. This makes biological sense. These are CR2 patients -- they have already had AML once, been treated, and relapsed. Their immune systems have been exposed to WT1 (the protein GPS targets) for months or years. GPS is not building an immune response from scratch. It is boosting pre-existing memory T cells. When I Googled this/search this, this is what is an **anamnestic recall response** \-- the immunological equivalent of a booster shot. The second dose kicks in fast because the immune system remembers. **The dosing amendment that changed everything (November 2022):** In the middle of REGAL enrollment, SELLAS amended the protocol to **continuous dosing -- treat until relapse.** This is a direct upgrade from Phase 2, where patients stopped receiving GPS after about a year and eventually relapsed. The mathematical plateau (the cure fraction) maps directly to this biological mechanism: continuous boosters maintain immune pressure on residual WT1-expressing leukemic stem cells permanently. Phase 2 patients lost that pressure when dosing stopped. REGAL patients never do. Where the delay DOES solve (BAT >= 13m): |**BAT mOS**|**Standard HR**|**4mo Delay HR**|**P(success)**| |:-|:-|:-|:-| |13m|0.25|0.27|100%| |14m|0.29|0.34|100%| |15m|\--|0.41|98%| |16m|0.39|0.50|87%| |18m|0.50|0.68|35%| |20m|0.61|0.88|6%| At BAT=14m, the 4-month delay shifts HR from 0.29 to 0.34. P(success) = 100%. The delay is ancient history by month 46+. The cure fraction overwhelms it. https://preview.redd.it/48w18quy4ekg1.png?width=1600&format=png&auto=webp&s=438b816af6159c94ff002c8322753261d966e88a Look at the survival curves. By month 18-24, the delayed GPS curve has nearly caught up to the standard GPS curve. The solver compensates by assigning a higher conditional cure fraction among survivors: the vaccine works on fewer patients (those who survived the delay), but it works *better* on them. The net effect on the trial-level HR is minimal. # Tying it together: what the stress tests tell us about BAT median OS These stress tests did not just prove that GPS survives worst-case scenarios. They acted as a **biological filter** that helped calculate exactly what the BAT mOS is. Here is how. The censoring test showed that the result only becomes threatened above BAT = 16 months -- any BAT value below that, even with 30% worst-case GPS dropout, still produces a clear GPS win. The long-survivor test showed that giving BAT a generous 20% cure fraction narrows the GPS cure fraction but does not flip the outcome at any realistic BAT value. And the vaccine delay test proved something critical: a 4-month delay is *mathematically impossible* at BAT values below 13 months. GPS must be activating fast, which is only consistent with moderate BAT values where the early event rate leaves enough surviving patients to produce a valid solution. These three tests systematically eliminated BAT values below 10 months (where the model requires biologically implausible uncured survival -- GPS "failures" living 5-6x longer than BAT patients, I cover this later on here) and above 14 months (where the model requires GPS non-responders to perform *worse* than untreated patients, a biological impossibility for a peptide vaccine). The stress tests forced the true BAT mOS into a highly constrained **10-14 month window** \-- and they did it independently of any literature prior. The published data simply confirmed what the model's own internal consistency already demanded. I stress tested all the way to a 23 BAT mOS (impossibilities), but for almost anyone that does DD for REGAL, the most common pushback on the original post was: "you are assuming BAT mOS = 8 to 10 months." Fair enough -- the trial is blinded. Nobody knows the exact number. So let me walk through how we narrow it down. **The Late Surge Shield.** Enrollment finished at 126 patients in April 2024. About 25 of those patients enrolled between December 2023 and April 2024 -- the "late surge" driven partly by the November 2022 protocol amendment that accelerated site activation. By December 2025, even this newest cohort has 20+ months of follow-up. Historical BAT median survival in CR2 AML is 8-10 months. If the drug were not working, that late cohort would have triggered a wave of BAT-arm deaths through 2025. Instead, only 12 events total across both arms in 12 months. The late enrollees have cleared the danger zone. With that context, here is the formal estimation. I ran a Bayesian-style analysis combining multiple constraints: 1. **Literature prior:** CR2 AML historical data from 7 published sources (Brayer 2015, REGAL FDA design, DiNardo 2020, Breems 2005, QUAZAR AML-001, Gilleece EBMT). Log-normal centered at about 9 months (range: 5.4m pre-venetoclax, 8-10m in the venetoclax era). Weighted center = 8.0 months. 2. **REGAL data constraints:** 60 events at month 46, 72 at month 58 3. **IDMC plausibility:** The arms were visibly separated at the interim analysis (the IDMC said "continue without modification" -- twice) 4. **Biological plausibility:** The required GPS cure fraction should be achievable (roughly 40-70%, consistent with Phase 2 immunologic response rate of 64%) **Results:** |**Metric**|**Value**| |:-|:-| |MAP (mode)|**11 months**| |Mean|**11.4 months**| |Median|**11 months**| |80% Credible Interval|**\[10, 13\] months**| |90% Credible Interval|**\[10, 14\] months**| https://preview.redd.it/ko2inquy4ekg1.png?width=1600&format=png&auto=webp&s=3880d88b18f235665dfe8cfc682f5f60b2ca8cb6 The posterior peaks at **11 months**, consistent with a venetoclax-era CR2 AML control arm. Seven published data sources converge on 8-10 months for CR2 non-transplant patients in the venetoclax era (pre-venetoclax: 5.4m per Brayer 2015, PMID 25802083; Ven-era r/R AML: 7.8m per DiNardo 2020, PMID 32896301; REGAL FDA design: 8.0m). What matters for the investment thesis: **even at the 90th percentile of the posterior (BAT = 14m), the model still shows very high probability of success.** You do not need to know the exact BAT mOS. The margin of safety swallows the uncertainty. Monte Carlo validation of the top candidates: |**BAT mOS**|**Cox HR**|**P(HR < 0.636)**|**P(HR < 0.50)**| |:-|:-|:-|:-| |10m|0.129 \[0.07-0.22\]|100%|100%| |12m|0.204 \[0.11-0.33\]|100%|100%| |14m|0.294 \[0.16-0.47\]|100%|99%| |16m|0.393 \[0.23-0.63\]|98%|85%| **Literature validation of the prior** (7 published data points, fully cited): |**#**|**Source**|**Raw mOS**|**Adjusted for REGAL**|**Weight**| |:-|:-|:-|:-|:-| |1|Brayer 2015 GPS Phase 2 controls (PMID 25802083)|5.4m|8.1m\*|High (21%)| |2|REGAL FDA design assumption (SEC filings)|8.0m|8.0m|Very High (32%)| |3|DiNardo 2020 Ven+Dec r/R AML (PMID 32896301)|7.8m|8.5m|High (21%)| |4|DiNardo 2020 treated secondary AML (same paper)|6.0m|7.0m|Medium (11%)| |5|Breems 2005 AML relapse index (PMID 15632409)|12.0m|7.5m\*\*|Low-Med (5%)| |6|QUAZAR AML-001 placebo arm (Wei, NEJM 2020)|14.8m|8.1m\*\*\*|Medium (11%)| |7|Gilleece EBMT CR2 WITH transplant (PMID 31363160)|42m|Ceiling only|Low| \* Pre-venetoclax 5.4m + venetoclax-era improvement of about 50% \*\* Includes transplant recipients; non-transplant about 60% of reported \*\*\* CR1 to CR2 adjustment (x0.55) All 6 quantitative data points cluster tightly around 7.0-8.5 months after adjustment for era, population (CR2 vs r/R vs CR1), and transplant status. The REGAL FDA design assumption of 8.0m sits at the center. This is not a coincidence -- it is what convergent evidence looks like. # How accurate is this? Methodology & Validation I know people will ask: "How do you know this model is right?" Here is the entire logic chain, from raw data to final confidence number. # The logic chain (start here if you read nothing else) **Step 1 -- Hard data (not assumptions):** * 60 events at month 46 (publicly confirmed) * 72 events at month 58 (publicly confirmed) * 54 patients alive out of 126 (publicly confirmed) * Only 12 new events in 12 months from 66 at-risk patients **Step 2 -- What math fits that data?** An 18% annual death rate from 66 patients at risk. Standard exponential survival would predict about 33%. The curve is decelerating -- patients are dying slower and slower over time. The ONLY mathematical form that produces a decelerating death rate is a **cure-fraction model**: some fraction of GPS patients “never die” of AML while the rest follow exponential decay. (An exponential GPS model would need mOS = 97.6 months -- 8+ years for relapsed AML. Nobody believes that.) **Step 3 -- How constrained is the model?** 3 parameters, 2 hard constraints, 1 degree of freedom (BAT mOS). For ANY BAT mOS you pick, there is exactly ONE (cure\_frac, uncured\_mOS) that fits. The model cannot overfit. It cannot be gamed. **Step 4 -- Does BAT mOS matter for the prediction?** No. I ran 300 Monte Carlo trial simulations at every BAT from 9-20 months. **GPS wins in every single scenario.** Even at BAT = 20m (far beyond any published CR2 AML control), the cure-fraction model predicts GPS outperforms BAT. **Step 5 -- The actual confidence number:** **Posterior-weighted P(trial success) = 99.9%** This integrates P(success | BAT) x P(BAT | data) over the full Bayesian posterior. It accounts for ALL uncertainty in BAT mOS -- every possible value, weighted by how likely it is given 7 published literature sources + biological plausibility constraints. It is not conditional on any single assumption. Now let me show you the detailed analysis behind each step. # The constraint system The cure-fraction model has 3 free parameters (BAT mOS, GPS cure fraction, GPS non-responder uncured mOS). It is locked to 2 hard constraints from REGAL data: 1. **60 events at month 46** (interim analysis, publicly confirmed) 2. **72 events at month 58** (Dec 2025 press release, publicly confirmed) That leaves exactly **1 degree of freedom** \-- the BAT mOS assumption. Once you pick a BAT mOS, the other two parameters are *uniquely determined*, not fitted. The solver finds the one and only (cure\_frac, uncured\_mOS) pair that satisfies both event constraints to machine precision (residual < 10\^-10). This means the model **cannot overfit**. 1 free parameter, 2 hard constraints, 0 wiggle room. # How the cure model constrains BAT mOS (the key insight) Here is what is really important to understand: the cure model's outputs at each BAT assumption are **biologically testable predictions.** For every BAT mOS value, the solver produces a unique cure fraction and uncured mOS (the GPS non-responder uncured mOS). We can ask: are these numbers biologically plausible? **The constraint manifold:** |**BAT mOS**|**Cure %**|**Uncured mOS**|**Ratio (Unc/BAT)**|**Biological Assessment**| |:-|:-|:-|:-|:-| |9m|38%|53.2m|5.91x|IMPLAUSIBLE| |10m|64%|20.0m|2.00x|Unlikely| |11m|68%|13.0m|1.18x|**Plausible**| |12m|68%|9.9m|0.83x|**Plausible**| |13m|67%|8.3m|0.63x|**Plausible**| |14m|65%|7.2m|0.52x|Unlikely| |16m|61%|6.1m|0.38x|IMPLAUSIBLE| |18m|58%|5.6m|0.31x|IMPLAUSIBLE| |20m|54%|5.4m|0.27x|IMPLAUSIBLE| **The ratio column is the key.** GPS is a cancer vaccine. It can help, but it cannot harm. Patients who do not respond to GPS are still receiving standard therapy (BAT). Their survival -- the "uncured mOS" -- should be roughly comparable to BAT patients (ratio of about 0.7-1.5x): * **BAT = 9m, uncured = 53m (5.9x):** GPS "failures" would live 6 times longer than the control arm. This is biologically impossible -- if the vaccine did not cure them, they should not dramatically outperform untreated patients. * **BAT = 10-13m, uncured roughly 10-20m (0.8-2.0x):** Uncured GPS non-responders is roughly equal to BAT. This is exactly what you would expect -- non-responders behave like the control arm, maybe slightly better from supportive care effects. * **BAT = 16-20m, uncured = 5-6m (0.3-0.4x):** GPS non-responders die in 5-6 months while BAT patients survive 16-20 months. The vaccine would be *harming* non-responders. Biologically implausible for a peptide vaccine with minimal toxicity. This biological filter narrows the plausible BAT range to approximately **10-14 months** \-- exactly where the literature says it should be. # Combining all evidence layers and the biological identity point Here is the strongest result: I solved for the exact BAT mOS where the ratio equals 1.0 -- where GPS non-responders perform identically to BAT patients. This is the **biological identity point**: the one BAT value that makes the model's internal predictions maximally self-consistent. **Biological identity point: BAT = 11.4 months.** At this BAT value: * Cure fraction = 68% * Uncured mOS = 11.4m (exactly equals BAT mOS) * GPS overall mOS = NR * **0 degrees of freedom.** The system is fully determined -- no assumptions, no priors, just data + biology. This is what makes the estimate robust: five independent evidence streams all converge on the same answer: 1. **Literature prior (7 published sources):** Weighted center = 8.0m, all cluster at 7-10m adjusted. Points to 9-12m. 2. **Cure model biological plausibility:** Eliminates BAT < 10m (uncured too high) and BAT > 16m (uncured too low). Leaves 10-14m. 3. **Biological identity (unc = BAT):** Exact solution at 11m. Narrows to 10-13m. 4. **IDMC behavior:** Arms visibly separated, substantial death gap between arms. Consistent with 10-14m. 5. **Phase 2 consistency:** Cure fraction 68% at identity point. Matches Phase 2 IR rate of 64% almost exactly. These streams converge independently on **BAT = roughly 10-13 months** (80% CI), with the biological identity point at 11.4m. # Statistical accuracy of the 11.4-month estimate How much should you trust a specific number from a blinded trial model? Here are the quantitative confidence metrics: |**Accuracy Metric**|**Value**|**What It Means**| |:-|:-|:-| |Posterior mass in 10-13m|85%|85% of all Bayesian probability sits in this narrow 3-month window| |Posterior mass in 10-14m|91%|Expanding to the full biologically plausible range covers 91%| |Estimator agreement|within 0.7m|MAP (10.8m), Mean (11.4m), and Median (11.2m) all agree within 0.7 months -- no skew, no outlier pull| |Identity point vs posterior mean|0.0m apart|The biology-derived point estimate and the data-derived posterior mean are nearly identical| |Constraint residual at identity|< 10^(-28)|Machine-precision fit to both observed event counts simultaneously| |Bio score at identity|0.00|Perfect biological plausibility: uncured mOS / BAT mOS = 1.00 exactly| |Leave-one-out stability|0.0m MAP shift|Removing any single literature source does not move the answer| |Prior sensitivity (25 combos)|MAP stays 9-12m|Tested 25 prior center/width combinations; answer is robust to prior choice| |Independent evidence streams|5 of 5 converge|Literature, plausibility filter, identity point, IDMC, Phase 2 -- all agree| The 11.4-month estimate is not fragile. It is overdetermined -- more independent constraints point to it than are mathematically required to identify it. The MAP, Mean, and Median all cluster within 0.7 months of each other. The biological identity point (11.4m) falls between the MAP and the Mean. Five independent evidence streams -- none of which share inputs -- converge on the same 10-13 month range. That is the difference between a fitted parameter and a discovered constant. # Validation results |**Test**|**Result**|**Interpretation**| |:-|:-|:-| |Leave-one-out (LOO)|Removing any single literature source shifts MAP by 0.0m|No single data point drives the result| |Posterior predictive check|Simulated events match observed (ratio: 0.97, 1.03)|Model generates data consistent with reality| |Prior sensitivity (25 combos)|MAP ranges 9-12m across all prior widths/centers tested|Not driven by prior assumptions| |Constraint residuals|< 10^(-10) for all solved BAT values|Machine-precision match to observed data| |Model comparison (exp vs cure)|Exponential GPS implies mOS = 97.6m (absurd)|Cure fraction is structurally necessary| |Degrees of freedom|1 free parameter after 2 hard constraints|Minimal parameters = impossible to overfit| |Biological plausibility filter|Only BAT 10-14m gives unc/BAT ratio 0.5-2.0x|Additional independent constraint on BAT| # Trial outcome robustness -- the table that matters most For EVERY plausible BAT value (9-20m), I solved the constraint system and ran 300 Monte Carlo trial simulations: |**BAT mOS**|**Cure %**|**Uncured mOS**|**Unc/BAT**|**GPS mOS**|**HR**|**95% CI**|**P(success)**| |:-|:-|:-|:-|:-|:-|:-|:-| |9m|38%|53.2m|5.91x|127.1|0.097|\[0.05, 0.16\]|100.0%| |10m|64%|20.0m|2.00x|NR|0.129|\[0.07, 0.22\]|100.0%| |11m|68%|13.0m|1.18x|NR|0.164|\[0.09, 0.27\]|100.0%| |12m|68%|9.9m|0.83x|NR|0.204|\[0.11, 0.33\]|100.0%| |13m|67%|8.3m|0.63x|NR|0.247|\[0.13, 0.40\]|100.0%| |14m|65%|7.2m|0.52x|NR|0.294|\[0.16, 0.47\]|100.0%| |16m|61%|6.1m|0.38x|NR|0.393|\[0.23, 0.63\]|97.7%| |18m|58%|5.6m|0.31x|NR|0.498|\[0.30, 0.82\]|84.3%| |20m|54%|5.4m|0.27x|NR|0.614|\[0.39, 1.00\]|54.7%| https://preview.redd.it/9ov1pruy4ekg1.png?width=1600&format=png&auto=webp&s=ef3d668bda939da160484a81cbc92e2c458b590d **Every single row predicts GPS wins.** The trial outcome prediction does not depend on knowing BAT mOS precisely. Whether BAT is 10 months or 20 months, the cure-fraction model -- constrained by 60 events at month 46 and 72 events at month 58 -- predicts GPS significantly outperforms BAT. # What each stress test proved (connecting it all together) Each stress test above attacked a different assumption. Here is how they feed into the confidence level: |**Stress Test**|**What It Attacked**|**Result**|**What It Proves**| |:-|:-|:-|:-| |Censoring (dropout)|Maybe GPS "alive" patients are secretly dead|GPS wins even with 30% worst-case dropout at BAT=14m|Even massive systematic bias does not change the outcome| |BAT long-survivors|Maybe BAT has its own cure fraction|GPS cure fraction drops but HR still clears at BAT=14m|The survivor budget constrains itself -- you cannot break both arms| |Vaccine delay|Maybe GPS takes 4+ months to work|No solution exists at BAT < 13m; modest HR impact above|The data itself rules out long delays. GPS works fast.| |BAT mOS uncertainty|We do not know the exact BAT value|100% P(success) at BAT 9-14m, 98% at 16m|The conclusion is insensitive to the main unknown| |Combined worst case|Stack ALL hostile assumptions|Needs BAT > 16m + 30% dropout + 20% BAT cure + 4mo delay simultaneously|All 4 must be true AND extreme to threaten the result| # The accuracy claim -- with the math **The number: posterior-weighted P(trial success) = 99.9%** This is not a qualitative judgment -- it is a computed integral. The calculation: P(success) = sum of P(success | BAT=x) x P(BAT=x | data) For each possible BAT mOS, I multiplied the MC-simulated probability of trial success by the Bayesian posterior probability of that BAT value, then summed. This accounts for ALL uncertainty in BAT mOS. The breakdown: * **P(BAT <= 16m) = 99.6%** \-- P(success) >= 98% everywhere in this range * **P(BAT > 16m) = 0.4%** \-- P(success) drops to 84-55%, but this region has near-zero posterior weight * **P(BAT > 20m) = 0.00%** \-- essentially impossible based on all published AML data The result: **99.9% posterior-weighted probability of trial success.** This already incorporates every source of uncertainty the model has: BAT mOS uncertainty, parameter estimation, and Monte Carlo simulation variance. Three levels of accuracy, from most to least precise: 1. **Trial outcome prediction (100% confidence):** Not assuming any single BAT -- this is the marginal probability across the full posterior. GPS wins almost everywhere, and "everywhere" is weighted by how likely each BAT value actually is. 2. **BAT mOS range (>95% confidence: 10-14m):** Five convergent evidence streams -- literature, biological plausibility filter, biological identity point (roughly 11m), IDMC behavior, and Phase 2 consistency -- all converge on the same 10-13m range. 3. **BAT mOS point estimate (best estimate: roughly 11m):** The biological identity point -- where GPS non-responders perform identically to BAT -- gives the most constrained single estimate. 0 degrees of freedom. **What would need to be true for this to be wrong:** * BAT mOS > 23 months (no CR2 AML population has ever achieved this), OR * The 60/72 event counts are fabricated (SEC fraud), OR * Survival curves can decelerate without a cure fraction (mathematically impossible) None of these are plausible. # # The combined worst case I have shown each stress test individually. But what if you stack them? What happens when: * BAT has a 20% cure fraction, AND * 30% of GPS "alive" patients are actually dead, AND * GPS takes 4 full months to start working? At BAT = 16m (the realistic upper bound for this combination), the stacked worst case pushes HR toward **0.65-0.70**, with P(success) dropping to **35-50%**. That sounds bad until you think about what it requires: 1. BAT outperforms every historical CR2 AML control by 100%+ (literature consensus: 8-10m) 2. 30% of GPS patients reported as alive are secretly dead 3. GPS takes 4 full months to activate (but the delay test says this is *mathematically impossible* at BAT < 13m) 4. 20% of BAT patients are naturally cured (2-4x higher than any published CR2 data) The probability of ALL FOUR happening simultaneously is effectively zero. Any ONE of them alone? GPS wins. You need all four stacked AND an extreme BAT assumption to even threaten the result. https://preview.redd.it/9yf1ridz4ekg1.png?width=1600&format=png&auto=webp&s=9eeee19d3ecd56d619bd1948782d76ce814d2895 # Updated margin of safety Here is how I think about this as a deep value investor. The question is not "what is the exact HR?" It is: **how many things need to go simultaneously wrong for this to fail?** Answer: almost all of them. Simultaneously. |**Stress Test**|**HR at BAT=14m**|**P(success)**|**Verdict**| |:-|:-|:-|:-| |Standard model (no stress)|0.29|100%|GPS wins| |\+ 30% censoring (worst-GPS)|0.45|96%|GPS wins| |\+ BAT 20% cure fraction|0.44|96%|GPS wins| |\+ 4-month vaccine delay|0.34|100%|GPS wins| Every single stress test clears the threshold. Not by a hair – by 30-50% margin. The **only** way to get HR above 0.636: push BAT beyond 23 months (no CR2 AML population has ever achieved this), OR stack 3-4 hostile assumptions simultaneously (each of which is individually unlikely and one of which -- the 4-month delay -- is mathematically ruled out at low BAT values). https://preview.redd.it/w1fxhyuy4ekg1.png?width=1600&format=png&auto=webp&s=9a536aff432fe8ff378b5fd0056d43c0f9fcb086 # What I learned from breaking stuff I went into this stress testing expecting to find a weakness. Something the original model was hiding. Some scenario where the thesis falls apart. I did not find one. What I found instead: * The censoring concern is real in theory but irrelevant in practice. You would need absurd levels of differential GPS-only dropout to matter. * BAT long-survivors are the most credible threat -- but even giving BAT a generous 20% cure fraction, GPS maintains a wide HR margin. The cure fraction drops, but the hazard ratio still clears. * The 4-month delay constraint is actually *evidence for* the model, not against it. The fact that a 4-month delay cannot solve at low BAT values means GPS must be working fast. The biology supports this -- it is an anamnestic recall response, not de novo priming. And the November 2022 continuous dosing amendment means REGAL patients maintain that immune pressure indefinitely, unlike Phase 2 where dosing stopped after a year. * The BAT mOS posterior is wider than I expected (\[10, 14\]m at 90% CI), but the thesis is robust across the entire range. * MRD stratification feeds directly into the models I already ran. It does not introduce a new failure mode -- it creates the bimodal BAT population that the long-survivor test already covers. And because MRD is a stratification factor, the arms are definitionally balanced. No luck-of-the-draw confounding. I’ll leave you with one of my recent thoughts (yG19 from ST) that is suitable for wrapping up, that really provide context on how rare of opportunity this has been/is and how lucky we are to be accumulating here: “If the warrants situation and unfavorable financing terms never happened, none of us would be here. We wouldn't have been able to accumulate/continue to accumulate at these prices. We are all so lucky. Without the warrants situation that caused this, there would be near zero chances that SLS would be trading at current prices, it would be significantly higher, reducing our multi-bagger compounded returns that we'll get from REGAL final analysis readout and buyout. Everyday it is mind-blowing to me that we have an opportunity to continue to accumulate at these prices when REGAL has 99.9999% chances of success and it will be the new standard of care in AML CR2 (not eligible for transplant). A monopoly for 5 to 8 years. The 7.5X to 49X upside from current shares is real. GPS low/conservative annual sales globally will be $4B+ from AML CR2 and CR1 (not eligible for transplant) This is the rarest of opportunities and there is a significant margin of safety. As a smart investor, you need to go heavy." Please post thoughts/questions/comments below and I’ll answer as I get a chance. Looking forward to thoughtful discussions here.
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© 2025 - PennyLoupe LLC. Austin, TX.

DJDT

Versions

Package Name Version
Django 5.2.1
Python 3.11.8
coverage Coverage 7.4.4
debug_toolbar Debug Toolbar 4.3.0
django_extensions Django Extensions 5.2.1
grappelli Grappelli 4.0.2

Time

Resource usage

Resource Value
User CPU time 60.884 msec
System CPU time 0.016 msec
Total CPU time 60.900 msec
Elapsed time 103.020 msec
Context switches 20 voluntary, 1 involuntary

Browser timing

Timing attribute Timeline Milliseconds since navigation start (+length)

Settings from app.settings

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BASE_DIR PosixPath('/mnt/disks/pennyloupe_disk/pennyloupe')
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INSTALLED_APPS ['coverage', 'grappelli', 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.humanize', 'django.contrib.messages', 'django.contrib.postgres', 'django.contrib.sessions', 'django.contrib.staticfiles', 'django_htmx', 'app.core.apps.CoreConfig', 'django_extensions', 'debug_toolbar']
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STATIC_URL '/static/'
STORAGES {'default': {'BACKEND': 'django.core.files.storage.FileSystemStorage'}, 'staticfiles': {'BACKEND': 'django.contrib.staticfiles.storage.StaticFilesStorage'}}
TEMPLATES [{'APP_DIRS': True, 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': ['templates'], 'OPTIONS': {'context_processors': ['django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages']}}]
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WSGI_APPLICATION 'app.wsgi.application'
X_FRAME_OPTIONS 'DENY'
YEAR_MONTH_FORMAT 'F Y'

Headers

Request headers

Key Value
Accept */*
Accept-Encoding gzip, br, zstd, deflate
Host pennyloupe.com
User-Agent Mozilla/5.0 AppleWebKit/537.36 (KHTML, like Gecko; compatible; ClaudeBot/1.0; +claudebot@anthropic.com)

Response headers

Key Value
Content-Type text/html; charset=utf-8

WSGI environ

Since the WSGI environ inherits the environment of the server, only a significant subset is shown below.

Key Value
CONTENT_LENGTH
CONTENT_TYPE
PATH_INFO /ticker/SLS/content/30625527
QUERY_STRING
REMOTE_ADDR 216.73.216.24
REQUEST_METHOD GET
SCRIPT_NAME
SERVER_NAME pennyloupe.com
SERVER_PORT 443
SERVER_PROTOCOL HTTP/1.1

Request

View information

View function Arguments Keyword arguments URL name
app.core.views.app.ticker_content_detail () {'content_id': 30625527, 'ticker': 'SLS'} ticker_content_detail

No cookies

No session data

No GET data

No POST data

SQL queries from 1 connection

  • default 32.33 ms (6 queries )
Query Timeline Time (ms) Action
SELECT "pl_content"."created_dt",
       "pl_content"."updated_dt",
       "pl_content"."status_id",
       "pl_content"."id",
       "pl_content"."key",
       "pl_content"."fetch_ref_id",
       "pl_content"."ticker",
       "pl_content"."publish_dt",
       "pl_content"."external_id",
       "pl_content"."published_by",
       "pl_content"."title",
       "pl_content"."summary",
       "pl_content"."tags",
       "pl_content"."url",
       "pl_content"."author",
       "pl_content"."author_reputation_score",
       "pl_content"."content_length",
       "pl_content"."meta_json",
       "pl_content"."parent_key",
       "pl_content"."sentiment_positive",
       "pl_content"."sentiment_negative",
       "pl_content"."sentiment_neutral",
       "pl_content"."sentiment",
       "pl_content"."sentiment_status",
       "pl_content"."relevance_score",
       "pl_content"."votes_positive",
       "pl_content"."votes_negative",
       "pl_content"."is_processing_flag",
       "pl_content"."vector_embedding_dt",
       "pl_content"."vector_id",
       "pl_content"."vector_status",
       "pl_content"."data_source_id",
       "pl_content"."parent_content_id",
       "pl_content"."publisher_id"
  FROM "pl_content"
 WHERE "pl_content"."id" = 30625527
 LIMIT 21SELECT ••• FROM "pl_content" WHERE "pl_content"."id" = 30625527 LIMIT 21
 3.76

Connection: default

Transaction status: Idle

/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/whitenoise/middleware.py in __call__(124)
  return self.get_response(request)

/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/django_htmx/middleware.py in __call__(43)
  return self.get_response(request)

/mnt/disks/pennyloupe_disk/pennyloupe/app/core/views/app.py in ticker_content_detail(2348)
  content = Content.objects.get(id=content_id)
SELECT "pl_content_content"."created_dt",
       "pl_content_content"."updated_dt",
       "pl_content_content"."status_id",
       "pl_content_content"."id",
       "pl_content_content"."key",
       "pl_content_content"."content",
       "pl_content_content"."content_ref_url",
       "pl_content_content"."content_id"
  FROM "pl_content_content"
 WHERE "pl_content_content"."content_id" = 30625527
 ORDER BY "pl_content_content"."id" ASC
 LIMIT 1SELECT ••• FROM "pl_content_content" WHERE "pl_content_content"."content_id" = 30625527 ORDER BY "pl_content_content"."id" ASC LIMIT 1
 9.63

Connection: default

Transaction status: Idle

/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/whitenoise/middleware.py in __call__(124)
  return self.get_response(request)

/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/django_htmx/middleware.py in __call__(43)
  return self.get_response(request)

/mnt/disks/pennyloupe_disk/pennyloupe/app/core/views/app.py in ticker_content_detail(2354)
  content_content = ContentContent.objects.filter(details=content).first()
SELECT "pl_content_publisher"."created_dt",
       "pl_content_publisher"."updated_dt",
       "pl_content_publisher"."status_id",
       "pl_content_publisher"."id",
       "pl_content_publisher"."name",
       "pl_content_publisher"."url",
       "pl_content_publisher"."domain",
       "pl_content_publisher"."description",
       "pl_content_publisher"."weight",
       "pl_content_publisher"."trust_flow_score",
       "pl_content_publisher"."citation_flow_score"
  FROM "pl_content_publisher"
 WHERE "pl_content_publisher"."id" = 818
 LIMIT 21SELECT ••• FROM "pl_content_publisher" WHERE "pl_content_publisher"."id" = 818 LIMIT 21
 1.23

Connection: default

Transaction status: Idle

/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/whitenoise/middleware.py in __call__(124)
  return self.get_response(request)

/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/django_htmx/middleware.py in __call__(43)
  return self.get_response(request)

/mnt/disks/pennyloupe_disk/pennyloupe/app/core/views/app.py in ticker_content_detail(2357)
  publisher = content.publisher
SELECT "pl_data_source"."created_dt",
       "pl_data_source"."updated_dt",
       "pl_data_source"."status_id",
       "pl_data_source"."id",
       "pl_data_source"."name",
       "pl_data_source"."type",
       "pl_data_source"."url",
       "pl_data_source"."description",
       "pl_data_source"."weight",
       "pl_data_source"."class_name",
       "pl_data_source"."class_params_json"
  FROM "pl_data_source"
 WHERE "pl_data_source"."id" = 7
 LIMIT 21SELECT ••• FROM "pl_data_source" WHERE "pl_data_source"."id" = 7 LIMIT 21
 0.96

Connection: default

Transaction status: Idle

/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/whitenoise/middleware.py in __call__(124)
  return self.get_response(request)

/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/django_htmx/middleware.py in __call__(43)
  return self.get_response(request)

/mnt/disks/pennyloupe_disk/pennyloupe/app/core/views/app.py in ticker_content_detail(2358)
  data_source = content.data_source
SELECT COUNT(*) AS "__count"
  FROM "pl_content"
 WHERE "pl_content"."parent_content_id" = 30625527SELECT COUNT(*) AS "__count" FROM "pl_content" WHERE "pl_content"."parent_content_id" = 30625527
 14.41

Connection: default

Transaction status: Idle

/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/whitenoise/middleware.py in __call__(124)
  return self.get_response(request)

/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/django_htmx/middleware.py in __call__(43)
  return self.get_response(request)

/mnt/disks/pennyloupe_disk/pennyloupe/app/core/views/app.py in ticker_content_detail(2370)
  return render(request, "ticker_content_detail.html", context)

/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/django/shortcuts.py in render(25)
  content = loader.render_to_string(template_name, context, request, using=using)

/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/django/template/loader.py in render_to_string(62)
  return template.render(context, request)

/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/django/template/backends/django.py in render(107)
  return self.template.render(context)

/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/django/template/base.py in render(171)
  return self._render(context)

/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/django/test/utils.py in instrumented_test_render(114)
  return self.nodelist.render(context)

/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/django/template/base.py in render(1016)
  return SafeString("".join([node.render_annotated(context) for node in self]))

/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/django/template/base.py in <listcomp>(1016)
  return SafeString("".join([node.render_annotated(context) for node in self]))

/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/django/template/base.py in render_annotated(977)
  return self.render(context)

/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/django/template/loader_tags.py in render(159)
  return compiled_parent._render(context)

/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/django/test/utils.py in instrumented_test_render(114)
  return self.nodelist.render(context)

/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/django/template/base.py in render(1016)
  return SafeString("".join([node.render_annotated(context) for node in self]))

/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/django/template/base.py in <listcomp>(1016)
  return SafeString("".join([node.render_annotated(context) for node in self]))

/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/django/template/base.py in render_annotated(977)
  return self.render(context)

/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/django/template/loader_tags.py in render(65)
  result = block.nodelist.render(context)

/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/django/template/base.py in render(1016)
  return SafeString("".join([node.render_annotated(context) for node in self]))

/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/django/template/base.py in <listcomp>(1016)
  return SafeString("".join([node.render_annotated(context) for node in self]))

/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/django/template/base.py in render_annotated(977)
  return self.render(context)

/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/django/template/defaulttags.py in render(320)
  match = condition.eval(context)

/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/django/template/smartif.py in eval(61)
  return func(context, self.first, self.second)

/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/django/template/smartif.py in <lambda>(107)
  ">": infix(10, lambda context, x, y: x.eval(context) > y.eval(context)),

/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/django/template/defaulttags.py in eval(886)
  return self.value.resolve(context, ignore_failures=True)

/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/django/template/base.py in resolve(722)
  obj = self.var.resolve(context)

/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/django/template/base.py in resolve(854)
  value = self._resolve_lookup(context)

/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/django/template/base.py in _resolve_lookup(925)
  current = current()
27 <td><a href="{% url 'ticker_content_detail' ticker=ticker content_id=content_detail.content.parent.id %}">{{ content_detail.content.parent.title }}</a></td>
28 </tr>
29 {% endif %}
30 {% if content_detail.content.children.count > 0 %}
31 <tr>
32 <th>Children</th>
33 <td>
34 <ul>

/mnt/disks/pennyloupe_disk/pennyloupe/app/core/templates/ticker_content_detail.html
SELECT "pl_content"."created_dt",
       "pl_content"."updated_dt",
       "pl_content"."status_id",
       "pl_content"."id",
       "pl_content"."key",
       "pl_content"."fetch_ref_id",
       "pl_content"."ticker",
       "pl_content"."publish_dt",
       "pl_content"."external_id",
       "pl_content"."published_by",
       "pl_content"."title",
       "pl_content"."summary",
       "pl_content"."tags",
       "pl_content"."url",
       "pl_content"."author",
       "pl_content"."author_reputation_score",
       "pl_content"."content_length",
       "pl_content"."meta_json",
       "pl_content"."parent_key",
       "pl_content"."sentiment_positive",
       "pl_content"."sentiment_negative",
       "pl_content"."sentiment_neutral",
       "pl_content"."sentiment",
       "pl_content"."sentiment_status",
       "pl_content"."relevance_score",
       "pl_content"."votes_positive",
       "pl_content"."votes_negative",
       "pl_content"."is_processing_flag",
       "pl_content"."vector_embedding_dt",
       "pl_content"."vector_id",
       "pl_content"."vector_status",
       "pl_content"."data_source_id",
       "pl_content"."parent_content_id",
       "pl_content"."publisher_id"
  FROM "pl_content"
 WHERE "pl_content"."parent_content_id" = 30625527SELECT ••• FROM "pl_content" WHERE "pl_content"."parent_content_id" = 30625527
 2.34

Connection: default

Transaction status: Idle

/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/whitenoise/middleware.py in __call__(124)
  return self.get_response(request)

/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/django_htmx/middleware.py in __call__(43)
  return self.get_response(request)

/mnt/disks/pennyloupe_disk/pennyloupe/app/core/views/app.py in ticker_content_detail(2370)
  return render(request, "ticker_content_detail.html", context)

/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/django/shortcuts.py in render(25)
  content = loader.render_to_string(template_name, context, request, using=using)

/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/django/template/loader.py in render_to_string(62)
  return template.render(context, request)

/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/django/template/backends/django.py in render(107)
  return self.template.render(context)

/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/django/template/base.py in render(171)
  return self._render(context)

/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/django/test/utils.py in instrumented_test_render(114)
  return self.nodelist.render(context)

/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/django/template/base.py in render(1016)
  return SafeString("".join([node.render_annotated(context) for node in self]))

/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/django/template/base.py in <listcomp>(1016)
  return SafeString("".join([node.render_annotated(context) for node in self]))

/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/django/template/base.py in render_annotated(977)
  return self.render(context)

/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/django/template/loader_tags.py in render(159)
  return compiled_parent._render(context)

/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/django/test/utils.py in instrumented_test_render(114)
  return self.nodelist.render(context)

/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/django/template/base.py in render(1016)
  return SafeString("".join([node.render_annotated(context) for node in self]))

/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/django/template/base.py in <listcomp>(1016)
  return SafeString("".join([node.render_annotated(context) for node in self]))

/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/django/template/base.py in render_annotated(977)
  return self.render(context)

/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/django/template/loader_tags.py in render(65)
  result = block.nodelist.render(context)

/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/django/template/base.py in render(1016)
  return SafeString("".join([node.render_annotated(context) for node in self]))

/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/django/template/base.py in <listcomp>(1016)
  return SafeString("".join([node.render_annotated(context) for node in self]))

/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/django/template/base.py in render_annotated(977)
  return self.render(context)

/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/django/template/defaulttags.py in render(327)
  return nodelist.render(context)

/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/django/template/base.py in render(1016)
  return SafeString("".join([node.render_annotated(context) for node in self]))

/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/django/template/base.py in <listcomp>(1016)
  return SafeString("".join([node.render_annotated(context) for node in self]))

/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/django/template/base.py in render_annotated(977)
  return self.render(context)

/mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/django/template/defaulttags.py in render(199)
  len_values = len(values)
32 <th>Children</th>
33 <td>
34 <ul>
35 {% for child in content_detail.content.children.all %}
36 <li><a href="{% url 'ticker_content_detail' ticker=ticker content_id=child.id %}">{{ child.title }}</a></li>
37 {% endfor %}
38 </ul>
39 </td>

/mnt/disks/pennyloupe_disk/pennyloupe/app/core/templates/ticker_content_detail.html

Static files (720 found, 2 used)

Static file paths

None

Static file apps

  1. grappelli
  2. django.contrib.admin
  3. django_htmx
  4. app.core
  5. django_extensions
  6. debug_toolbar

Static files

css/pennyloupe.css
/mnt/disks/pennyloupe_disk/pennyloupe/app/core/static/css/pennyloupe.css
img/penny_loupe_logo.png
/mnt/disks/pennyloupe_disk/pennyloupe/app/core/static/img/penny_loupe_logo.png

django.contrib.staticfiles.finders.AppDirectoriesFinder (720 files)

Path Location
.DS_Store /mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/grappelli/static/.DS_Store
grappelli/.DS_Store /mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/grappelli/static/grappelli/.DS_Store
grappelli/js/grappelli.js /mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/grappelli/static/grappelli/js/grappelli.js
grappelli/js/jquery.grp_related_m2m.js /mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/grappelli/static/grappelli/js/jquery.grp_related_m2m.js
grappelli/js/jquery.grp_autocomplete_fk.js /mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/grappelli/static/grappelli/js/jquery.grp_autocomplete_fk.js
grappelli/js/jquery.grp_autocomplete_generic.js /mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/grappelli/static/grappelli/js/jquery.grp_autocomplete_generic.js
grappelli/js/jquery.grp_timepicker.js /mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/grappelli/static/grappelli/js/jquery.grp_timepicker.js
grappelli/js/jquery.grp_collapsible_group.js /mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/grappelli/static/grappelli/js/jquery.grp_collapsible_group.js
grappelli/js/jquery.grp_related_generic.js /mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/grappelli/static/grappelli/js/jquery.grp_related_generic.js
grappelli/js/jquery.grp_related_fk.js /mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/grappelli/static/grappelli/js/jquery.grp_related_fk.js
grappelli/js/jquery.grp_collapsible.js /mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/grappelli/static/grappelli/js/jquery.grp_collapsible.js
grappelli/js/grappelli.min.js /mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/grappelli/static/grappelli/js/grappelli.min.js
grappelli/js/jquery.grp_autocomplete_m2m.js /mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/grappelli/static/grappelli/js/jquery.grp_autocomplete_m2m.js
grappelli/js/jquery.grp_inline.js /mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/grappelli/static/grappelli/js/jquery.grp_inline.js
grappelli/images/spritesheet-1694777276.png /mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/grappelli/static/grappelli/images/spritesheet-1694777276.png
grappelli/images/backgrounds/loading-small.gif /mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/grappelli/static/grappelli/images/backgrounds/loading-small.gif
grappelli/images/backgrounds/changelist-results.png /mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/grappelli/static/grappelli/images/backgrounds/changelist-results.png
grappelli/images/backgrounds/ui-sortable-placeholder.png /mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/grappelli/static/grappelli/images/backgrounds/ui-sortable-placeholder.png
grappelli/images/backgrounds/nav-grabber.gif /mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/grappelli/static/grappelli/images/backgrounds/nav-grabber.gif
grappelli/images/backgrounds/messagelist.png /mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/grappelli/static/grappelli/images/backgrounds/messagelist.png
grappelli/images/icons/ui-datepicker-next.png /mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/grappelli/static/grappelli/images/icons/ui-datepicker-next.png
grappelli/images/icons/link-external-small_hover.png /mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/grappelli/static/grappelli/images/icons/link-external-small_hover.png
grappelli/images/icons/tools-arrow-up-handler_hover.png /mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/grappelli/static/grappelli/images/icons/tools-arrow-up-handler_hover.png
grappelli/images/icons/date-hierarchy-back.png /mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/grappelli/static/grappelli/images/icons/date-hierarchy-back.png
grappelli/images/icons/tools-delete-handler-predelete.png /mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/grappelli/static/grappelli/images/icons/tools-delete-handler-predelete.png
grappelli/images/icons/breadcrumbs-rtl.png /mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/grappelli/static/grappelli/images/icons/breadcrumbs-rtl.png
grappelli/images/icons/datepicker_hover.png /mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/grappelli/static/grappelli/images/icons/datepicker_hover.png
grappelli/images/icons/tools-trash-handler_hover.png /mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/grappelli/static/grappelli/images/icons/tools-trash-handler_hover.png
grappelli/images/icons/selector-add-m2m-vertical_hover.png /mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/grappelli/static/grappelli/images/icons/selector-add-m2m-vertical_hover.png
grappelli/images/icons/tools-related-remove-handler-small_hover.png /mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/grappelli/static/grappelli/images/icons/tools-related-remove-handler-small_hover.png
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grappelli/images/icons/breadcrumbs_hover.png /mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/grappelli/static/grappelli/images/icons/breadcrumbs_hover.png
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admin/css/unusable_password_field.css /mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/django/contrib/admin/static/admin/css/unusable_password_field.css
admin/css/base.css /mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/django/contrib/admin/static/admin/css/base.css
admin/css/dark_mode.css /mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/django/contrib/admin/static/admin/css/dark_mode.css
admin/css/login.css /mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/django/contrib/admin/static/admin/css/login.css
admin/css/responsive_rtl.css /mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/django/contrib/admin/static/admin/css/responsive_rtl.css
admin/css/responsive.css /mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/django/contrib/admin/static/admin/css/responsive.css
admin/css/rtl.css /mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/django/contrib/admin/static/admin/css/rtl.css
admin/css/widgets.css /mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/django/contrib/admin/static/admin/css/widgets.css
admin/css/autocomplete.css /mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/django/contrib/admin/static/admin/css/autocomplete.css
admin/css/dashboard.css /mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/django/contrib/admin/static/admin/css/dashboard.css
admin/css/nav_sidebar.css /mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/django/contrib/admin/static/admin/css/nav_sidebar.css
admin/css/changelists.css /mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/django/contrib/admin/static/admin/css/changelists.css
admin/css/forms.css /mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/django/contrib/admin/static/admin/css/forms.css
admin/css/vendor/select2/LICENSE-SELECT2.md /mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/django/contrib/admin/static/admin/css/vendor/select2/LICENSE-SELECT2.md
admin/css/vendor/select2/select2.css /mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/django/contrib/admin/static/admin/css/vendor/select2/select2.css
admin/css/vendor/select2/select2.min.css /mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/django/contrib/admin/static/admin/css/vendor/select2/select2.min.css
django_htmx/htmx.min.js /mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/django_htmx/static/django_htmx/htmx.min.js
django_htmx/htmx.js /mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/django_htmx/static/django_htmx/htmx.js
django_htmx/django-htmx.js /mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/django_htmx/static/django_htmx/django-htmx.js
js/htmx.min.js /mnt/disks/pennyloupe_disk/pennyloupe/app/core/static/js/htmx.min.js
js/tradingview/charting_library/charting_library.standalone.js /mnt/disks/pennyloupe_disk/pennyloupe/app/core/static/js/tradingview/charting_library/charting_library.standalone.js
img/how-it-works.png /mnt/disks/pennyloupe_disk/pennyloupe/app/core/static/img/how-it-works.png
img/penny_loupe_logo_bw.png /mnt/disks/pennyloupe_disk/pennyloupe/app/core/static/img/penny_loupe_logo_bw.png
img/volatility.png /mnt/disks/pennyloupe_disk/pennyloupe/app/core/static/img/volatility.png
img/penny_loupe_bg.png /mnt/disks/pennyloupe_disk/pennyloupe/app/core/static/img/penny_loupe_bg.png
img/penny_loupe_logo.png /mnt/disks/pennyloupe_disk/pennyloupe/app/core/static/img/penny_loupe_logo.png
img/news_sites.png /mnt/disks/pennyloupe_disk/pennyloupe/app/core/static/img/news_sites.png
img/pennyloupe_icon.png /mnt/disks/pennyloupe_disk/pennyloupe/app/core/static/img/pennyloupe_icon.png
img/penny_loupe_logo_inv.png /mnt/disks/pennyloupe_disk/pennyloupe/app/core/static/img/penny_loupe_logo_inv.png
css/pennyloupe.css /mnt/disks/pennyloupe_disk/pennyloupe/app/core/static/css/pennyloupe.css
django_extensions/js/jquery.ajaxQueue.js /mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/django_extensions/static/django_extensions/js/jquery.ajaxQueue.js
django_extensions/js/jquery.bgiframe.js /mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/django_extensions/static/django_extensions/js/jquery.bgiframe.js
django_extensions/js/jquery.autocomplete.js /mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/django_extensions/static/django_extensions/js/jquery.autocomplete.js
django_extensions/img/indicator.gif /mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/django_extensions/static/django_extensions/img/indicator.gif
django_extensions/css/jquery.autocomplete.css /mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/django_extensions/static/django_extensions/css/jquery.autocomplete.css
debug_toolbar/js/utils.js /mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/debug_toolbar/static/debug_toolbar/js/utils.js
debug_toolbar/js/history.js /mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/debug_toolbar/static/debug_toolbar/js/history.js
debug_toolbar/js/toolbar.js /mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/debug_toolbar/static/debug_toolbar/js/toolbar.js
debug_toolbar/js/timer.js /mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/debug_toolbar/static/debug_toolbar/js/timer.js
debug_toolbar/js/redirect.js /mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/debug_toolbar/static/debug_toolbar/js/redirect.js
debug_toolbar/css/toolbar.css /mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/debug_toolbar/static/debug_toolbar/css/toolbar.css
debug_toolbar/css/print.css /mnt/disks/pennyloupe_disk/pennyloupe/.venv/lib/python3.11/site-packages/debug_toolbar/static/debug_toolbar/css/print.css

Templates (6 rendered)

Template path

  1. templates

Templates

ticker_content_detail.html
/mnt/disks/pennyloupe_disk/pennyloupe/app/core/templates/ticker_content_detail.html
Toggle context {'False': False, 'None': None, 'True': True} {'DEFAULT_MESSAGE_LEVELS': {'DEBUG': 10, 'ERROR': 40, 'INFO': 20, 'SUCCESS': 25, 'WARNING': 30}, 'csrf_token': '<SimpleLazyObject: <function csrf.<locals>._get_val at ' '0x7fa93951ec00>>', 'messages': <FallbackStorage: request=<WSGIRequest: GET '/ticker/SLS/content/30625527'>>, 'perms': PermWrapper(<SimpleLazyObject: <function AuthenticationMiddleware.process_request.<locals>.<lambda> at 0x7fa93951d9e0>>), 'request': '<<request>>', 'user': '<SimpleLazyObject: <function ' 'AuthenticationMiddleware.process_request.<locals>.<lambda> at ' '0x7fa93951d9e0>>'} {'content_detail': {'content': <Content: $SLS Part 2 and FINAL (Deepest Due Diligence for REGAL Trial) (From a Deep Value Investor) (Predicting BAT mOS from Predictive Model) - Confident-Web-7118>, 'content_content': <ContentContent: Content ID: 37845184>, 'data_source': <DataSource: Reddit>, 'publisher': <ContentPublisher: Reddit>}, 'ticker': 'SLS'}
base.html
/mnt/disks/pennyloupe_disk/pennyloupe/templates/base.html
Toggle context {'False': False, 'None': None, 'True': True} {'DEFAULT_MESSAGE_LEVELS': {'DEBUG': 10, 'ERROR': 40, 'INFO': 20, 'SUCCESS': 25, 'WARNING': 30}, 'csrf_token': '<SimpleLazyObject: <function csrf.<locals>._get_val at ' '0x7fa93951ec00>>', 'messages': <FallbackStorage: request=<WSGIRequest: GET '/ticker/SLS/content/30625527'>>, 'perms': PermWrapper(<SimpleLazyObject: <function AuthenticationMiddleware.process_request.<locals>.<lambda> at 0x7fa93951d9e0>>), 'request': '<<request>>', 'user': '<SimpleLazyObject: <function ' 'AuthenticationMiddleware.process_request.<locals>.<lambda> at ' '0x7fa93951d9e0>>'} {'content_detail': {'content': <Content: $SLS Part 2 and FINAL (Deepest Due Diligence for REGAL Trial) (From a Deep Value Investor) (Predicting BAT mOS from Predictive Model) - Confident-Web-7118>, 'content_content': <ContentContent: Content ID: 37845184>, 'data_source': <DataSource: Reddit>, 'publisher': <ContentPublisher: Reddit>}, 'ticker': 'SLS'}
components/leftnav.html
/mnt/disks/pennyloupe_disk/pennyloupe/templates/components/leftnav.html
Toggle context {'False': False, 'None': None, 'True': True} {'DEFAULT_MESSAGE_LEVELS': {'DEBUG': 10, 'ERROR': 40, 'INFO': 20, 'SUCCESS': 25, 'WARNING': 30}, 'csrf_token': '<SimpleLazyObject: <function csrf.<locals>._get_val at ' '0x7fa93951ec00>>', 'messages': <FallbackStorage: request=<WSGIRequest: GET '/ticker/SLS/content/30625527'>>, 'perms': PermWrapper(<SimpleLazyObject: <function AuthenticationMiddleware.process_request.<locals>.<lambda> at 0x7fa93951d9e0>>), 'request': '<<request>>', 'user': '<SimpleLazyObject: <function ' 'AuthenticationMiddleware.process_request.<locals>.<lambda> at ' '0x7fa93951d9e0>>'} {'content_detail': {'content': <Content: $SLS Part 2 and FINAL (Deepest Due Diligence for REGAL Trial) (From a Deep Value Investor) (Predicting BAT mOS from Predictive Model) - Confident-Web-7118>, 'content_content': <ContentContent: Content ID: 37845184>, 'data_source': <DataSource: Reddit>, 'publisher': <ContentPublisher: Reddit>}, 'ticker': 'SLS'} {'block': <Block Node: leftnav. Contents: [<TextNode: '\n <div'>, <IncludeNode: template=<FilterExpression '"components/leftnav.html"'>>, <TextNode: '\n </di'>]>}
components/header.html
/mnt/disks/pennyloupe_disk/pennyloupe/templates/components/header.html
Toggle context {'False': False, 'None': None, 'True': True} {'DEFAULT_MESSAGE_LEVELS': {'DEBUG': 10, 'ERROR': 40, 'INFO': 20, 'SUCCESS': 25, 'WARNING': 30}, 'csrf_token': '<SimpleLazyObject: <function csrf.<locals>._get_val at ' '0x7fa93951ec00>>', 'messages': <FallbackStorage: request=<WSGIRequest: GET '/ticker/SLS/content/30625527'>>, 'perms': PermWrapper(<SimpleLazyObject: <function AuthenticationMiddleware.process_request.<locals>.<lambda> at 0x7fa93951d9e0>>), 'request': '<<request>>', 'user': '<SimpleLazyObject: <function ' 'AuthenticationMiddleware.process_request.<locals>.<lambda> at ' '0x7fa93951d9e0>>'} {'content_detail': {'content': <Content: $SLS Part 2 and FINAL (Deepest Due Diligence for REGAL Trial) (From a Deep Value Investor) (Predicting BAT mOS from Predictive Model) - Confident-Web-7118>, 'content_content': <ContentContent: Content ID: 37845184>, 'data_source': <DataSource: Reddit>, 'publisher': <ContentPublisher: Reddit>}, 'ticker': 'SLS'} {'block': <Block Node: header. Contents: [<TextNode: '\n\n '>, <IncludeNode: template=<FilterExpression '"components/header.html"'>>, <TextNode: '\n\n '>]>}
components/ticker_subnav.html
/mnt/disks/pennyloupe_disk/pennyloupe/app/core/templates/components/ticker_subnav.html
Toggle context {'False': False, 'None': None, 'True': True} {'DEFAULT_MESSAGE_LEVELS': {'DEBUG': 10, 'ERROR': 40, 'INFO': 20, 'SUCCESS': 25, 'WARNING': 30}, 'csrf_token': '<SimpleLazyObject: <function csrf.<locals>._get_val at ' '0x7fa93951ec00>>', 'messages': <FallbackStorage: request=<WSGIRequest: GET '/ticker/SLS/content/30625527'>>, 'perms': PermWrapper(<SimpleLazyObject: <function AuthenticationMiddleware.process_request.<locals>.<lambda> at 0x7fa93951d9e0>>), 'request': '<<request>>', 'user': '<SimpleLazyObject: <function ' 'AuthenticationMiddleware.process_request.<locals>.<lambda> at ' '0x7fa93951d9e0>>'} {'content_detail': {'content': <Content: $SLS Part 2 and FINAL (Deepest Due Diligence for REGAL Trial) (From a Deep Value Investor) (Predicting BAT mOS from Predictive Model) - Confident-Web-7118>, 'content_content': <ContentContent: Content ID: 37845184>, 'data_source': <DataSource: Reddit>, 'publisher': <ContentPublisher: Reddit>}, 'ticker': 'SLS'} {'block': <Block Node: content. Contents: [<TextNode: '\n\n <div class="uk-flex'>, <IncludeNode: template=<FilterExpression '"components/ticker_subnav.html"'>>, <TextNode: '\n <!-- /SUB NAV--'>, <Variable Node: content_detail.content.title>, <TextNode: '</td>\n '>, <IfNode>, <TextNode: '\n '>, <IfNode>, <TextNode: '\n '>, <Variable Node: content_detail.content.id>, <TextNode: '</td>\n '>, <Variable Node: content_detail.content.external_id>, <TextNode: '</td>\n '>, <Variable Node: content_detail.content.author>, <TextNode: '</td>\n '>, <Variable Node: content_detail.content.publish_dt>, <TextNode: '</td>\n '>, <Variable Node: content_detail.content.summary>, <TextNode: '</td>\n '>, <Variable Node: content_detail.content.tags>, <TextNode: '</td>\n '>, <IfNode>, <TextNode: '\n '>, <Variable Node: content_detail.content.sentiment_negative>, <TextNode: ' &nbsp;&nbsp;\n '>, <Variable Node: content_detail.content.sentiment_neutral>, <TextNode: ' &nbsp;&nbsp;\n '>, <Variable Node: content_detail.content.sentiment_positive>, <TextNode: '<br/>\n '>, <django.template.defaulttags.WidthRatioNode object at 0x7fa939f7cfd0>, <TextNode: '%;"></div>\n '>, <Variable Node: content_detail.content.sentiment_neutral>, <TextNode: '" style="width:'>, <django.template.defaulttags.WidthRatioNode object at 0x7fa939f552d0>, <TextNode: '%;"></div>\n '>, <Variable Node: content_detail.content.sentiment_positive>, <TextNode: '" style="width:'>, <django.template.defaulttags.WidthRatioNode object at 0x7fa939f559d0>, <TextNode: '%;"></div>\n '>, <Variable Node: content_detail.content.relevance_score>, <TextNode: '</td>\n '>, <IfNode>, <TextNode: '\n '>, <IfNode>, <TextNode: '\n <li clas'>, <Variable Node: content_detail.data_source.name>, <TextNode: '\n '>, <Variable Node: content_detail.data_source.id>, <TextNode: ')\n '>, <Variable Node: content_detail.data_source.type>, <TextNode: '</td>\n '>, <Variable Node: content_detail.data_source.url>, <TextNode: '" target="_blank">'>, <Variable Node: content_detail.data_source.url>, <TextNode: '</a></td>\n '>, <Variable Node: content_detail.data_source.description>, <TextNode: '</td>\n '>, <Variable Node: content_detail.data_source.weight>, <TextNode: '</td>\n '>]>} {'ticker': 'SLS'}
components/footer.html
/mnt/disks/pennyloupe_disk/pennyloupe/templates/components/footer.html
Toggle context {'False': False, 'None': None, 'True': True} {'DEFAULT_MESSAGE_LEVELS': {'DEBUG': 10, 'ERROR': 40, 'INFO': 20, 'SUCCESS': 25, 'WARNING': 30}, 'csrf_token': '<SimpleLazyObject: <function csrf.<locals>._get_val at ' '0x7fa93951ec00>>', 'messages': <FallbackStorage: request=<WSGIRequest: GET '/ticker/SLS/content/30625527'>>, 'perms': PermWrapper(<SimpleLazyObject: <function AuthenticationMiddleware.process_request.<locals>.<lambda> at 0x7fa93951d9e0>>), 'request': '<<request>>', 'user': '<SimpleLazyObject: <function ' 'AuthenticationMiddleware.process_request.<locals>.<lambda> at ' '0x7fa93951d9e0>>'} {'content_detail': {'content': <Content: $SLS Part 2 and FINAL (Deepest Due Diligence for REGAL Trial) (From a Deep Value Investor) (Predicting BAT mOS from Predictive Model) - Confident-Web-7118>, 'content_content': <ContentContent: Content ID: 37845184>, 'data_source': <DataSource: Reddit>, 'publisher': <ContentPublisher: Reddit>}, 'ticker': 'SLS'} {'block': <Block Node: footer. Contents: [<TextNode: '\n '>, <IncludeNode: template=<FilterExpression '"components/footer.html"'>>, <TextNode: '\n '>]>}

Context processors

django.template.context_processors.csrf
Toggle context {'csrf_token': <SimpleLazyObject: <function csrf.<locals>._get_val at 0x7fa93951ec00>>}
django.template.context_processors.debug
Toggle context {}
django.template.context_processors.request
Toggle context {'request': <WSGIRequest: GET '/ticker/SLS/content/30625527'>}
django.contrib.auth.context_processors.auth
Toggle context {'user': <SimpleLazyObject: <function AuthenticationMiddleware.process_request.<locals>.<lambda> at 0x7fa93951d9e0>>, 'perms': PermWrapper(<SimpleLazyObject: <function AuthenticationMiddleware.process_request.<locals>.<lambda> at 0x7fa93951d9e0>>)}
django.contrib.messages.context_processors.messages
Toggle context {'messages': <FallbackStorage: request=<WSGIRequest: GET '/ticker/SLS/content/30625527'>>, 'DEFAULT_MESSAGE_LEVELS': {'DEBUG': 10, 'INFO': 20, 'SUCCESS': 25, 'WARNING': 30, 'ERROR': 40}}

Cache calls from 1 backend

Summary

Total calls Total time Cache hits Cache misses
0 0 ms 0 0

Commands

add get set get_or_set touch delete clear get_many set_many delete_many has_key incr decr incr_version decr_version
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Signals

Signal Receivers
class_prepared
connection_created register_type_handlers
got_request_exception
m2m_changed
post_delete
post_init
post_migrate create_permissions, create_contenttypes
post_save create_initial_trade_position
pre_delete
pre_init
pre_migrate inject_rename_contenttypes_operations
pre_save
request_finished close_old_connections, close_caches, reset_urlconf
request_started reset_queries, close_old_connections
setting_changed reset_cache, clear_cache_handlers, update_installed_apps, update_connections_time_zone, clear_routers_cache, reset_template_engines, storages_changed, clear_serializers_cache, language_changed, localize_settings_changed, complex_setting_changed, root_urlconf_changed, static_storage_changed, static_finders_changed, form_renderer_changed, auth_password_validators_changed, user_model_swapped, update_toolbar_config, reset_hashers, Options.setting_changed, update_level_tags, uninstall_if_needed, clear_caches, StaticFilesStorage._clear_cached_properties, FileSystemStorage._clear_cached_properties, FileSystemStorage._clear_cached_properties, FileSystemStorage._clear_cached_properties, FileSystemStorage._clear_cached_properties, FileSystemStorage._clear_cached_properties, FileSystemStorage._clear_cached_properties