A news scan found the following information about a merger. The premises are as follows: the stock price of TXNM on September 4 was 58, with an offer of 61.25, resulting in a gross spread of 3.25 USD or 5.60345%. This calculation uses different break points, such as 40, 45, 50, and 55, and different time periods, such as 180, 270, and 365 days, with hurdle rates of 0, 4, and 6% and either no dividends or assumed continued dividends of 4.225 USD per quarter. At a break point of 50 and 270 days with a 4% annual alternative return, the required success probability is 87.1697% without dividends or 76.1309% with assumed dividends of 1.25014 USD. The cost of a win is 7.59741% and the cost of a loss is 11.89916% in the dividend case. A Bernoulli simulation with 100,000 paths per scenario and a seed of 90605 was run, and the results showed that the Kelly criterion, a measure of optimal bet size, is widely different for assumed success rates of 60, 75, and 90%. A wrapper simulation with 19 quarterly payoffs, 10000 paths, and a seed of 90604 showed a mean return of -0.32820% for MRGR and -0.26958% for MNA, with 95% confidence intervals of [-0.50152, -0.17937] and [-0.59914, 0.05320] respectively. The quarter Kelly was clipped to 0 for both. A hypothetical 25% fund allocation for 4 quarters showed terminal returns of -0.61930%, -0.31185%, and -0.03280% for MRGR and -0.88974%, -0.21603%, and 0.32215% for MNA at the 5, 50, and 95 percentiles respectively. These results cannot bound unseen crashes. The operator liquidations showed a mean return of -0.86097% and a Kelly criterion of 0, which are not natural strategy outcomes. The sources of this information are https://runway.adityaperswal.workers.dev/data/stock-bars, query_trades fundquant, https://www.txnmenergy.com/investors/acquisition.aspx, and https://www.prnewswire.com/news-releases/txnm-energy-board-declares-quarterly-common-stock-dividend-302699014.html.
The automated trading fund manager started with $235.86 in cash and no holdings or queued orders, aiming for repeatable profit. The manager's goal is bound by a constraint called net edge, which is independently validated and now distinguished from data quality. The manager has two premises: first, the daily Average Daily Range (ADR) in December 2025 underestimates the hourly range by 13.33%, 14.33%, and 20.71%; and second, the current 36-day Open-High-Low-Close-Volume (OHLCV) reconciles. The manager also fixed control hourly holdouts, which resulted in -2.42%, -13.14%, and -34.62% returns, with no eligible allocation for the next 3, 3, and 4 periods. There are three possible chains of events: Chain A, where historical range understatement leads to stop and target closer and fixed risk positions larger; Chain B, where blanket data rejection leads to valid current information being discarded and missed future opportunities; and Chain C, where using positive full period Command-Line Interface (CLI) leads to deployment without validated holdout and capital drawdown. The beneficiaries of this situation are liquidity providers and venues from turnover, and the fund from cleaner measurement, but only if the net edge eventually exists. A strong counterargument is that all three current Simple Moving Averages (SMA) are above 50 and Bitcoin (BTC) has a small positive arithmetic expectation, which could support a trend. However, the response to this is that a naive quarter Kelly strategy would result in a $2.88 return below the $10 minimum, with a negative compounded holdout and three exits, and the terminal winner cannot validate this strategy. The facts end at measurements, and the consequence chains are conditional, not causal estimates. There is no target extension for a money shortfall. The sources of this information are https://runway.adityaperswal.workers.dev/data/crypto-bars, https://data.alpaca.markets/v1beta3/crypto/us/bars, and https://api.exchange.coinbase.com/products/BTC-USD/candles.
A simulation of 100,000 paths was run with a seed of 60604 and 10 sessions. The simulation sampled two 5-day blocks from the latest 252 daily SRRK close returns, including the tails. A diagnostic exit was triggered at the first sampled close crossing +2 Average Daily Range (6.8113%) or -1 Average True Range (-3.6738%), or otherwise on day 10. Fees of 5 basis points and a slip of 10 basis points were applied to each side. This simulation does not account for continuous fills or 15-minute venue execution. An independent adverse jump of -30% was injected at a uniform day with an assumed window probability of 0%, 1%, 5%, or 10%. The results showed mean returns of 1.4974%, 1.3593%, 0.7600%, and 0.0374%, with win rates of 47.536%, 47.288%, 46.265%, and 45.027%. The 5th percentiles were -7.4857%, -7.5778%, -9.5225%, and -14.2296%. At a 10% assumed hazard, 4.603% of paths lost more than $8.5099 when hypothetically deploying the full $49.14. Even with zero-added hazard, there was an overshoot because checks were made at daily close. These results are conditional stress outputs, not forecast win probabilities or approved sizing. A 30% jump alone on $49.14 would lose $14.742, which is more than 1% of $8.5099. The full-loss cap of $8.5099 also fails whole-share affordability. The empirical momentum edge was -303, and the quarter Kelly override was negative, overriding the positive bootstrapped path mean. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=SRRK&timeframe=1Day&feed=iex&adjustment=all. The facts end at bar returns, and all hazard/jump parameters are explicit assumptions.
The manager of an automated trading fund made some notes about the stock TXNM. On September 4, the stock's price was $58.00, with a 50-day average of $57.458 and a 200-day average of $58.434. The average true range, or ATR, which measures price volatility, was $0.267 over 20 days. The manager notes that using a stop-loss order based on the ATR may not be enough to protect against a big loss if a merger deal falls apart. For example, if the stock's price were to drop suddenly, a stop-loss order at $57.733 might not be triggered in time. The manager looks at what happened to another stock, Avangrid, when a merger deal was terminated on January 2, 2024. The stock's price dropped 7.12% in one day. The manager also notes that TXNM's median daily trading volume is $46.52 million, and that buying three shares would cost around $174. If the stock's price were to drop to $50, the loss would be $24, which is above the manager's risk limit of 8.5099%. The manager also looks at another stock, MRGR, which has a higher ATR and average daily range, or ADR. The manager concludes that the risk of a big loss due to a merger deal falling apart is too high, even if the stock's price seems affordable. The manager used data from several sources, including the SEC website and a website that provides stock price data.
The performance of a merger exchange-traded fund, or ETF, is uncertain. The fund's returns were analyzed from January 2016 to December 2021 and then from January 2022 to August 2026. During the later period, the fund had a return of 20.68199%, compared to 15.04870% for another fund called MNA, 19.32620% for a fund called BIL, and 70.66035% for a fund called SPY. The merger fund's average annual return was 4.13930%, which is not much higher than the 3.88576% return of the BIL fund. To estimate the uncertainty of the fund's returns, 10,000 random samples were taken from 21-session blocks of data, which is a statistical method called bootstrapping. The results showed that the difference in returns between the merger fund and the BIL fund could be anywhere from -2.96955% to 3.59355% per year. A statistical model called ordinary least squares, or OLS, was used to analyze the relationship between the merger fund's returns and the returns of the SPY fund. The model showed that the merger fund's returns were not significantly related to the SPY fund's returns during the later period. The fund's performance was also analyzed in terms of its beta, which is a measure of its volatility, or risk, relative to the overall market. The results showed that the fund's beta was low, but this does not necessarily mean that it is not exposed to the risk of a market crash. The analysis also showed that the fund's returns were not significantly different from zero during the later period, which means that the evidence does not support the use of short-term trading strategies for this fund. The data used for the analysis came from a website called runway.adityaperswal.workers.dev, and the original motivation for the analysis came from a research paper published by the University of Chicago.
A news scan found the headline that a deep-dive analysis of NFLX data was concluded at 04UTC. The question was whether cheap tails in NFLX reflect mispricing or return and contract artifacts. The answer is that both distinct phenomena must be separated. The analysis was based on four premises: 1) executable fresh quotes and OCC-normalized deliverables; 2) independent fair value above ask plus fees and bid exit costs; 3) empirical or validated option payoff with positive small position edge; and 4) full premium less than or equal to 1% equity and cap. Premise 1 fails due to indicative feed and old series; premise 2 only holds conditionally with base winsor prices positive and all 53 affordable fails specified low vol stress; premise 3 fails due to no actual option history and planned exit MC negative; and premise 4 affordability alone can pass $3 put but does not rescue other premises. Therefore, the decision is to hold cash. The validity of this decision is based on all necessary gates under this mandate, and any failure implies no size. The soundness of this decision is supported by directly measured split quotes and conditional models. Counterarguments include a 38.03% forecast that survives audit, low vol scenario arbitrary not proved bound, winsor can remove insurance events, and physical tail proxy test mean +$7.77. The conclusion is rewritten accordingly: uncertainty and execution and base hit failure, not proven overpricing. The current history selection, IEX coverage, 3 profitable proxy windows, and posthoc event classification limit inference. Prior indicative gaps probably not executable qualitative only; no fabricated Bayesian probability. Evidence which would change size includes entitled synchronized bid ask history, verified fees and deliverables, and independent held out option payoffs with net edge confidence above zero. A rerun of the model and quarter Kelly then cap whole contracts by full premium is also considered. A $0.20 roundtrip assumed allowance and actual partner fees unverified. 1000 step American checks failed adverse case; 15 min-grid MC stop 0.01 target 0.09 has mean -0.8449/-0.9723/-1.3830 at 3 vols, quarter Kelly 0. No orders per role; none proposed. Existing monitor 30 Sep 8 13:50 UTC and job 22 already request fresh feed/model/history gates, no change before 06 UTC next run and no new duplicate job. No September dated NFLX event established; no invented catalyst monitor. Research is saturated on corporate action and model question; unresolved actual OPRA/history, exact fees, NY close closure variance, stochastic IV, and complete event classification explicitly deferred. Evidence analyses 295, 296, 299, 300, 301 and simulation/consequences records; sources include https://infomemo.theocc.com/infomemos?number=57651, https://docs.alpaca.markets/us/docs/historical-option-data, and https://runway.adityaperswal.workers.dev/data/option-chain/NFLX?feed=indicative. The fund will retain $127.65 in cash.
A corporate-action and event variance flag needs a long-leg payoff test. The premises are that a quotational transformation does not create cash, a long leg cannot lock a relative gap without a forbidden short leg, and cash must survive full premium loss. Starting with all cash at $850.99 and a mispricing of $127.65, the goal is to achieve repeatable base hits with a validated executable long option expectancy. The first-order split divides per-share stock and strike units by 10 and creates 10 contracts per old contract. Ignoring this split produces false realized volatility and vertical flags. Second-order research systems rank these flags as outliers and may overweight stale contracts. The issuer claims employee accessibility benefits, and liquidity providers can earn a spread without convergence promises. Third-order under long-only venue buying apparently cheap legs retains directional and theta risk. Five fresh indicative inversions do not prove profit, and capital can expire worthless. A separate event chain involves WBD withdrawal and termination fee changes to Netflix cash and funding needs. Competing PSKY bears its own transaction obligations. A subsequent Netflix stock jump elevates physical variance statistics, which can inflate unrelated ordinary week put valuation if modeled as stationary symmetric diffusion. This last step is inference, not proven event causality. A counterexample is that a real future jump could make a put profitable even after artifact removal, so rejection does not assert all options are fair. Until frequency and bid-ask returns are measured, the size is 0. A monthly shortfall of $349.01 changes neither payout distribution nor the $8.5099 full premium limit. There are no holdings, exits, or adjustments. Sources include https://infomemo.theocc.com/infomemos?number=57651, https://www.sec.gov/Archives/edgar/data/1065280/000106528026000212/nflx-20260630.htm, https://runway.adityaperswal.workers.dev/data/option-chain/NFLX?feed=indicative, and https://docs.alpaca.markets/us/docs/historical-option-data.
A news scan found the headline about a cheap put option for NFLX. The premises of this evaluation are to consider planned bid exits, not just the terminal fair value, and to use physical paths that are independent of the market's implied volatility, or IV, which is a measure of how much the market thinks a stock's price will move. The results are conditional until they are validated against the actual option history. The specific option being considered is the NFLX Sep 11 K72 put, which had a bid of $0.02 and an ask of $0.03 on Friday, with 4 remaining trading sessions. The mid-calibrated session IV is 32.94478%, and the rate is 4%, with q, or the dividend yield, being 0. The initial terminal 2-session geometric Brownian motion, or GBM, which is a mathematical model for stock price movement, with fixed IV and half spread, and fees of $0.20 per contract, gives a blend physical volatility of 38.03027%, with a win rate of 16.93%, an average win of $18.2765, an average loss of $2.9420, and a mean of +$0.6503. The training wins or volatility is 35.93251%, with a mean of -$0.04315, and the low volatility is 30.15233%, with a mean of -$1.54882. The positive unbounded tail mean is not a base-hit validation, which means it is not a reliable measure of success. The explicit exit diagnostic is a 1.5 average true range, or ATR, 14 decline, which is equal to 3.4575 underlying points over 2 sessions, giving a Black-Scholes model, or BSM, bid of $0.091519, with a round target down of $0.09, and a stop of $0.01 below the initial bid of $0.02. The target net profit is $5.80, versus a full premium plus assumed fees of $3.20, which gives a risk-reward ratio, or RR, of 1.8125. This is not an order. The evaluation used NumPy seed 2026090605, with 100,000 paths, and 52 equal 15-minute-like grid steps over 2/252 years, with common normal draws for all volatility scenarios. The remaining time, or T, decreases from 4/252 to 2/252, and the modeled bid is the maximum of the BSM minus $0.005, or 0. The first bid is less than or equal to $0.01 stop, or greater than or equal to $0.09 target, which exits at the modeled observed bid, while others exit at the end. The blend win rate is 16.382%, with an average win of $7.399572, an average loss of $2.460156, and a mean of -$0.844935. The stop rate is 83.514%, the target rate is 16.252%, and the time rate is 0.234%. The wins or win rate is 15.161%, with a mean of -$0.972319, and the low win rate is 11.074%, with a mean of -$1.383048. The qKelly, which is a measure of the optimal fraction of wealth to invest, is 0 for all scenarios. This evaluation does not match historical option liquidity or actual closure variance, as it uses a continuous variance grid that compresses overnight, with IV and spread fixed, and no overnight jump or stochastic IV or latency or American path model. Favorable overshoot fills are assumed, but not promised. Stops do not bound full premium risk, and one contract with $3 plus fees is affordable, but the edge is zero. The sources of this data are https://runway.adityaperswal.workers.dev/data/option-chain/NFLX?feed=indicative and https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=NFLX&timeframe=1Day&adjustment=all&feed=iex. The conclusion is to reject this option and not remove exits to accommodate an optimistic mean.
A news scan found the headline 'CLI aggregate-period control cannot validate costed holdouts'. A required run of the Command Line Interface, which is a way to interact with a computer program using text commands, used a Simple Moving Average, or SMA, of 20 and 50 days, and returned the following results: for Bitcoin, a gain of 52.1948%, a drawdown, which is a measure of loss, of 38.3724%, a win rate of 33.333%, and 12 trades; for Ethereum, a gain of 75.4890%, a drawdown of 46.9620%, a win rate of 50%, and 8 trades; and for Solana, a loss of 37.4426%, a drawdown of 67.0018%, a win rate of 37.5%, and 8 trades. The data used included 980 bars, or units of time, for Bitcoin and Ethereum, and 742 bars for Solana, with some data from September 6 being incomplete. The Localbars.js file, which is a JavaScript program, made a single page request with no end filter, and the Command Line Interface did not use an out-of-sample, or OOS, test, which is a way to evaluate a model's performance on unseen data, and did not have an explicit cost model, which is a way to account for costs. The vector, or set of numbers, for the holdout, or test, period from 306 to 308 was not numerically equivalent, meaning it did not match the expected results. The conclusion is that this is an exploratory control only, and the gross positive results for Bitcoin and Ethereum should not be selected as a validated strategy. The source of the data is https://runway.adityaperswal.workers.dev/data/crypto-bars and the code is located at /app/agent/dist/backtest/bars.js.
A news scan found the headline 'Three to four exits cannot support Kelly deployment'. The text that follows is an explanation of this headline. Premises include fixed control with hourly payoffs for Bitcoin, Ethereum, and Solana. For example, Bitcoin had a win rate of 21.3586% and a loss rate of 10.2452%. Ethereum had a win rate of 32.5817% and a loss rate of 18.3817%. Solana had a win rate of 7.9297% and a loss rate of 14.4321%. The plug-in quarter Kelly for Bitcoin had an equity of 0.33874%, but the naive notional value of $2.88 was below the $10 minimum for cryptocurrencies. The uncertainty gate was independently set to zero. Using a NumPy seed of 20260906, a 20,000 iteration bootstrap mean was calculated, resulting in 95% ranges of -14.1482% to 21.3586% for Bitcoin, -28.8701% to 32.5817% for Ethereum, and -25.2149% to 4.4192% for Solana. However, these intervals are not calibrated confidence intervals due to the small sample size. Conditional scenarios with 10% equity exposure resulted in end medians of 850.99, 852.80, 825.07, and 711.47, with loss frequencies of 48.685%, 62.845%, and 99.885%. The empirical median for Bitcoin was positive, but it could not cancel out the negative full allocation compound return or wide uncertainty. The IID assumption omits regime and serial dependence, and the forced terminal winner is not a natural exit. The 20 trades are not a monthly forecast. As a result, there is no calibrated positive edge, and the actual size is 0 before the position cap of 212.7475, fund cap of 235.86, and planned risk of 8.509. The conclusion is to preserve cash and never round up the Kelly to the venue minimum. The source of this information is https://runway.adityaperswal.workers.dev/data/crypto-bars, which was recorded on hours 306-308.
A frozen protocol note, number 294, was used to analyze data from January 2016 to December 2021, with a holdout period from January 2022 to August 2026. The analysis started with the first session of each quarter, after the prior quarter's close, and used a stop loss and target based on the prior 20 sessions' average true range, or ATR, which is a measure of price volatility. The stop loss was set at 1 times the ATR and the target was set at 1.5 times the ATR, with a maximum of 20 sessions before moving on to the next open, and no reentry was allowed in the same quarter. The analysis was long-only, meaning it only considered buying, and used cash-only transactions. The results were calculated using vectorbt, a backtesting library, and showed a net mean return of -0.009944%, an average win of 0.814326%, and an average loss of 0.609413%, with a quarter Kelly criterion of 0.000000, which is a measure of the optimal fraction of capital to allocate to a trade. The drawdown, or peak-to-trough decline, included the initial cash and session-ending marks. The analysis used daily adjusted bars, which approximate the distribution of trades and fills, but did not validate sparse trading or 15-minute stop and partial-profit execution. This analysis is an adaptation of an exit strategy, not the exact original trading strategy from the paper. The data source is https://runway.adityaperswal.workers.dev/data/stock-bars, and the local files study.py, trades.json, and metrics.json were used.
A frozen protocol note, number 294, was used to analyze data from January 2016 to December 2021, with a holdout period from January 2022 to August 2026. The analysis started with the first session of each quarter, after the previous quarter's close, and used a stop loss and target based on the average true range, or ATR, of the previous 20 sessions. The ATR is a measure of how much a stock's price moves. The stop loss was set at 1 times the ATR and the target was set at 1.5 times the ATR, with a maximum of 20 sessions. If the target or stop loss was not reached within 20 sessions, the position was closed at the next open. There was no reentry into the trade during the same quarter. The analysis was long-only, meaning only buys were considered, and used cash-only transactions. The results were checked using two different methods, vectorbt order ledger and independent compounded trade returns, which agreed with each other to within a very small margin of 1e-8. The fees were 5 basis points, or 0.05%, plus slippage of 45 basis points, or 0.45%, each way. A basis point is a unit of measurement for interest rates and fees. The analysis used gap stops at the open and ambiguous daily bars stopped at the first occurrence. There were 19 completed cohorts, but they were not independent merger deals. The training data was split into 10 and 40 variants for sensitivity checks, and the frozen 20 was used later. The results showed a net mean return of -0.965262%, an average win of 0.115454%, and an average loss of 1.092405%. The quarter Kelly, a measure of the optimal fraction of capital to allocate to a trade, was 0.000000. The drawdown, or peak-to-trough decline, included the initial cash and session-ending marks. The daily adjusted bars were used to approximate distributions and fills, but neither sparse trading nor 15-minute stop and partial-profit execution was validated. This analysis was an exit adaptation, not an exact replication of the original paper's trading strategy. The data source was https://runway.adityaperswal.workers.dev/data/stock-bars, and the local files study.py, trades.json, and metrics.json were used.
A frozen protocol note, number 294, was used to analyze data from January 2016 to December 2021, as data from 2015 was not available. The analysis also included a holdout period from January 2022 to August 2026. The strategy involved entering a position on the first session of each quarter, after the prior quarter's close, and setting a stop loss and target based on the prior 20-session average true range, or ATR, which is a measure of price volatility. The position was held for a maximum of 20 sessions, and then the next open, with no reentry allowed that quarter. The analysis was long-only, meaning it only considered buying, and used cash-only transactions. The results were verified using two different methods, vectorbt order ledger and independent compounded trade returns, which agreed within a very small margin of 1e-8. The fees were 5 basis points, or 0.05%, plus 10 basis points of slippage, which is the difference between the expected and actual price of a trade, each way. The analysis also included gap stops at the open, and ambiguous daily bars were stopped first. There were 19 completed cohorts, but these were not independent merger deals, and no unfinished endpoints were counted. The training data included 10 and 40 variants, which were sensitivity checks, and the frozen 20 was used later. The net mean return was -0.269581%, the average win was 0.552549%, and the average loss was 0.867493%. The quarter Kelly, a measure of the optimal fraction of capital to allocate to a trade, was 0.000000. The drawdown, or peak-to-trough decline, included initial cash and session-ending marks. Daily adjusted bars were used to approximate distributions and fills, but neither sparse trading nor 15-minute stop/partial-profit execution was validated. This analysis was an exit adaptation, not an exact replication of the original paper's trading strategy. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars, and the local study.py, trades.json, and metrics.json files were also used.
A frozen protocol note, number 294, was used for training. The actual training data was from January 2016 to December 2021, because data from 2015 was not available. The holdout period was from January 2022 to August 2026. The training started with the first session open after the prior quarter closed. A stop loss, called stop1ATR, and a target, called target1.5ATR, were set based on the average true range, or ATR, of the prior 20 sessions. The maximum number of sessions was 40, then the next open was used. There was no reentry in the same quarter. The training was long-only, meaning only buys were considered, and cash-only, meaning all trades were paid in cash. The vectorbt order ledger and the independent compounded trade returns agreed within a very small margin, 1e-8. The fee was 5 basis points, or bp, plus slippage of 10bp each way. A basis point is one hundredth of one percent. Gap stops were executed at the open, and ambiguous daily bars stopped first. There were 23 completed cohorts, which are groups of data, but they were not independent merger deals. No unfinished endpoints were counted. Ten out of 40 variants were used for sensitivity checks, and 20 were frozen for later use. The net mean return was 0.114560 percent, the average win was 1.332183 percent, and the average loss was 0.822073 percent. The quarter Kelly, a measure of risk, was 0.021499. Drawdown, or loss, includes the initial cash and the marks at the end of each session. Daily adjusted bars were used to approximate distributions and fills, but sparse trading and 15-minute stop or partial-profit execution were not validated. This is an adaptation of an exit strategy, not the exact original-paper trading. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars, and the local files are study.py, trades.json, and metrics.json.
A frozen protocol note, number 294, was used for training. The training data was from January 2016 to December 2021, as the requested data from 2015 was unavailable. The holdout period was from January 2022 to August 2026. The training process started with the first session open after the prior quarter's close. The stop loss and target were set based on the prior 20-session average true range, denoted as ATR20, with the stop loss set at 1 times the ATR and the target set at 1.5 times the ATR. The maximum number of sessions was 20, after which the next open was used. There was no reentry allowed in the same quarter. The training was long-only, meaning only buying was allowed, and cash-only, meaning all trades were paid in cash. The vectorbt order ledger and independent compounded trade returns agreed within a very small margin of 1e-8. A fee of 5 basis points, or 0.05%, plus slippage of 10 basis points, or 0.10%, each way was applied. Gap stops were executed at the open, and ambiguous daily bars stopped first. There were 23 completed cohorts, but they were not independent merger deals. No unfinished endpoints were counted. The training included 10 out of 40 variants as sensitivity checks, and the frozen 20 were used later. The net mean return was 0.060697%, the average win was 1.208300%, and the average loss was 0.822073%. The quarter Kelly criterion was 0.012558. The drawdown included the initial cash and session-ending marks. Daily adjusted bars were used to approximate distributions and fills, but neither sparse trading nor 15-minute stop and partial-profit execution was validated. This is an adaptation of an exit strategy, not the exact original-paper trading strategy. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars, and the local study.py, trades.json, and metrics.json files were used.
A frozen protocol note, numbered 294, was used for training. The actual training data was from January 2016 to December 2021, as the requested data from 2015 was unavailable. The holdout period was from January 2022 to August 2026. The training process involved quarterly sessions, where the first session of each quarter would open after the prior quarter's close. The stop loss and target prices were set based on the prior 20-day average true range, denoted as ATR20, with a stop loss at 1 times the ATR and a target at 1.5 times the ATR, with a maximum of 10 sessions before the next open, and no reentry was allowed in the same quarter. The training was long-only, meaning only buy positions were considered, and cash-only, meaning all positions were closed before the next quarter. The vectorbt order ledger and independent compounded trade returns agreed within a very small margin of 1e-8. A fee of 5 basis points, or 0.05%, plus slippage of 10 basis points, or 0.10%, each way, was applied. Gap stops were executed at the open, and ambiguous daily bars stopped at the first opportunity. There were 23 completed cohorts, which are not independent merger deals, and no unfinished endpoints were counted. The training included 10 out of 40 variants as sensitivity checks, and the remaining 20 were used later. The results showed a net mean return of -0.031775%, an average win of 0.995613%, and an average loss of 0.822073%. The quarter Kelly criterion was 0.000000. The drawdown included the initial cash and session-ending marks. Daily adjusted bars were used to approximate distributions and fills, but neither sparse trading nor 15-minute stop and partial-profit execution was validated. This is an adaptation of an exit strategy, not the exact original-paper trading strategy. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars, and the local study.py, trades.json, and metrics.json files were used.
A frozen protocol note, number 294, was used for actual training data from January 2016 to December 2021, as data from 2015 was unavailable. The holdout period was from January 2022 to August 2026. The strategy involved quarterly trades, with the first session open after the prior quarter's close. The stop loss was set at 1 times the average true range over 20 sessions, and the target was set at 1.5 times the average true range, with a maximum of 20 sessions before the next open, and no reentry was allowed that quarter. The strategy was long-only, and the vectorbt order ledger and independent compounded trade returns agreed within a very small margin of 1e-8. The fee was 0 basis points, plus 2 basis points of slippage each way. The strategy used gap stops at the open, and ambiguous daily bars stopped first. There were 19 completed cohorts, but these were not independent merger deals, and no unfinished endpoints were counted. The training data included 10 and 40 variants as sensitivity checks, and the frozen 20 was used later. The net mean return was -0.068713%, the average win was 0.400042%, and the average loss was 0.490593%, with a quarter Kelly of 0.000000. The drawdown included the initial cash and session-ending marks. The daily adjusted bars approximated distributions and fills, but neither sparse trading nor 15-minute stop/partial-profit execution was validated. This is an adaptation of an exit strategy, not the exact original paper trading strategy. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars, and the local study.py, trades.json, and metrics.json files were used.
A frozen protocol note, number 294, was used to analyze the MRGR merger. The actual training data used was from January 2016 to December 2021, as the requested data from 2015 was unavailable. The holdout period was from January 2022 to August 2026. The analysis was done on a quarterly basis, with the first session open after the prior quarter's close. The stop loss was set at 1 times the average true range over 20 sessions, and the target was set at 1.5 times the average true range, with a maximum of 20 sessions before the next open. There was no reentry allowed in the same quarter. The analysis was long-only, meaning only buy positions were considered, and cash-only, meaning no leverage was used. The vectorbt order ledger and independent compounded trade returns agreed within a very small margin of 1e-8. The fees were 5 basis points plus slippage of 45 basis points each way. Gap stops were set at the open, and ambiguous daily bars stopped first. There were 19 completed cohorts, but they were not independent merger deals. The training data was split into 10 and 40 variants for sensitivity checks, and the frozen 20 was used later. The net mean return was -1.023470%, the average win was 0.000000%, and the average loss was -1.023470%. The quarter Kelly was 0.000000. The drawdown included the initial cash and session-ending marks. Daily adjusted bars were used to approximate distributions and fills, but neither sparse trading nor 15-minute stop/partial-profit execution was validated. This analysis was an exit adaptation, not the exact original-paper trading. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars, and the local study.py, trades.json, and metrics.json files were used.
A news scan found the note: Frozen protocol note294. The actual training data used is from January 2016 to December 2021, as the requested 2015 data was unavailable, and the holdout period is from January 2022 to August 2026. The strategy uses a quarterly first-session open after the prior-quarter close, with a stop set at 1 times the average true range over 20 sessions, known as ATR20, and a target at 1.5 times the ATR20, with a maximum of 20 sessions before moving on to the next open, and no reentry is allowed that quarter. This is a long-only strategy, meaning it only buys assets, and it uses cash-only transactions. The vectorbt order ledger and independent compounded trade returns agree within a very small margin of 1e-8. The fee is 5 basis points, or 5bp, plus slippage of 10 basis points, or 10bp, each way. A basis point is a unit of measurement, equal to 0.01%. Gap stops occur at the open, and ambiguous daily bars stop first. There are 19 completed cohorts, which are groups of merger deals, and no unfinished endpoints are counted. The training includes 10 and 40 variants as sensitivity checks, and the frozen 20 is used later. The net mean return is -0.328197%, the average win is 0.162090%, and the average loss is 0.684770%, with a quarter Kelly of 0.000000. The drawdown includes the initial cash and session-ending marks. Daily adjusted bars are used to approximate distributions and fills, but neither sparse trading nor 15-minute stop and partial-profit execution has been validated. This is an adaptation of an exit strategy, not the exact original-paper trading strategy. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars, and the local study is based on study.py, trades.json, and metrics.json.
A frozen protocol note, numbered 294, was used for training. The actual training data used was from January 2016 to December 2021, as data from 2015 was unavailable. The holdout period was from January 2022 to August 2026. The training process involved quarterly sessions, where the first session of each quarter would open after the prior quarter's close. The stop loss and target prices were set based on the prior 20-day average true range, denoted as ATR20, with a stop loss at 1 times the ATR and a target at 1.5 times the ATR. The maximum duration for each trade was 40 sessions, after which the next open would be used, and no reentry was allowed in the same quarter. The training was long-only, meaning only buy trades were considered, and cash-only, meaning all trades were settled in cash. The vectorbt order ledger and independent compounded trade returns were compared and found to agree within a very small margin of 1e-8. A fee of 5 basis points, or 0.05%, and slippage of 10 basis points, or 0.10%, were applied to each trade. The training data included 23 completed cohorts, but these were not independent merger deals. The training process also included sensitivity checks, with 10 out of 40 variants used for this purpose, and the remaining 20 were frozen for later use. The results showed a net mean return of -0.249348%, an average win of 0.703346%, and an average loss of 0.513986%. The quarter Kelly criterion was 0.000000. The drawdown included the initial cash and session-ending marks. The daily adjusted bars used in the training were an approximation of the actual distributions and fills. It is noted that this is an exit adaptation and not the exact original-paper trading strategy. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars, and the local study.py, trades.json, and metrics.json files were used.
A frozen protocol note, number 294, was used for training. The training data was from January 2016 to December 2021, as data from 2015 was not available. The holdout period was from January 2022 to August 2026. The training process used quarterly data, where the first session of each quarter was determined by the open price after the prior quarter's close. The stop loss and target prices were set based on the prior 20-day average true range, denoted as ATR20, with a maximum of 20 sessions before moving on to the next open price, and no reentry was allowed in the same quarter. The training was long-only, meaning only buy orders were considered, and cash-only, meaning all trades were settled in cash. The vectorbt order ledger and independent compounded trade returns were in agreement, with a difference of less than 1e-8. A fee of 5 basis points, or 0.05%, and slippage of 10 basis points, or 0.10%, were applied to each trade. Gap stops were executed at the open price, and ambiguous daily bars stopped at the first opportunity. The training consisted of 23 completed cohorts, which were not independent merger deals, and did not include any unfinished endpoints. Ten out of 40 variants were used for sensitivity checks, and the remaining 20 were used later. The results showed a net mean return of -0.249348%, an average win of 0.703346%, and an average loss of 0.513986%. The quarter Kelly criterion was 0.000000. The drawdown included the initial cash and session-ending marks. Daily adjusted bars were used to approximate distributions and fills, but sparse trading and 15-minute stop/partial-profit execution were not validated. This training was an adaptation of an exit strategy, not the exact original-paper trading strategy. The data source was https://runway.adityaperswal.workers.dev/data/stock-bars, and local files study.py, trades.json, and metrics.json were used.
A frozen protocol note, number 294, was used for training. The training data used was from January 2016 to December 2021, as data from 2015 was not available. The holdout period was from January 2022 to August 2026. The training process involved quarterly sessions, where the first session of each quarter would open after the close of the previous quarter. The stop loss and target prices were set based on the average true range of the previous 20 sessions. The maximum number of sessions was 10, after which the next open would be used. There was no reentry allowed in the same quarter. The training was long-only and used cash-only transactions. The vectorbt order ledger and independent compounded trade returns were in agreement, with a difference of less than 1e-8. The fees were 5 basis points, plus a slippage of 10 basis points each way. The training data consisted of 23 completed cohorts, but these were not independent merger deals. The training process also included sensitivity checks, with 10 out of 40 variants used for training, and 20 frozen for later use. The results showed a net mean return of -0.235721%, an average win of 0.683850%, and an average loss of 0.491158%. The quarter Kelly criterion was 0.000000. The drawdown included the initial cash and session-ending marks. The daily adjusted bars were used to approximate the distributions and fills, but this method did not validate sparse trading or 15-minute stop and partial-profit execution. This training process was an adaptation of an exit strategy, not the exact original paper trading strategy. The source of the data was https://runway.adityaperswal.workers.dev/data/stock-bars, and the local files study.py, trades.json, and metrics.json were also used.
On September 4, the IEX close for SRRK was 55.305, which is below the 20-day Simple Moving Average of 55.6155, but above the 50-day Simple Moving Average of 53.4402. The 20-session return was 4.4772%. The Average Daily Range over 20 days was 1.8835, and the Average True Range over 14 days was 2.03179. The 20-day low was 50.78 and the 20-day high was 61.75. The annualized close volume over 20 days was 36.0533%. The mean volume on IEX was 68,740.65, which is a partial market. Two ADR target prices were considered, with 59.072 being the target and 50.78 being the low stop, resulting in a risk-to-reward ratio of 0.8325, which is below 1.5. A tighter stop at one ATR, or 53.2732, would be a modeled range exit, but this was not adopted as it did not demonstrate thesis invalidation. The fund has 49.14, which is not enough to buy a whole share at 55.305, and would be 12.5458% short before fees. Fractions of shares cannot be exited during extended hours under the provided venue rules. The fixed T20 current entry on September 1 has passed. Historically, the worst IEX overnight gap was -16.5104% on October 13, 2025. On September 23, the CRL announcement caused the stock to open at 29.91, which was -8.3359% from the prior close, and close at 33.77, which was +3.4937% from the open. This close-only outcome masks the intraday gap risk. These are observed feed prices, not causal CRL return estimates. There are no active holdings, and no progress or exit adjustments have been made. The source of this data is https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=SRRK&timeframe=1Day&feed=iex&adjustment=all, and the fund book and venue rules are from the September 6 run context. In conclusion, there will be zero entry due to failed edge, geometry, and execution constraints.
A news scan found the text: Premises: same stock only one completed FDA deadline; arbitrary stock windows measure background drift, not dated edge. Fixed 15 session non-overlapping windows with 16 session step, IEX 2021–Sep 4 2026, fee 5/slip 10bp each side. Train 61 windows mean 1.2841%, median -1.5474%, win 45.9016%, bootstrap 95% mean CI [-6.0859, 11.4398]%. Holdout 25 windows fully entered/exited 2025+, mean 1.6538%, median 0.8703%, win 56%, CI [-3.6993, 7.1608]%; one Dec 18–Jan 13 boundary window excluded explicitly. 20,000 iid bootstrap resamples, seed 60604; non-overlap reduces mechanical dependence but regime autocorrelation remains. Descriptive 420 daily rows 2025+: corr SRRK/XBI 0.5691, SPY 0.3380; two-factor OLS beta XBI 1.2164, SPY 0.09075, daily intercept -0.06767%, R2 0.3243. No robust SE or causal claim; XBI contains biotech exposure and factors correlated. Conclusion: rising price or positive placebo mean does not identify event premium. Need prospective independently classified manufacturing-resubmission cohort with announcements known before entry and full pipeline overlap. No posterior approval probability assigned; N1 cannot calibrate hazard. Source https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=SRRK,XBI,SPY&timeframe=1Day&feed=iex&adjustment=all. All measured rows/metrics archived.
A preregistered experiment, not ready for trading, tested an exit rule. The rule used a Simple Moving Average, a calculation to smooth out price changes, with one condition: the 20-day average must be higher than the 50-day average, based on the prior day's closing prices. The test entered a trade at the next day's opening price and exited at the next opening price when the condition was false. The test included a 0.25% fee and a 0.10% slippage, a measure of the difference between expected and actual trade prices, on each side of the trade, with double the usual cost stress. The test trained on data from January 2024 to December 2025, with SOL data starting in August 2024, and used a frozen holdout period from January 1 to September 5, 2026. The test started with no balance and ended with a forced liquidation on the last day. There were no range exits, stops, or targets, so no trade proposal was made, even if the result was positive. The test had four variants: raw daily, rebuilt daily, hourly using rebuilt signals, and hourly using raw signals. A missing hour of data made the reconstructed daily data incomplete and suppressed the 50-day filter; no data was filled in. The test found that identical signals and execution times yielded identical daily and hourly returns; only the marking of drawdown, a measure of the decline in value, differed. The raw daily holdout result was -42.07581462700807%, with 5 trades; the rebuilt and hourly result was -34.622129406621696%, with 4 trades; and the double cost result was -36.427338917791616%. The hourly training result was 6.677033703761973%, with 4 trades. The test also recorded parameter sensitivity, a measure of how the results change with different input values, for training only, with no selection of holdout data. Every 60 rows of the full cash ledger matched the compounded net capital returns, with a difference of less than 5.7e-14 percentage points. The fee-inclusive capital return replaced the vectorbt entry value-only return denominator. The terminal daily and hourly times were aligned before the final results. The test had a small sample size, a forced terminal winner, prior inspected 2026 data, imputed open fills, and unvalidated 15-minute range exit mean, with no independent edge. The BTC tiny positive plug-in Kelly, a measure of the optimal bet size, was not adopted due to a negative compounded return and uncertainty. The conclusion was to reject the new allocation, as data repair could not revive the previous ETH ratio, LTC, and funding rejections. The source of the data was https://runway.adityaperswal.workers.dev/data/crypto-bars, with direct validation at https://data.alpaca.markets/v1beta3/crypto/us/bars.