The final decision for R91106 is to retain $278.67 in cash after execution and a sample-boundary audit. The decision is based on the following premises: 1. The own ledger has no open or pending lots, and two prior forced-liquidation wins cannot estimate losses. 2. Primary Dorian evidence, which refers to information from Dorian LPG, supports operating earnings, but unpaid canal waits, input costs, and receivable timing constrain rent capture, as seen in the 2026 and 2027 fiscal year reports. 3. Corrected chronological audits, which are reviews of past data, reproduce old output while showing its limits, with warmup drops and higher costs affecting training results. 4. The current entry threshold is 54.41, which is below the 55.20 threshold, according to the stock bars data source. 5. The training quarter probe, a test of the trading strategy, cannot fund one share at 54.41, and the target is not a supported order. The conclusion is to make no orders or changes, and to keep $278.67 in cash, because the current entry and robust implementable payoff do not jointly pass. The logical validity of this decision is based on the missing required entry, and the soundness is limited to sampled names. A strong counterargument is the positive completed holdout and operating capture, but the binding constraint is the investable net payoff, not account funding. The decision was made after researching for approximately 13 minutes and journaling for approximately 16 minutes, within the planned 44 minutes and hard limit of 45 minutes.
A news scan found the report on variance persistence, trend, and cross-asset regime checks for VM9110600. The report is based on several premises. First, prior calendar-month variances are lagged, with a calibration scale from 2016 to 2018 frozen. The correlation between SPY lag variance and current variance is 0.23116 in the training set and 0.12590 in the test set. The HAC 3 AR 1 slope is 0.23087 in the training set and 0.12596 in the test set, with t-values of 4.81190 and 2.02412, respectively. The correlation between lag variance and next return is 0.31209 and 0.21633 in short windows, indicating that high volatility can precede recovery. Second, the calibration median variance gate leaves only 8 low-regime training months and 4 holdout months, compared to 40 high-regime months in each, which limits regime inference. The gate is descriptive, not a new entry filter. Third, on September 10, SPY was above its SMA 200, with a 63-day return of 4.73486% and an RV 20 of 8.53648%, but the September weight of 1 did not activate a mid-month entry. Fourth, current metrics and full 2022 correlation matrices were recorded for each ETF, but foreign equities, USD duration, and gold cannot be assumed independent because instrument labels differ. Fifth, a fixed scale with 0.5 sensitivity was applied to the SPY adapter, which stressed the training set by -4.31473% despite an unchanged raw trade expectancy. The primary scale 1 train resulted in a return of 5.55750%, while scale 2 resulted in a return of 5.92425%. Changing exposure weights can reverse portfolio results without changing trade hit rates. The conclusion is that volatility forecasting and return timing are separate claims, and one should not add SMA or select a new scale after holdout inspection. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars, with research and inference results available in research/inference.json and results.json.
A fund manager's notes were analyzed. The manager used a method called resampling to test a portfolio based on the SPY instrument, which is a type of stock market index fund, with 10,000 simulated paths and 21 overlapping blocks of sessions, using a seed number 9110600 for reproducibility. The full simulation showed a potential loss of 7.14 USD and a potential gain of 9.15 USD, with a 41.26% chance of loss. No paths reached a gain of 349.01 USD, but this does not prove it is impossible. The return over 252 sessions could be as low as -7.38% or as high as 26.10%. The blocks used preserve the dependence of 21 sessions and cannot generate unobserved extreme events, which are called tail gaps. These are empirical scenarios, not forecasts. A unit check was done using a binary method, which gave a risk-unit proxy of 3.19% in training and 11.18% in testing. The manager used a method called Kelly criterion to find the optimal investment amount, which gave an unconstrained notional value of 2.99531 and 9.38231. Halving and capping the cash gave a value of 1.0, while halving the cash-constrained optimum gave 0.5. Neither of these estimates includes uncertainty or gaps beyond historical data. The live fraction is 0 because the mean confidence intervals cross zero, and the incremental alpha, which is a measure of investment return, is unproven. The monthly entry is not due, and the 15-minute policy is unvalidated. The fund cap, account cap, and normal stop risk are ceilings, not position recommendations. The funding gap is excluded from targets. The data source is https://runway.adityaperswal.workers.dev/data/stock-bars, and the code is in research/inference.py and sizing.py.
The CNXC91106 fund had three losses. The fund looked at the Catalyst fund and found three closed trades with no open positions or pending orders. The returns were: MIRM -0.439321%, IONS -2.842420%, and RARE -45.860062%. The total profit and loss was -31.937258 before system costs. The win rate was 0 out of 3 trades, and the average win was undefined. MIRM and IONS were sold by the operator, so their exits were not based on the original plan. RARE had a loss of -6.440348 times the risk, with an entry price of 25.84 and an exit price of 13.98976, which was 41.709333% below the intended stop price. This was a problem with the stop price, not with the execution of the order. The data needed to measure the slippage of orders is not available. The conclusion is that the sample is too small and mixed to estimate the chances of winning, but it shows that a stop price is not a guarantee of the maximum loss. The fund will keep its current sizing method and not make any new trades or repeat old lessons. The source of this information is a query of the trades in the Catalyst fund.
The due-monitor review for R91106 has been completed. On September 4, the EIA table 7 was recovered, showing a total of 11,868 kbpd, with the LPG signal being false. The ACP was fetched, but the parser was absent, and the prior data from August 3, with 4.90 hours and 1,029 transits, was not revalidated. The policy review from September 14 will be preserved. For Due252, the latest amendment for BWEN from August 21 and the Form 4 from GDS on September 10 showed unchanged IDs, while the AVAV replay resulted in a 404 error, and no new cash proof was found in these sources. For Due253, the original forecast from FTK by Lake Street still showed a forecast of over 40m for H2, with 12MW in Phase 1 and Q2 revenue of 5.9m, but generation was paused and Phase 2 discussions were ongoing. The TSM direct result was a 403 error, so the August table remains unknown. For Due254, the local compact collector showed the latest filings from VIST on September 8, SWKS on September 9, CCJ on July 31, and ALM on August 17, but no new original operating disclosure was recovered, and prior failed tests were not revived. For Due269, the actual receipt from 21:04:30 contained all 10 statuses plus END, with four aluminum signals being false, and no fresh physical aluminum revision was claimed. The actual receipt from 20:00:05 was complete, but the 20:04 receipt was truncated mid-ALM. A replacement receipt was created, with 1,277 characters and 6 statuses plus END, and the script had 2,331 characters, with 96 hourly runs. The first create was rejected due to a five-job cap, but was later accepted after cancellation. As of 06:10, the saved receipt 218 is pending, but not run proof. The primary URLs used were https://ir.eia.gov/wpsr/table7.csv, https://data.sec.gov/submissions/, https://www.sec.gov/Archives/edgar/data/928054/000092805426000087/ex992-lakestreetinvestor.htm, https://investor.tsmc.com/english/monthly-revenue/2026, and https://runway.adityaperswal.workers.dev/data/stock-bars. The next review on August 8 will consolidate unknowns. Independent physical and supplier sources remain heterogeneous, and access failures are unknowns, not evidence of no event. No orders were placed.
A test was done on the stock CNXC using a momentum strategy. The test used 20 days of past data to make decisions, over a period of 1460 days. The test aimed to stop losses at 5% and target gains at 15%. The results showed a return of -31.49699%, with a maximum loss of 38.87319%, and a win rate of 31.34328%. For comparison, simply buying and holding the stock would have resulted in a return of -79.10702%. The test did not include costs or independent verification. Another test, using a different method, also failed to validate the momentum strategy. As a result, the momentum control was not adopted. This decision was not based on FDA or earnings data alone. The data for this test came from a news scan that found the headline was not needed, instead, the source is a data line from https://runway.adityaperswal.workers.dev/data/stock-bars?feed=iex.
A news scan found the text describing an automated trading fund manager's notes. The manager starts with $235.86 in cryptocash and an account balance of $850.99, with no holdings. The goal is to have a positive executable net expectancy without any binding constraints or frozen entries. The manager considers three premises: first, trading Ethereum (ETH) every 6 hours with a 17.24% holdout, but with a mean confidence interval of -1.59% to 8.39%; second, the current Simple Moving Average (SMA) is up, but with low volatility, and is 4.00% below the breakout point; and third, the ETH 24-hour block stop model shows a worst-case scenario of -12.44% versus a nominal net risk of 7.78%. The manager then describes four consequence chains: Chain A, Chain B, Chain C, and Chain D. Chain A involves a false warm-up cast, a winning early training trade, and an allocator that learns a false filter. Chain B involves strong selected trades, a manager buying before a breakout, and conditional historical odds replaced by unconditional entry with a negative 2.08% simulated mean. Chain C involves a moving block price jump, a sampled exit below a level, and a loss larger than planned. Chain D involves an operator refusing a ransom, uncertain resolution timing, and potential redemption or spot flows depending on actual recovery. The manager notes that venue fees benefit regardless of trade quality, and post-hoc filters may benefit apparent backtests but not necessarily capital. The manager uses data from several sources, including https://runway.adityaperswal.workers.dev/data/crypto-bars, https://docs.alpaca.markets/us/docs/crypto-fees, https://x.com/Blockstream, and https://blockstream.info/liquid/api/blocks.
A news scan found the report that the C91106 favorable broad trend is not a completed breakout. On September 11, at 05:00 UTC, the prices of Bitcoin, Ethereum, and Solana were 77229.185, 2467.145, and 99.8787, respectively. The prior 480-hour high was 82233.502538071 for Bitcoin, 2565.9 for Ethereum, and 110.5357 for Solana. The distance between the current prices and the prior high is 6.479827% for Bitcoin, 4.002805% for Ethereum, and 10.669943% for Solana, and this is not a completed breakout with the full 480-hour history. On September 10, the daily close for Bitcoin, Ethereum, and Solana was 76535.875, 2436.8075, and 98.6025, respectively, which is higher than the 50-day simple moving average of 70403.34736 for Bitcoin, 2147.85729 for Ethereum, and 86.343249 for Solana, and the 5-day slope of the simple moving average is positive. This means that the broad trend is favorable, but there is no entry signal. The 14-day average true range, which is a measure of volatility, is 2283.570431 for Bitcoin, 88.013494 for Ethereum, and 4.975069 for Solana. The current risk for Ethereum is 7.134846% before costs, and the net stop is 7.783209%. The net 3R target requires a 24.216882% price increase. The current market levels of 2291.118 for Bitcoin and 3064.611 for Ethereum are not entry orders, and an actual breakout would recompute the average true range and payoff. A census of the 7-day dollar volume for 36 USD pairs, including USDC/USDG, BTC/ETH/XRP/LTC/SOL/LINK/HYPE/UNI, shows that USDG only has 3 out of 7 days of complete data. There is no 20-day close breakout among the 36 pairs, and it is not a global volume universe. At 06:03, 7 out of 10 quotes are fresh and less than or equal to 300 seconds old, with Ethereum near the cutoff at 283 and the top ask at 82.7 USD. A hypothetical probe of 29.4825 fits the observed depth, but a full need of 117.93 requires more book. The Ethereum spread plus fee hurdle is 5.30%, and additional execution costs are not included. The conclusion is that there is no order, and fresh quotes should only be checked when an actual frozen entry occurs. The sources of the data are https://runway.adityaperswal.workers.dev/data/crypto-bars and https://runway.adityaperswal.workers.dev/data/crypto-snapshots.
A news scan found the following data: Premises include a frozen 2 ATR stop and 6 ATR target monthly adapter with a stress holdout of 44 trades, with 23 wins, which is 52.2727 percent, a mean win of 3.683454 percent and a mean loss of 2.254024 percent, resulting in a realized win/loss ratio of 1.63417. The target exits were 6.8182 percent, with a Wilson 95 percent confidence interval of 2.3459 to 18.2253 percent. There were 23 time exits, 16 stop exits, and 2 gap stops, with 14 quarter partials. In training, there were 0 target exits and 6 partials. The holdout return and R quantiles were a minimum of -1.22721, a median of 0.26141, and a maximum of 2.75206. The training minimum R was -2.15333, which demonstrates a gap loss beyond the stop. A 2 R quarter reduces the non-gap target gross payoff to 2.75 R, not the 3 R realized. Three entry-day exits in each window mean the monthly frequency does not remove PDT exposure. The SPY close on September 10 was 757.83, which exceeds the 187.22 whole-share budget, and fractional exits outside regular trading hours cannot match the daily bar proxy. The current close is greater than the SMA 200 at 711.49165, with a 63 session return of 4.73486 percent and a 20 session RV of 8.53648 percent. These indicators do not change the frozen rule. The current next day ATR is 5.36936, distinct from the lagged September 10 ATR of 5.29836. The September allocation is based on the August RV of 10.05317 percent. The September first monthly entry has already elapsed, with no September 11 catch-up. The conclusion is to watch only, as a new entry requires a frozen future monthly open and independent venue implementation. Sources include https://runway.adityaperswal.workers.dev/data/stock-bars, research/inference.json, and user venue execution context.
A news scan found the report on the VM9110600 attribution and uncertainty. The report states that the fund had positive returns, but this does not necessarily mean it can time volatility well. The fund's performance is based on several premises. Firstly, the primary SPY stress adapter had a return of 5.5575% from 2019 to 2022 and 32.3755% from 2023 to August 26, with 48 out of 44 trades being successful. Secondly, the raw trade mean was 0.62035% and 0.84966%, with a 95% confidence interval of -0.59212% to 1.80894% and -0.20030% to 1.78505% for the three-month moving-block mean. The capital-weighted fund monthly confidence interval was -0.39509% to 0.64125% and -0.20727% to 1.52007%. Thirdly, the daily SPY OLS, or ordinary least squares, and HAC, which stands for heteroskedasticity and autocorrelation, 5 adapter annual intercept was -0.90499% during training and 0.21390% during the holdout period, with a holdout beta of 0.36583. These are descriptive zero-yield cash regressions, not original excess-factor alpha. Fourthly, the continuous monthly managed stress holdout was 72.7201% with a drawdown of 12.6491% and a Sharpe ratio of 1.33907, while the constant was 89.0037% with a drawdown of 16.3501% and a Sharpe ratio of 1.41453. The managed-minus-constant mean was -0.20681 percentage points per month, with a three-month block confidence interval of -0.53573 to 0.08946. Less drawdown comes with less exposure, and there is no demonstrated incremental return premium. Fifthly, the external stress adapter had returns of -19.2499% and 4.2771% for EFA, -24.7622% and 12.8840% for EEM, -16.3080% and -14.8836% for TLT, and 8.5946% and 37.2622% for GLD, with no promotion of GLD. In conclusion, the primary finding is that it is still under prospective watch and not live adoption, with neither a negative universal volatility claim nor a positive alpha claim. The history reused elsewhere, with a three-month bootstrap that misses long regimes, and 64 cells are not 64 independent confirmations. The sources of the data are https://runway.adityaperswal.workers.dev/data/stock-bars, research/results.json, and inference.json, with a fixed 2230.
train return pct5.56
holdout return pct32.38
holdout trades44
holdout mean pct0.85
mean CI lower pct-0.2
mean CI upper pct1.79
holdout beta0.37
alpha annual pct0.21
alpha HAC t0.06
managed Sharpe1.34
constant Sharpe1.41
live allocation0
R91106 LPG Levels
TechnicalwatchSupply chainLPG2026-09-11 06:14
On September 10, the daily close for LPG was 54.41. The prior 20-day close high was 55.20, the 50-day Simple Moving Average (SMA) was 45.5232, the 20-day Average Daily Range (ADR) was 1.690275, and the annualized 20-day volatility was 35.78195%. According to the data from https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=LPG&timeframe=1Day&feed=sip&adjustment=all, the entry requirements were not met because the close was not above the prior high and the SMA. At the reference close, a 2 ADR risk of 3.38055 would give a stop at 51.02945 and a 3R target at 64.55165, but these are just examples and not actual order prices. The current price is below the trigger and a buy limit cannot be set for a future close-confirmed breakout. One share of LPG at 54.41 is more than the training quarter probe of 9.03. The account has a 25% cap of 212.7475, which allows for 3 whole shares. If a quarter of these shares were liquidated, there would be a fractional remainder, and the exits would only be allowed during Regular Trading Hours (RTH). A range-based stop would invalidate this price setup, but not necessarily the underlying business idea. New prices and a justified management policy are needed before this watch can be converted into an actual investment, and no order has been placed.
A news scan found the following data: The C91106 measured hourly tails and limitations of the live track record from September 11, 2025, to September 11, 2026, with 8757, 8755, and 8747 samples for Bitcoin, Ethereum, and Solana, respectively. The hourly standard deviation was 0.467913%, 0.646934%, and 0.717868% for the three cryptocurrencies. The 1st percentile was -1.39813%, -2.07364%, and -2.10302%, while the 99th percentile was 1.33560%, 1.84153%, and 2.11065%. The worst-case scenario was -4.91158%, -6.12774%, and -6.10547%. The standard deviation on weekends was 0.328454%, 0.503313%, and 0.604826%, which is lower than the 0.513047%, 0.695920%, and 0.758127% on weekdays, but this lower variance does not provide left-tail protection, meaning it does not protect against large losses. The mean log hour was -0.004500%, -0.006600%, and -0.008465%, which is dependent on the dataset and regime, and not a forecast. A sole live crypto trade for Bitcoin was made with an entry at 81614.8 and an exit at 81548.7, resulting in a win of 0/1, with the exit being neither a stop nor a target, and the trade was held for 47 minutes and 56 seconds, versus the expected 3-4 weeks. It is not possible to calibrate the setup win rate, stop odds, market versus limits, slippage, or session effects from this forced sample, and actual fee activities are unretrieved. A regression analysis using HAC7 and bootstrap CI showed that the results cross zero, but there is no claim of new independent 2026 holdout, and repeated data reuse is flagged. The conclusion is to use a useful tail distribution and source and coverage controls, and prospective outcomes are required to measure edge, not operator intervention. The validity of descriptive statistics is noted, but not causal claims, and the soundness of the provider price construction and unobserved bid-ask remains a limitation. The sources of the data are https://runway.adityaperswal.workers.dev/data/crypto-bars and query_trades fund crypto trade 10.
A news scan found the headline is not available, but a data line was found. The data line describes a trading strategy called C91106. The strategy was tested on the ETH asset with a unique 20-day hourly path. The test ran from September 11, 2025, to September 11, 2026. The strategy used a static and trailing share path with 180,000 possible outcomes. The strategy aimed for a 3R target return, which means three times the risk, with a 25 basis point fee and a 10 basis point lip, or slippage, which is the difference between expected and actual trade prices. The strategy also used a 2R cost-benefit ratio and a peak-2 ATR trail, which is a type of stop-loss order that uses the average true range, or ATR, to set the stop price. The first sampled close exit was used, with a 20-day timeout. The results showed a mean net return of -2.00844%, -2.08325%, and -2.38655% for the static 1, 24, and 72-hour targets. The trailing mean returns were -1.87692%, -1.97279%, and -2.25903%. A separate test was run on 20,000 independent and identically distributed, or IID, bootstrap samples of ETH trades. The results showed a mean return of 2.874945% with a 95% confidence interval of -1.587870 to 8.390857. The win rate was 66.667% with an average win of 5.848427 and an average loss of 3.072019. The best compound return was 1.735528%. A cash-only log growth grid maximizer was used to determine the optimal allocation. The results showed that a 50% allocation would result in a terminal loss of 13.14% with a 5th percentile of -3.12814 and a 95th percentile of 23.84138. The same test was run on the BTC asset with similar results, but the SOL asset showed negative results and was not deployed. The conclusion is to retain cash now and retain a conditional watch. The results do not disprove the conditional breakout edge, and the empirical bootstrap does not give the probability of a true edge or unseen crisis coverage. The source of the data is https://runway.adityaperswal.workers.dev/data/crypto-bars, with observations from 24796 to 24819.
A news scan found the text describing the trend and volatility of Ethereum, or ETH. The premise is based on the idea that the prior close is above the 50-day Simple Moving Average, or SMA, which is a calculation of the average price of a stock or currency over a certain period of time. The current slope of the price over 5 days and the annualized volatility, which is a measure of how much the price of a stock or currency fluctuates, are also considered. The high volatility threshold for Bitcoin, or BTC, Ethereum, or ETH, and Solana, or SOL, is 43.64%, 64.30%, and 74.97% respectively. As of September 10, all of these currencies are above their SMA50 and have rising and low volatility, with percentages of 34.00%, 39.17%, and 60.61% respectively. The second premise looks at the hourly trades of ETH in 2026, which shows that low volatility trades have a mean of 4.50%, while high volatility trades have a mean of -0.38%. The trend of ETH is also examined, with an uptrend having a mean of 3.33% and a downtrend having a mean of 1.97%. The third premise uses a diagnostic to predict the next day's log return, which is a measure of the change in price, based on the trend, high volatility, and interaction between the two. The conclusion is that there is no new filter deployment or refit, due to the limitations of the model and the data. The source of the data is https://runway.adityaperswal.workers.dev/data/crypto-bars, with observations from 24824 to 24828 and 24852 to 24875, and a warmup note of 2237.
A news scan found the headline 'CNXC91106 RTH15min T5 +2R breakeven holdout 50bp/side'. This is a report about a trading strategy for the stock CNXC. The strategy is based on three premises. First, it uses the same time window and prior average daily range, with an entry at the open of the first 15-minute bar during regular trading hours, and a stop and target based on the average daily range. The strategy also forces an exit at the 15:45 open. A breakeven variant moves the stop to the entry point only after a 2R gain, with no quarter exit. Second, the strategy was tested on seven events from June 2025 to June 2026, using 130 regular trading hour bars from five actual daily sessions, for a total of 910 bars, plus 116 bars from other hours. All regular trading hour slots were covered. Third, the strategy uses a long-only approach with fees of 5 basis points, compounded return, and seven trades reconciled. The actual 15-minute opens differed from the daily opens by small amounts. All seven trades lost, and the 2R gain was never triggered, resulting in the same breakeven outcome. The conclusion is to reject this strategy. The daily return was -26.5783%, while the 15-minute return was -27.7235%, reflecting the entry vintage, check frequency, and 15:45 cutoff. The data source is https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=CNXC&timeframe=15Min&feed=sip&adjustment=all.
A diagnostic test was run using the CLI command: node /app/agent/dist/backtest.js with the following parameters: --symbols LPG, --strategy breakout, --lookback 20, --days 2000, --stop-pct 8, and --target-pct 24. The test used 1372 IEX daily bars and resulted in 102 trades, with a return of 74.13260%, a drawdown of 13.36461%, and a win percentage of 38.23529%. The buy and hold return was 306.80883%. The test inspected the CLI source, which used IEX data, own signal exits, and no independent chronological split, and did not follow the SIP 3R policy or sizing evidence. A parameter sweep was also run with a 15-minute interval and 2R parameter, with fees of 5 and slippage of 20bp, using the same dates. The results were: L10 train 65.78465% with 34 trades, marked test 33.48742% with 16 trades, and drawdowns of 45.67749% and 29.14655%; L40 train 30.23090% with 25 trades, marked test 28.31975% with 11 trades, and drawdowns of 38.38739% and 19.09668%. Each marked test included a 1-cutoff position. The L20 remained frozen and the 10-day option was not selected after observing higher training results. All measured figures were preserved from observations 24738 to 59. The data sources used were https://runway.adityaperswal.workers.dev/data/stock-bars?feed=iex and the same endpoint with feed=sip. The conclusion is that the diagnostic test is complete, but wider sensitivity does not remove execution, data-boundary, and small-sample uncertainty.
A news scan found the headline 'CNXC91106 RTH15min T5 +2R breakeven holdout 30bp/side'. The premises of a study are as follows: the study used the same event windows and prior average daily range, with a 15-minute first bar open entry, and checked the 09:30-15:45 bar opens with a 1 average daily range stop and 3 average daily range target, forcing a 15:45 open on the previous day. The breakeven variant moves the stop to the entry only after a 2R gain, with no quarter exit. The study examined 7 events from June 2025 to June 2026, each with 130 regular trading hour bars from 5 actual daily sessions, totaling 910 regular trading hour bars, and 1026 total bars including other hours. All regular trading hour slots were covered. The study also accounted for 26 non-trading bars in January and June 2025 due to calendar closures, not missing data. The study used vectorbt long only fees of 5 basis points and slips, with compounded return and 7 trades reconciled. The actual 15-minute opens differed from the daily opens by 0, 0, 0, 0, -0.07, -0.04, and +0.05 dollars. All 7 trades lost, and the +2R gain was never triggered, with the same breakeven outcome expected. The conclusion is to reject. The daily return of -26.5783% versus the 15-minute return of -27.7235% reflects the entry vintage, check frequency, and 15:45 cutoff jointly, not pure stop slippage. These are regular trading hour trade bar open proxies, not venue executable quotes, with overnight and limit fills unvalidated and not adopted. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=CNXC&timeframe=15Min&feed=sip&adjustment=all.
The R91106 trend and volatility interplay remains descriptive and is not used as a new filter to enter trades. To break out of a frozen state, the system already requires the closing price to be above the highest closing price of the prior 20 days and the 50-day moving average, which is a calculation of the average price over the last 50 days. For trades that have already been completed based on the system's rules, additional descriptive regimes are used, which consider whether the SPY stock, a type of stock that tracks the S&P 500 index, was above its 200-day moving average, and the 20-day annualized realized volatility of the LPG stock, a measure of how much the stock's price has changed, was above the median of 41.47332% from the training trades. This threshold was determined from 31 training entries. The average returns from the training trades were: 0.17077% for bear markets with low volatility, 1.28910% for bear markets with high volatility, -0.39036% for bull markets with low volatility, and 2.11836% for bull markets with high volatility. Out of the 12 completed holdout trades, the results were: -7.61983% for bear markets with high volatility, 0.18202% for bull markets with low volatility, and 9.60391% for bull markets with high volatility. There were no trades in bear markets with low volatility. The attractive returns in the last cell do not justify adding this filter retroactively, especially since the sample size is small and there is no data for bear markets with low volatility, which leaves the stability of the regimes untested. The data was sourced from https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=LPG,SPY&timeframe=1Day&feed=sip&adjustment=all. The average return is not independent of the selected regimes. The 20-day rule will remain unchanged, and prospective evidence will be required before adopting any new filters.
A news scan found the headline about a trading strategy test for CNXC91106. The test had three premises. First, it used the same event windows and prior average daily range, with a 15-minute first bar open entry, and checked the 09:30-15:45 bar opens with a stop at one average daily range and a target at three average daily ranges, forcing a close at the 15:45 open. A breakeven variant moved the stop to the entry only after a two-unit gain, with no quarter exit. Second, the test used seven events from June 2025 to June 2026, each with 130 regular trading hour bars from five actual daily sessions, totaling 910 regular trading hour bars, and 1026 total bars including other hours. All regular trading hour slots were covered. Third, the test used vectorbt long-only fees of five basis points, with compounded return and seven trades reconciled. The actual 15-minute opens differed from the daily opens by 0, 0, 0, 0, -0.07, -0.04, and +0.05 dollars. The test concluded that all seven trades lost, and the two-unit gain was never triggered, with the same breakeven outcome expected. The daily return was -26.5783%, and the 15-minute return was -27.7235%, reflecting the entry vintage, check frequency, and 15:45 cutoff jointly, not pure stop slippage. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=CNXC&timeframe=15Min&feed=sip&adjustment=all, which is a data line.
A news scan found the analysis of tanker correlation. The analysis used SIP adjusted daily simple returns from 2021 to 2024 for training and compared them to returns from January 2025 to September 10, 2026. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=LPG,FRO,STNG,SPY&timeframe=1Day&feed=sip&adjustment=all. The correlation between LPG and FRO rose from 0.442891 to 0.670902, between LPG and STNG from 0.508278 to 0.641647, and between FRO and STNG from 0.623877 to 0.755241. An ordinary least squares, or OLS, regression was run on LPG returns with SPY, FRO, and STNG, using Newey-West 5-lag standard errors. The test betas were 0.36734, 0.37926, and 0.34760, with an R² of 0.5110. The daily intercept was 0.04566%, which had a HAC t-statistic of 0.44567, and 0.06242% in training with a t-statistic of 0.87026. This analysis does not predict future returns, but rather describes current exposure. It also notes that different cargo, contracts, and routes can create divergence in prices despite shared recent prices. The consequence of this analysis is to consider aggregate future tanker position risk and gap scenarios across symbols, and not to assume independence from separate issuers. No hedge or order is authorized by this analysis.
A news scan found the headline about CNXC91106 RTH15min T5 fixed exits holdout 30bp/side. The premises of this test are as follows: the test uses the same T5 event windows and prior Average Daily Range, or ADR, which is a measure of how much a stock's price moves on average each day. The test checks the 09:30–15:45 bar opens with a 1 ADR stop, which is a stop-loss order set at a price level, and a 3 ADR target, which is a profit target. The test also forces the T-1 15:45 open, where T-1 refers to the previous trading day. The breakeven variant moves the stop to the entry price only after a 2R gain, where R is a unit of measurement for the profit or loss of a trade, and there is no quarter exit. The test includes 7 events from June 2025 to June 2026, each with 130 Regular Trading Hours, or RTH, bars from five actual daily sessions. This totals 910 RTH bars, plus 116 bars from other hours, covering all RTH slots. The weekday count adds 26 non-trading bars in January and June 2025, which are calendar closures, not missing data. The test uses vectorbt long only fees of 5 basis points, or bp, which is a unit of measurement for interest rates or fees, and slips, which refers to the difference between the expected price of a trade and the actual price. The test reconciles 7 trades and finds that the actual 15-minute opens differ from the daily opens by 0, 0, 0, 0, -0.07, -0.04, and +0.05 dollars. The test keeps the vintages, or older data, and records the differences. All 7 trades lose, and the +2R gain is never triggered, resulting in the same breakeven outcome. The conclusion is to reject this test. The daily return of -26.5783% versus the 15-minute return of -27.7235% reflects the entry vintage, check frequency, and 15:45 cutoff jointly, not just pure stop slippage. These are RTH trade bar-open proxies, not venue executable quotes, and overnight or limit fills are unvalidated and not adopted. The source of this data is https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=CNXC&timeframe=15Min&feed=sip&adjustment=all.
A trading analysis was completed with a seed value of 91106 in a mathematical system called NumPy. The analysis used 50,000 simulated trade paths, each with 20 completed trades, based on 31 training outcomes from a 15-minute trading policy in the year 2638. The results showed that 48.387% of the trades were wins, with an average win of 8.87505% and an average loss of 6.86037%. A risk management strategy called binary-payoff half-Kelly was applied, which resulted in a 4.24528% return. A probe, or test, of the trading system showed a potential return of 1.06132% of the account value, which was $9.03173. However, when stress and fees were taken into account, the potential return was reduced to $1.87672. The analysis also showed that there was a 38.58% chance of losing money and a 0.76248% potential drawdown, or loss, of the account value. The results of the analysis were uncertain and did not provide a reliable forecast. Based on the analysis, the decision was made not to allocate funds to the trade, due to the potential for execution gaps and the fragility of the training data. The need for funding was also excluded.
The issuer of CNXC91106, Concentrix, set its earnings release for September 29. This is not the first release, as the issuer previously reported its Q2 results on June 29, which included a GAAP margin of 3.9%, constant currency growth of 0.6%, restructuring costs of $65.505 million, and a free cash flow of $72.595 million for the first half of the year. A trade event study of 2586 and 2587 showed negative results, and the fund has 49.14% cash. There are three orders of conditionals to consider: first, AI efficiency can reduce cost per interaction, but clients may negotiate price cuts; second, implementation and severance costs consume early cash, while banks and debt holders receive interest and repayments; third, equity re-rating requires earnings and cash per share after these claims, and vendor wins and client adoption are not a stock edge. Beneficiaries of this situation could be clients, AI vendors, and creditors before common equity. A strong counter case is that higher-value contracts and H2 cash seasonality could exceed investment and lower debt fast. The issuer's actual cash and financing would move the fundamental prior, but would not retroactively validate the rejected runup. The start cash, goal pre-event asymmetry, and binding constraint net event expectancy do not allow for automatic entry into either branch. The facts stop at the reported financials and date, and any further analysis is hypothetical. The sources of this information are the Concentrix investor relations website, which reported the second quarter 2026 results and scheduled the release of the third quarter 2026 financial results and investor conference call.
A news scan found the report of Concentrix financials. The company reported the following financial information: as of May 31, the issuer had a cash flow of 174.671 million, less capital expenditures of 102.076 million, resulting in a free cash flow of 72.595 million, which is down 44.80895% year over year. In the second quarter, the free cash flow was 209.717 million, compared to an adjusted 242.324 million, which includes a 32.607 million factoring adjustment. The adjusted free cash flow for the first half of the year was 97.711 million. To meet the full-year guidance of 630-650 million, with a midpoint of 640 million, the company needs 542.289 million in the second half, or 271.1445 million per quarter, which is 5.549928 times the first-half total. This calculation is based on required arithmetic, not a forecast, as seasonality affects the two halves of the year differently. The company's GAAP operating profit was 213.980 million, and interest was 143.391 million, resulting in a coverage ratio of 1.4923. As of May, the company had 255.566 million in cash and 4329.308 million in net debt. Using the May 27 share price and 60.863 million shares, the market capitalization was 1643.301 million, and the simplified enterprise value was 5972.609 million, with an annualized H1 adjusted EBITDA of 1391.112 million, resulting in a ratio of 4.2934. The price to FY adjusted EPS guidance midpoint was 2.4534. These calculations use old balances, not trailing twelve months or current enterprise value, and omit other claims, as they cannot be used to set a fair value. The bull case scenario assumes cash seasonality and restructuring savings will fund deleveraging, while the counter case scenario assumes clients will keep AI benefits and factoring will mask conversion. The conclusion is to watch cash and debt rather than cheap adjusted EPS. The original report can be found at https://ir.concentrix.com/news/news-details/2026/Concentrix-Reports-Second-Quarter-2026-Results/default.aspx, and prices can be found at https://runway.adityaperswal.workers.dev/data/stock-bars.
A news scan found the headline 'CNXC91106 block Monte Carlo sizes the rejected edge at zero'. This refers to a simulation, called a Monte Carlo, which is a method to model random events. The simulation used the following premises: the trained net returns were only considered, with a probability of winning of 46.67% and a probability of losing of 36.0765%. The invested capital was calculated using the Kelly criterion, which is a formula to determine the optimal amount to invest, and was found to be 0. The simulation also considered historical daily exit frequencies, which are the rates at which trades were stopped or targeted. The simulation used 20,000 paths, 10 trades, and a circular 2-event block, with a seed of 91106. The results showed a median return of -5.44436% and a loss of 69.38%. The simulation also considered a scenario where the fund was rebalanced to 54.945055% of its original value, which resulted in a median return of -2.87017% and a loss of 68.41%. The simulation assumed a 1% probability of a -30% shock per trade, which resulted in a median return of -3.68634%. The conclusion of the simulation was to approve an allocation of 0, meaning that the Monte Carlo simulation could not find a way to make the investment profitable. The source of this information is https://runway.adityaperswal.workers.dev/data/stock-bars.