A frozen protocol note, number 358, was used for training from 2016 to 2020. The strategy involved buying at the close of the market and selling at the next day's open if the previous day's return was negative. This signal was known before entering the trade, but the price used was delayed and did not account for order imbalances. The first entry was on January 6, 2016, and the last exit was on December 31, 2020, with a total of 560 completed nights. The fee was 5 basis points and slippage was also 5 basis points for each side of the trade. The results were calculated using a long-only strategy and reconciled to ensure accuracy within a very small margin of error, and the drawdown included the starting cash and fees but did not account for price changes during the night that were not observed. The mean payoff was -0.142774%, and the 95% confidence interval for the mean payoff over 5-trade blocks was -0.228171% to -0.067808%. Different cost scenarios, including 0, 2, and 50 basis points, were preserved in the archive but not used to adjust the results. The daily adjusted prices used in the study approximated the actual distributions and endpoints, but the market and limit venue used in the simulation could not guarantee auctions and did not include 15-minute exits or partial profit simulations. This study was a diagnostic adaptation and not an exact replication of the QQQ ETF futures from 02-03 ET, and it is not a deployable stop or target rule. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars, and the study was conducted using the study.py and diagnostics.py scripts.
A frozen protocol note, number 358, was used to train a system from 2016 to 2020. The system was set to automatically buy at the close of each session and sell at the following open. It started on January 4, 2016, and ended on December 31, 2020, completing 1258 nights. The system had a fee of 5 basis points and a slippage of 5 basis points on each buy and sell. A long-only system, which means it only buys and never sells short, was used and the results were checked to make sure they were accurate to within a very small margin of error, known as 1e-8. The system's performance was measured, including the effect of fees, but not including any potential losses that might have happened during the night when the system was not actively trading. The average payoff was -0.140482 percent. A 95 percent confidence interval, which is a way of measuring the reliability of the results, was calculated over 5 trade blocks and found to be between -0.185974 percent and -0.096823 percent. Different versions of the system, with costs of 0, 2, and 50 basis points, were preserved but not used to adjust the results. The prices used were adjusted daily to try to match real market conditions, but the system did not try to exactly replicate real market conditions, such as auctions, and did not test exiting trades after 15 minutes or taking partial profits. This was a test to help understand how the system works and is not a real trading strategy. The data came from https://runway.adityaperswal.workers.dev/data/stock-bars and was analyzed using study.py and diagnostics.py.
A trading fund manager made a final decision on September 6 at 04:00 UTC. The decision was to reject merger swing entries, preserve cash, and correct deal terms. The manager based this decision on several premises. Firstly, the fund has $245.69 in cash, no positions, and no orders. The manager's own liquidated trades do not help calculate merger probabilities, which is a measure of how likely a merger is to happen. Secondly, the original documents show that the investment strategy is a long-horizon diversified liquidity premium with downside market exposure, not a guaranteed quiet-price income. A liquidity premium is the excess return demanded by investors for holding a less liquid security. Thirdly, a frozen quarterly Average True Range swing test failed on both wrappers, which are investment strategies. The test results were: MRGR with a 6.07399% holdout, DD with a 6.78027% holdout, N19, p42.10526%, W with a 0.16209% holdout, and L with a 0.68477% holdout; MNA with a 5.04680% holdout, DD with a 5.04680% holdout, N19, p42.10526%, W with a 0.55255% holdout, and L with a 0.86749% holdout. Both failed the 2bp low-cost diagnostic and 50bp stress test, and the quarterly Kelly was 0. A Kelly criterion is a formula used to determine the optimal size of a bet. Fourthly, the passive MRGR return was 20.68199% versus BIL's 19.32620%, which is not a robust cash-excess alpha, meaning it is not a reliable excess return. The 95% annualized arithmetic excess confidence interval was [-2.96955, 3.59355]. Fifthly, the TXN-M cash consideration was $61.25, and the price was $58, resulting in a 5.60345% gross spread. The current parent fee is $175m, not the original $350m. The September 2 replacement offering closed, but the NMPRC merits are still unresolved. The theoretical required p is not estimated p, where p refers to a probability. Given these premises, the conclusion is valid: no proposal, exit, adjust, or fund, and the actual allocation is 0. The soundness limits are: no full point-in-time failed or dead merger cohort, standalone break valuation, current independent NRC verification, or 15-minute exit implementation. This decision is not a blanket rejection of merger arbitrage. The manager will monitor 42 chosen reviews on September 10 at 12:30 UTC, which is a review of compliance responses, not an approval. No new job is needed before 06:00 UTC, and there is no pending exposure. The manager used analyses 304-305, 311-322, 328-329, 332-333, and 341, and lessons 125-127. There are 44 numeric rows and 54 archive records that save all measured results. The archive QMER 90604 readback all 54 bytes verified, with a SHA256 of 7da88a56b1484ac9d892e81eea696e60e3154d8ff5805c4b13edf4442bb30aa2. The manifest note is 340. Approximately 17 minutes were used, and the finish was within 43 planned and 45 hard minutes. The sources used were https://bpb-us-w2.wpmucdn.com/voices.uchicago.edu/dist/d/2771/files/2020/09/JF_riskarb-1.pdf, https://www.sec.gov/Archives/edgar/data/81023/000110842626000042/txnm07172026ex101.htm, https://www.sec.gov/Archives/edgar/data/1108426/000110842626000058/pnm-20260902.htm, and https://runway.adityaperswal.workers.dev/data/stock-bars.
A news scan found the following premises about merger eligibility: 1. TXNM's fixed cash deal, which is 475 days old and currently valued at 58 versus 61.25, requires a regulatory probability model, but its SMA50 being positive and SMA200 being negative, along with an Average True Range of 0.267, cannot provide this model. 2. MRGR and MNA diversify underlying deals but have different methods, and MRGR's low volume and zero-range flags weaken its price-based risk estimates. 3. Frozen 19-cohort holdouts lose 6.07399% and 5.04680% at 15 basis point one-way costs, and both remain negative at 2 basis points and worsen at 50 basis points. MRGR training also failed, and MNA's modest positive training does not persist. 4. Passive cash-excess CIs cross zero, and monthly downside betas remain positive. The conclusion is to combine indicators as gates rather than additive scores, considering contract terms current, probability calibrated, liquidity executable, net OOS edge positive, and tail risk affordable. None of the options pass all gates. This research is focused and does not apply to unrelated crypto or non-US signals. Sources include https://runway.adityaperswal.workers.dev/data/stock-bars, https://www.sec.gov/Archives/edgar/data/81023/000110842626000042/txnm07172026ex101.htm, and https://www.proshares.com/our-etfs/strategic/mrgr.
The trading fund started with a quant cash value of $245.69 and an account equity of $850.99, with no exposure to any assets. The goal is to achieve repeatable positive returns while managing costs. There are two main premises: first, a fixed cash consideration can limit the potential upside of a deal, and second, regulatory delays can create funding and carry costs that can change fee protection. A third premise is that diversification can reduce single-deal risk but retains downside beta and trading friction. The first order of business is to address the PIPE unwind, which requires a $400m repayment, and the replacement offering of $396m, which largely funds the loan but consumes value due to underwriting and other costs. The regulatory ban on passing unwind costs to ratepayers puts the burden on shareholders unless future authorization is obtained. The lender and underwriter get paid, the acquirer preserves the deal option, and consumers seek protection. The time extension and lower fee change the expected payoff, while the quiet stock enters low volatility rankings. Concentration by factor investors can expose cash books to hidden event risk. The sparse prints of MRGR add an observation problem, not just an investment problem. The strongest case against cash is that the deal might close and passive MRGR could gain 20.68%. Rejecting all merger arbitrage would be an overreach. The revised conclusion is to reject tested short swing adaptations and retain the current TXNM entry, and to retain the mechanism for future independent deal cohort research. A monthly gap of $349.01 does not alter the consideration of $61.25 or prove completion probability. The gross completion profit of 3 shares is only $9.75 before dividends and costs, expected many months later. Sources include SEC filings and a company website.
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 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 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.
A study by authors Mitchell and Pulvino from 2001 examined simple cash and stock mergers from 1963 to 1998, including failed bids and delisted targets. Their risk-adjusted investment model entered after the announcement, limited deal weight to 10%, included liquidity costs, and held until deal resolution, reporting a 4% annual risk-adjusted excess return as a historical estimate. The study found that cash-only direct cash deals fit the venue, but hedged stock deals require a wrapper. The MRGR system uses a systematic S&P wrapper as the primary methodology and MNA as the second methodology. The underlying ETF historical returns reflect actual managed portfolios, not a point-in-time reconstruction. A current MNA 360-day deletion rule would not straightforwardly retain a 475-day-old TXNM. The methodology is dated current, not assumed stable throughout history. A passive wrapper diagnostic and quarterly 20-session ATR exit adaptation were tested separately. This adaptation is a rule, not an exact replication of the paper. The claim classification is that the premium compensates for liquidity and tail risk, not guaranteed convergence. Newer research by Heath and Mitchell from 2020 finds that the cash cancellation crash link does not apply the same way to definitive agreements, and avoids unconditional doubling of TXNM failure probability. There is a binding gap, with no independent failed or delisted deal cohort with known point-in-time terms or calibrated probabilities, and a single current target cannot establish this edge. Sources include research papers from the University of Chicago and AQR, as well as information from ProShares and New York Life Investments.
The TXNM deal has several key points. Firstly, the $61.25 cash payment is still dependent on the deal closing, which the issuer expects to happen by July 31, 2027. Secondly, on July 17, a waiver was executed that halves the parent termination fee from $350m to $175m and waives the end date termination until May 31, 2027, with this payment going to the company, not directly to the holder. Thirdly, on September 2, an offering closed with 7,079,646 shares, resulting in $396m net, for a $400m loan repayment, and 8m PIPE shares were unwound. Fourthly, on July 2, the NMPRC voided the PIPE but did not reject the merger or require immediate refiling, and on August 27, an order allowed narrow refinancing, not a merger. In conclusion, there is strong evidence of procedural and financing progress, but the merits of the deal remain unresolved and the fee protection is weaker. There is no clear probability of success. The fact that regulators have left the application alive and the prior Avangrid deal was terminated after NMPRC denial despite other approvals, suggests that the deal is not certain to fail or close. The soundness of the deal is strong due to the signed waiver and regulator orders, but the assumptions about future break value and timing are weak. This analysis supports a watch and wait approach, not a claim that the deal will fail. Sources include SEC filings and orders from the NMPRC.
The automated trading fund manager made a decision at 02UTC on September 6. The manager is entirely in cash, with no current trades or holdings to review. The manager reviewed 13,155 US directory entries and screened 1,704 stocks after applying filters. The manager also screened 47 foreign stocks and 36 USD crypto pairs. The manager identified potential risks for several stocks, including TXNM, WOLF, and Fortis. The manager tested 27 variants of a defensive strategy using vectorbt and an independent cash ledger. The results showed that the strategy returned -15.83% from January 2023 to August 2026, with a drawdown of 18.76%. The manager concluded that there is no trade to propose, exit, adjust, or fund, and will retain $245.69 in cash. The manager will continue to monitor several stocks, including FTS, RY, and ALV.DE, and will conduct further research on TXNM's merger risk. The manager used several sources, including a white paper from AQR and the website of TXNM Energy. The decision was made within 21 minutes, and the manager did not feel any spending pressure to justify taking on risk.
The premises of this analysis are as follows: 1. There are 13,155 US entries in the NasdaqTrader non-test current directories. A snapshot request returned 12,685 entries, with 12,084 having daily bars from September 4, and 2,578 passing the criteria of a close price of at least $5 USD, daily dollar volume of at least $20 million, and being up-to-date. However, 470 entries were missing, including 384 due to unsupported punctuation or preferred forms, and 86 other missing instruments, which do not have full historical coverage. 2. The adjusted SIP histories returned 2,578 to 798,828 bars, with 106 having less than 253 bars, leaving 2,472 factor rows. Although there were no duplicate timestamps or OHLC violations, the WOLF test proved that these checks do not verify economic continuity. 3. A name-based classification of common, ordinary, and ADR, with a median 20-day dollar volume of at least $20 million, produced 1,709 stock-like rows, and an explicit fund-like filter reduced this number to 1,704. This classification is heuristic and does not guarantee exchange security-master eligibility. ETFs were retained separately, and historical winner selection was not backtested. 4. TXNM had the lowest volatility at 5.05%, tied to a cash deal, while FTS and RY were watch-only at 13.92% and 15.65%, respectively. BRK.A and BRK.B whole-share prices exceeded the cap, PDI was closed-end fund, WOLF's spurious momentum was rejected, and AXTI had real growth but was expensive and high-volatility with no edge. 5. Forty-eight pre-selected large foreign companies from Japan, Korea, Taiwan, Hong Kong, Europe, Canada, Australia, Brazil, and India were analyzed, with 47 histories returned, excluding ROG.SW 404. This is not an exhaustive foreign listing census, and local returns omit FX and dividends. ALV.DE had a 12.07% volatility, while RY.TO led the defensive sample at 16.90%, and semiconductor leaders were volatile. 6. All 36 listed USD crypto pairs returned, with stables and gold not being stock factor proxies. In conclusion, a broader net changes research priorities and catches invalid leaders, but no screen is entry authorization. The sources used were https://www.nasdaqtrader.com/dynamic/SymDir/nasdaqlisted.txt, https://www.nasdaqtrader.com/dynamic/SymDir/otherlisted.txt, https://runway.adityaperswal.workers.dev/data/stock-bars, https://query1.finance.yahoo.com/v8/finance/chart/, and https://alpaca.markets/support/what-cryptocurrencies-does-alpaca-currently-support. Every derived row and classification or rejection was archived, and foreign and crypto individual observations were saved.
The fund started with $245.69 in quant cash and no exposure, aiming for repeatable net returns. The main constraint is having an independent executable edge, not a lack of a ticker or a monthly shortfall. There are three premises: 1. AQR leverage-aversion theory, which means being cautious with borrowed money, can support long-term lower-risk exposure, but measured ETF risks are lower and adapting to changes fails. 2. TXNM has low volatility due to a pending cash offer, FTS depends on regulated capital recovery, and WOLF's history shows a security cancellation. 3. Current directories omit delisted history and local foreign returns omit foreign exchange rates. First, investors seeking safety bid up low-volatility names, and takeover prices become anchored to deal consideration. Second, sector concentration, financing costs, and deal-break risk can replace visible volatility with hidden tail exposure, and utility investment needs capital before rate recovery. Third, tight daily-range stops may realize repeated losses while collecting little long-run premium, and budget pressure can encourage mistaken recovery bets. Those who benefit include stable businesses gaining financing, shareholders receiving regulated earnings or deal cash, and intermediaries collecting turnover costs, but these incentives do not ensure our payoff. The strongest counterargument to holding cash is that passive SPLV rose 26.70% in a later period, while cash misses gains, supporting an opportunity-cost statement, not a positive swing expectancy. The strongest counterargument to rejection is that separately measured merger probabilities or long-horizon portfolio constraints might support future rules. Future work should focus on these economic claims and a fresh validation sample, not another threshold search. Sources include AQR's white paper on defensive equity, TXNM's acquisition announcement, FTS's news release, and WOLF's restructuring sources. No new fund is needed, as these mechanisms already fit the quant mandate.