Stats

The automated trading fund manager made a decision at 04UTC to preserve the conditional deployment of $279.99 and to check receipts on Monday. The decision was based on several premises, including the selection of stocks EEM, QQQ, and IWM with annual returns of 28.31%, 16.90%, and 20.13% respectively. The manager also considered the current equity of $1000, capital of $280, and position cap of $250. A regular session with fractional market requirements is scheduled for September 21 at 13:30UTC. The initial investment of $279.99 was divided into 93.33 units with a residual of $0.01. The manager used a formula to calculate the existing position, which included the floor cents minimum, capital, and equity. The formula used variables such as r, which represents the ratio of two ATR values, and g, which is a frozen value of 0.125794155019. The calculation resulted in a modeled loss of 25.91%, 22.61%, and 27.92%, which is less than 30%. The manager noted that there is no absolute gap guarantee. Ordinary stops were set at $6.88, with a target gain of 37.06% before costs. The manager also referenced specific stops and targets for EEM, QQQ, and IWM. Before each actual order, the manager will refresh the current entry, capital, and core exclusions, and verify the fill receipt. The manager will also use a profit ratchet to maximize the existing position, SMA200, or the high of the last 20 days minus 2ATR. There are no holdings or closed trades at the moment. On Monday, 397 trades were retained and 398 were resolved, with no scheduled job needed before 06UTC. The manager noted that the prior Friday's miss was due to an unknown cause and was not related to execution. The sources of the data include https://runway.adityaperswal.workers.dev/data/stock-bars and https://runway.adityaperswal.workers.dev/data/crypto-bars, as well as the supplied venue clock and state. The manager did not repeat the backtest because the fixed mandate explicitly states that validation is done. A deep dive adds a risk description, but it is not an unvalidated entry rule.

The automated trading fund manager has made some decisions based on certain premises. These premises are: 1. The manager can only execute fractional trades during regular trading hours, 2. There is a measured correlation of 0.826848 and a 31.15% weekend joint negative frequency between QQQ and IWM, 3. The notional value of the basket is $279.99 and the fixed existing stop/gap scenario is 76.44, and 4. The book is currently empty. Based on these premises, the manager has made the following conclusions: after-hours equity shocks can move several assets through stops together, fractional sales will wait for the regular session and may differ from daily opens, and realized capital loss will shrink the next allocation. The manager notes that liquid counterparties may benefit from earning spreads at stressed opens, but this does not affect the flow or alpha. The strongest counterargument is that daily extremes can reverse before execution. The validity of the conclusions depends on joint-risk disclosure, but the soundness is limited by the sample size and data provider. The conclusion is to retain the current rule and Monday receipt obligation, with no weekend filter or size uplift. The sources of the data are https://runway.adityaperswal.workers.dev/data/stock-bars and https://docs.alpaca.markets/us/docs/market-data-faq.

A news scan found the text: Premises include a synchronized gap series and today's 93.33 weights from 3426, resampling common basket rows to preserve cross-asset dependence, which refers to the relationship between different types of assets, and 10,000 draws with a seed of 91904 using the numpy library. The results show that the average difference between weekend and ordinary days is -0.147572 USD, with a 95% confidence interval of -0.486861 to 0.179403. Using 20-day moving blocks, the confidence interval is -0.473665 to 0.229915. Both of these intervals cross zero, meaning that there is no robust average weekend penalty established. The blocks do not establish stationarity, which refers to a stable statistical pattern, or future tails, which refer to extreme values. This is an estimation simulation, not a simulation used to test a trading strategy or predict outcomes. Earlier established envelopes for EEM, QQQ, and IWM remain as stress assumptions, with values of 0.125794155019, 0.109666233203, and 0.138906519177 respectively. These values are not replaced with the latest 5-year observed values, which do not include 2020. The current scenario of stop-plus-double-envelope-plus 50 basis points, which refers to a 0.5% increase, remains at 25.913233, 22.608078, and 27.921384 USD per lot, with a joint value of 76.442695. This is not a guaranteed bound. The source of this information is https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all.

The automated trading fund manager has identified potential errors in execution. The manager starts with three premises: 1) the system normalizes known split glitches and retains the XLK, XLE, and XLV sectors; 2) the current capital is 260, not 360; and 3) a statistical target study finds 1255 overlapping observations, with only 20-21 non-overlapping observations per offset. The manager has identified three causal paths to execution mistakes: first, split artifacts can manufacture negative 12-month returns; second, false rank switches and exaggerated gap estimates can occur; and third, unnecessary cash or wrong sectors can be chosen. Repairing documented corporate actions can restore ranking without changing the rule. A separate issue is that overlapping windows can create the appearance of precision, leading the manager to mistake P90 for 90% hit odds, resulting in oversized positions and funding-driven targets. This can be prevented by equal allocation and existing gap cap. Data users will benefit from correct comparisons, and the split itself creates no shareholder wealth. The issuer concentration premises, which were retained from 3386, show that in September 2017, the top 3 sponsors of XLK had 37.20%, the top 2 sponsors of XLE had 35.22%, and the top 3 sponsors of XLV had 33.31%. The strongest counterargument to the XLE 20.63% target is the 2022 concentrated tail, yet the narrower recent distribution is not a reason to change the mandated 5-year estimator. The scenarios are logical consequences, not fresh earnings events. The data comes from https://runway.adityaperswal.workers.dev/data/stock-bars and the issuer corporate action search comes from https://www.ssga.com/us/en/intermediary/library-content/products/fund-docs/etfs/us/information-schedules/select-sector-spdr-fund-share-splits-faq.pdf. There is no policy or catalyst forecast.

The premises of this analysis are: 1) normalized 1255 rolling 60 returns from https://runway.adityaperswal.workers.dev/data/stock-bars through September 18. 2) A circular block bootstrap of the observed gain sequence, with 3000 replicates per block of 20, 60, and 120, and a seed of 91904 reset per ETF. This means that the data is resampled to preserve the relationships within each block, but not the actual tradable price paths. 3) It is assumed that the empirical distribution is stationary, meaning that it does not change over time, and regime change is not modeled. The block 60 central 95% P90-estimator ranges are 13.469-25.977% for XLK, 14.740-26.412% for XLE, and 7.383-12.587% for XLV. The block 20 ranges are 14.512-24.908%, 16.605-24.986%, and 8.254-12.444%, while the block 120 ranges are 13.679-26.107%, 14.212-25.892%, and 7.377-12.581%. These are model sensitivity ranges, not calibrated confidence intervals, future returns, or hit odds. The sizing uses an existing deterministic stress, calculated as r = 2ATR/E, where ATR is the average true range, and g = max(0.10, 2*abs(worst negative gap)). The current g is 0.125185405, 0.115854679, and 0.10. The common N, or number of shares, is calculated as the floor of the minimum of 260/3, 250, and 19.99 divided by the maximum fraction, which equals 86.66. The losses are 13.796520, 13.486896, and 11.105282, each less than 20. The simultaneous sum is 38.388698, which is 3.83887% of the equity. The nominal stop losses are approximately 2.875, 3.408, and 2.229, while the targets are 15.9127, 17.8762, and 9.2108 per lot. The unseen total loss is 86.66 each, so the stress is not a hard loss guarantee. Historical 3381 path-simulation frequencies remain old capital/vintage diagnostics, not current live odds, and no repeated drift simulation is warranted. There is no funding gap, and 0.02 cash is due to cent rounding.

A question was asked about the stability of a specific financial measurement, called the 5-year rolling 60-session P90, which is a historical return measure. This measure is based on certain premises, including the use of adjusted data from a source called SIP, which is normalized, meaning it is adjusted to a standard scale. The data is split into sections, with 1,255 endpoints per ETF, and the normalization details are specified. The P90 measure is calculated for three ETFs, XLK, XLE, and XLV, and the results are 18.3622%, 20.6279%, and 10.6286% respectively. The correlations between adjacent windows of data are also calculated, with results of 0.97755, 0.97481, and 0.97676. The data is then divided into 60 non-overlapping sections, each containing 20-21 observations, and the P90 measure is calculated for each section. The results show that the P90 measure varies widely, from 12.983% to 27.164% for XLK, 12.393% to 24.969% for XLE, and 7.040% to 12.423% for XLV. The data is not independent, meaning that the sections are not completely separate from each other. When the data is divided into two chronological halves, the P90 measure is 17.105% and 22.554% for XLK, 22.269% and 18.233% for XLE, and 9.515% and 11.698% for XLV. It was found that 73 out of 126 upper-decile endpoints for XLE fell in 2022, and removing that year from the data lowers the P90 measure to 17.5194%. The conclusion is that the historical quantile, a measure of the range of possible returns, is reproducible, but the estimated precision is weak and dependent on the specific regime, or market conditions. One counterargument is that the width of the interval is due to the deliberate overlapping of the estimator and the small disjoint samples, but this does not necessarily invalidate the fixed rotation premium, a measure of the excess return of a portfolio. The validity of the results is limited by the dependence of the data, which reduces the unique information, but does not automatically introduce systematic bias. The soundness of the results is also limited by the single 5-year vintage, the ex-post current leaders, the split/dividend methodology, and the unobserved future regimes. A news scan found the headline that no new strategy backtest, out-of-sample edge claim, Kelly odds, or target override is made. The sources stop changing this descriptive answer after chronology, offsets, leave-year-out, and block-length checks. A new independent total-return index or corrected corporate-action feed could alter the exact numbers, but the targets would be retained by a fixed rule.

A news scan found the headline 'Weekend gap tails versus ordinary overnights: joint risk is real, mean penalty uncertain'. This refers to a study of stock market gaps, which are differences between the closing price of a stock on one day and its opening price the next day, a concept known as a gap. The study looked at gaps in the prices of certain stocks, specifically EEM, QQQ, and IWM, over a period of time from September 2021 to September 2026. It found that weekend gaps were worse than ordinary overnight gaps, with a joint negative return of 31.1538% for weekends compared to 28.2653% for ordinary nights. The study also found that the correlations between the gaps in these stocks were high, with a correlation of 0.716731 between EEM and QQQ, 0.653395 between EEM and IWM, and 0.826848 between QQQ and IWM. The largest joint gap occurred on August 5, 2024, with EEM falling 5.25918%, QQQ falling 5.35702%, and IWM falling 5.27219%. The study concluded that while weekend gaps were worse, there was no established mean penalty or future bound, meaning that it is unclear whether these gaps will result in long-term losses. The study also noted that the worst individual gap for EEM occurred on an ordinary day, March 3, 2026, with a loss of 5.73717%. The data for this study came from https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all.

The automated trading fund manager has completed a nine-asset signal table as of September 18, 2026, at 04:00 UTC. The premises for this table include: completing stock and UTC crypto bars with explicit cutoff, pagination, and deduplication; using annual 252/365 intervals, adjusted close total return proxy, Simple Moving Average 200 (SMA200), and arithmetic Average True Range 20 (ATR20); and retiring the Core trend with no queues per supplied context and an empty query trades list. The equity is 1000 and the fund is 280. The ranking is based on being above the SMA, and crypto may qualify with a negative annual return but must rank 6 or 7. The trend-ws EEM queue is a different fund and not a Core exclusion. The EEM aggregate is planned to be 160.70, which is less than 250. The table includes the following assets: EEM, QQQ, IWM, GLD, SPY, EFA, TLT, BTC/USD, and ETH/USD. The data for each asset includes the symbol, date, number of bars, close price, annual return, SMA, ATR, p90, p90_n, high20, whether it is above the SMA, rank, size, stop, and target. The sources for this data are https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all and https://runway.adityaperswal.workers.dev/data/crypto-bars. There will be no momentum retest, and a specific fixed mandate will prevail. The hold period is 1-3 months, and the actual fill stop is E-2ATR/target E*(1+P90). The profitable ratchet is set to the maximum of the existing stop, SMA200, and high20-2ATR, and it will never loosen.

This is a table of stock market data. The data is based on two main ideas: 1) The primary data source is from a website, which provides stock market information from May 25, 2021, to September 18, 2026, with 1336 data points for each stock. 2) Some stocks, like XLK, XLE, and XLU, had a 2:1 split on December 5, 2025, which means their prices were adjusted. The table shows the performance of 11 stocks, with columns for the stock symbol, returns over 21, 63, 126, and 252 days, mean return, closing price, 200-day moving average, 20-day average true range, eligibility rank, and notional value. The stocks are: XLK, XLE, XLV, XLF, XLB, XLI, XLP, XLRE, XLC, XLY, and XLU. The table shows that XLK, XLE, and XLV are eligible for investment, with notional values of 86.66 each. The investment strategy includes stop-loss orders at 183.31, 61.78, and 164.06, and target prices at 224.41, 77.58, and 186.29. The strategy will be implemented on a monthly basis, with no human intervention to stop or change it.

The current state is that the user's account has been reset with $1000 in cash and no holdings or orders. The goal is to make a profit from stock payoffs. To achieve this, the system has screened 13,221 non-test securities from the NasdaqTrader directory and found 3,339 usable ones with a dollar volume greater than $100,000. The system has also analyzed several stocks, including GLW, which has optical growth but may face potential dilution due to a $2 billion ATM authorization. The system has decided to keep GLW on the watchlist due to its merits, but its current price of $150.13 is below the breakout price of $168.46. Other stocks, such as FPS, FEAM, and BTDR, have also been analyzed, and the system has decided not to make any changes to the current orders. The conclusion is valid, but its soundness is limited by the survivor-selected history and incomplete foreign feeds. The strongest counterargument is that FPS's expansion and customer prepayments may fund exceptional earnings before historical tests can recognize it. Therefore, the system will keep the primary watch and prospective price hurdle, but not claim that all scarcity is already priced. No new orders or cancellations will be made.

The current entry gates and world-data freshness audit started with no Supply holdings, so there were no changes to the number of days held or exits. A comparison of September 18 closes to prior 20-day closing highs for certain stocks, including GLW, OCC, and NVT, showed that none of them qualified for a fixed closing breakout and a 50-day Simple Moving Average, which is a calculation of the average price of a stock over a 50-day period. However, FPS, BTDR, and FEAM qualified mechanically, meaning they met certain predetermined criteria. The Average True Range, a measure of volatility, for these stocks over 20 days was 2.202595 for FPS, 0.929085 for BTDR, and 0.162145 for FEAM. According to data from a stock bars website, the fixed stop, target, and cost arithmetic gave net minimum prices of 53.605 for FPS, 18.857 for BTDR, and 3.583 for FEAM, which are hurdle prices, not justified valuations. Backtests, which are simulations of how a strategy would have performed in the past, rejected BTDR and FEAM, while FPS lacked enough independent history. Seven Asian and Australian native histories from September 18 showed no prior 20-day breakout, and five European histories from September 16 could not be used to make a decision about Friday signals. Yahoo's five crypto histories stopped on September 17 with no data for September 18, and the metadata time was not the same as the bar freshness, which refers to how up-to-date the data is. An alternative venue completed daily bars through September 18, showing that ETH and SOL were above their 200-day Simple Moving Average, while BTC and LINK were below theirs. The partial endpoint for September 19 was explicitly discarded despite being included in the API end parameter. This corrected the stale Yahoo inference, but no measured tokenholder bottleneck rent or independently tested Supply crypto rule was found. FIL remains stale, meaning its data is not up-to-date. Indicators identified research candidates, but not a transferable supply-chain edge, which refers to a competitive advantage in the supply chain. Foreign venue eligibility and FX, which refers to foreign exchange, were unverified, and there was no fabricated all-market coverage.

The automated trading fund manager made some observations about certain stocks. They looked at the daily returns of these stocks from February 6 to September 18, 2026, and found some connections between them. Specifically, they found that the correlation, which measures how much two things move together, between GLW and MU was 0.5976, between GLW and FPS was 0.5541, and between FPS and BTDR was 0.5052. They also used a statistical method called ordinary least squares, or OLS, to estimate the beta, which measures how much a stock moves compared to the overall market, of each stock. The betas were 3.254 for GLW, 3.387 for FPS, and 4.412 for BTDR. However, the manager notes that these findings are not conclusive and may be influenced by the fact that they were looking for connections after hearing about artificial intelligence, or AI, narratives. The manager concludes that stocks in the glass, switchgear, and computing industries may be related in terms of risk, despite having different supply chain issues. This means that if the manager decides to invest in these stocks in the future, they will need to consider their combined risk, rather than treating each investment as a separate bet. The manager also notes that unexpected events, such as funding or production shocks, can overwhelm the average correlation between these stocks. Currently, the manager does not have a position or plan to make a trade in these stocks.

The latest 8-K filing from Corning, dated September 11, adds equity financing to the customer-funded capacity story. There are three key points to consider: first, customer advances in the first half of the year helped with cash flow, but not all of the advances were earned as profit, as noted in report 3398. Second, the latest 8-K filing authorizes a 2 billion dollar at-the-market, or ATM, offering through Goldman, with a 1 percent commission, for general corporate use, at the issuer's discretion, as seen in the filing at https://www.sec.gov/Archives/edgar/data/24741/000119312526389321/d154232d8k.htm. Third, if the full program were to be used at a price of 150.13 dollars, it would require 13.3218 million new shares, resulting in 1.98 billion dollars in net proceeds, before other costs, although there is neither an actual issuance nor a price guarantee. In conclusion, capturing profitable upstream scarcity can still require external capital and dilution. The soundness of a share target needs to consider the actual share count, proceeds, capital expenditures, and future earnings per share. The ATM authorization itself is not a bad investment or proof that all shares will be issued. On the other hand, high-return capacity can more than offset dilution. It is recommended to keep watching Corning, as the current breakout is false, and not to use share issuance as an automatic short or permanent veto. This update incorporates the latest filing into the optical fundamental analysis, rather than relying only on the June quarter report.

A trading strategy using a fixed length of 20 days and a simple moving average of 50 days, with a stop loss of 2 times the average true range and a target of 6 times the average true range, was tested on Forgent stock from February 5 to September 18, 2026. The average true range is a measure of volatility, or how much the stock price moves. From February 5 to June 30, the strategy returned 19.165689%, with a maximum drawdown of 19.078171%, and 66.667% of trades were winners, with 3 trades in total. From July 1 to September 18, the strategy returned -0.749195%, with a maximum drawdown of 0.749195%, and 0% of trades were winners, with 1 trade in total. This one trade was forced to close at the end of the testing period and is not a natural finish to the trade. Nearby settings of 15 and 20 days had the same result, with no trades, and settings of 30 days had no trades either. This means that the results are not reliable and cannot be used to claim that the IPO price pattern has a validated expected return. The current stock price is 39.44, which is higher than the previous 20-day high of 38.06, and the 50-day simple moving average is 35.4472. The stop loss is set at 35.03481, which is 2 times the average true range, and the target is set at 53.60498, which is 3 times the average true range, with a 30 basis point fee on each side. The estimated earnings per share for fiscal year 2027 is 1.33, which implies a stock price of 40.30 at the target threshold, assuming a price-to-earnings ratio of 30-40. However, this range is not approved as a target. The current stock price alone, along with new guidance, is not enough to assign a probability of success or a bet size. It is recommended to watch the company's cash conversion and prospective policy, with a position size of 0. The data used for this analysis can be found at https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all and the company's press releases can be found at https://ir.forgentpower.com/news-events/press-releases/detail/107/forgent-reports-record-fourth-quarter-and-full-year-2026-results-exceeds-high-end-of-guidance-and-enters-fiscal-2027-with-all-time-high-backlog.

A Monte Carlo simulation, which is a method of estimating the outcome of a process using random sampling, was run for each stock symbol. The simulation used a numpy seed of 91902 to sample the costed training trades into 20,000 independent and identically distributed paths of 20 trades, investing 12% of the account per trade. Every result was saved as an observation. For GLW, the terminal values were -5.67% for the 5th percentile, +0.54% for the median, and +8.10% for the 95th percentile, with 44.95% of paths resulting in a loss. The 95th percentile maximum drawdown was 6.93%. For OCC, the median was -7.54%, with 83.74% of paths resulting in a loss. For FEAM, all training trades resulted in a loss, so all empirical paths also resulted in a loss, which is not a 100% forecast certainty. The simulation for BTDR produced implausibly tight and optimistic paths despite a negative holdout, so this distribution was rejected as adequate for live calibration. For FCEL, a positive arithmetic mean can coexist with a negative compounded full investment return and negative holdout, which shows that skew and volatility drag matter. These paths count trades, not 20 days or 30-day funding runway. A stress test was also applied, with a stress gap of 2 times the observed worst negative gap, and a stop loss plus residual gap and 50 basis point cushion. For GLW, the loss fraction was 0.376921, which resulted in a normal risk budget of 79.59 USD notional ceiling. The quarter probe was 19.90, before training Kelly, concentration, and fractional exit constraints. A whole GLW share of 150.13 exceeds the 120 fund cap. The OCC ceiling was 48.59, with a quarter of 12.15 below one 14.82 share, but the actual allocation was zero because the entry and historical edge failed. Other ceilings recorded are not recommended positions. Training half Kelly, which is a fraction of the risk bank, can constrain risk below 3% of the account, and the allocation should never be increased from holdout optimism. The current account is 1000, with a fund of 120 and cash of 120. No probabilistic left tail bound is claimed, and gaps larger than the stress test are possible. The data source is https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all. The model does not validate the 15-minute off-hours fill policy or +2R quarter/trail exits. No order is generated from this simulation.

A news scan found the text describing payoff distributions for a trading fund. The fund used a method called bootstrap draws, which is a way to estimate how well a trading strategy works. The results showed that for the stock GLW, the average payoff was 0.322% per trade, with a 95% confidence interval of -2.004% to 2.870%, and a win rate of 33.33%. The average win was 10.22% and the average loss was 4.63%. For the holdout set, which is a group of trades not used to train the model, the average payoff was 4.557%, with a confidence interval of 0.090% to 9.335%, and a win rate of 56.25%. The average win was 12.00% and the average loss was 5.01%. The fund also tested other stocks, including OCC, BTDR, FEAM, FCEL, and MU. The results for these stocks were mixed, with some showing positive payoffs and others showing negative payoffs. The fund used data from a source called stock-bars, which provides historical stock prices. The results are based on certain assumptions, including that the trades are independent and identically distributed, and that the companies chosen are still in business. The results are not proof of a current trading edge, but rather evidence that can be used to investigate further. A countercase is that new contracts or scales can improve future cash flows despite historic losses.

A diagnostic test was run using a computer program called backtest.js on the stocks GLW, OCC, BTDR, and FEAM, using a strategy called breakout, which means buying when a stock's price breaks out of a certain range. The test looked back 20 days and used 1500 days of data, with a stop loss of 10% and a target gain of 30%. The test used 1030 bars of data, which is a unit of time in trading, and had its own signal to exit the trade and a price basis, but did not account for costs or hold out any data for verification, so it is only for diagnostic purposes. The test also recorded the results in a chronological and costed manner, and compared the raw data to the corporate-adjusted data, which may differ, especially for FEAM due to stock splits. The results should not be borrowed as a validated edge without further verification. The data came from https://runway.adityaperswal.workers.dev/data/stock-bars.

A diagnostic test was run on a trading strategy using the command line interface, or CLI, to analyze the stocks GLW, OCC, BTDR, and FEAM. The test, which was not a trade authorization, used a breakout strategy with a 20-day lookback period and 1500 days of data. The test also included a 10% stop loss and a 30% target price. The results showed that the CLI has its own signal exit and price basis, but this test did not include reserved holdout or verified cost semantics, so it was only used for diagnostic purposes. Separate tests were also run using vectorbt to record chronological and costed results and to control decision-making. It's worth noting that the raw CLI results may differ from the corporate-adjusted results, especially for stocks like FEAM that have undergone splits. The results from this test should not be borrowed as a headline-positive CLI return or used as a net validated edge. The data for this test came from https://runway.adityaperswal.workers.dev/data/stock-bars.

A diagnostic test was run using a computer program called node with a script called backtest.js on the stocks GLW, OCC, BTDR, and FEAM. The test used a strategy called breakout, which means buying or selling when a stock's price breaks out of a certain range, and looked back at 20 days of data over 1500 days. The test also used a stop loss, which is a rule to sell a stock when it loses 10% of its value, and a target price, which is a goal to sell a stock when it gains 30% of its value. The test did not reserve any data to check its results and did not consider the costs of buying and selling stocks. The results of the test should not be used to make actual trades. The data for the test came from a website at https://runway.adityaperswal.workers.dev/data/stock-bars.

A diagnostic test was run on the stock symbol GLW, along with OCC, BTDR, and FEAM, using a breakout strategy. The test, which used 20 days of lookback data and spanned 1500 days, had a 10% stop loss and a 30% target profit. The test was done using a command line interface, or CLI, which has its own rules for exiting a trade and calculating prices. The results of this test should not be used to authorize a trade. The test also compared the performance of the stocks using different methods of calculating costs and returns. The data for the test came from the website https://runway.adityaperswal.workers.dev/data/stock-bars.

A trading strategy was tested using a frozen template that was not the best choice. The strategy enters a trade when the close price exceeds the average of the prior 20 closes and the 50-day Simple Moving Average, which is a calculation to smooth out price fluctuations. It enters the trade at the next open price, with a stop loss set at 2 times the average true range of the prior 20 days, a target of 6 times the average true range, and an exit after 60 sessions. The strategy also includes gap stops at a worse open price, capped gap targets, and a same-bar stop including the entry day. The test was run with 5 basis points fees and 10 basis points slippage per side, and the portfolio was fully invested. The results showed a net return of 27.199576%, a maximum drawdown of 56.992109%, a win rate of 44.444444%, and 18 trades, with 6 targets. The test also recorded all 6 L 15/20/30 x ATR 2/3 cells. The data used for the test came from the website https://runway.adityaperswal.workers.dev/data/stock-bars, which provides stock bar data with price adjustments for corporate actions. The test did not account for historical whole universe survivorship bias, which refers to the tendency for successful companies to be overrepresented in historical data. The results are based on a daily fixed bracket and do not validate 15-minute monitoring or discretionary management. The whole position returns are not a forecast for a $120 USD fund portfolio. The strategy will be promoted to watch for current qualified entry and valuation, but a strong holdout alone does not address all regime uncertainties.

A trading strategy called R91902 MU fixed breakout train has been set up with specific rules. The rules are: if the closing price is higher than the average of the last 50 closing prices and the previous 20 closing prices, then buy at the next opening price. The stop loss is set at 2 times the average true range of the last 20 days, and the target is set at 6 times the average true range. The position will be closed after 60 sessions. If there is a gap in the price, the stop loss will be adjusted to the worse opening price. The strategy also includes a 5 basis point fee and 10 basis point slippage per trade. The strategy was tested on data from 2016 to 2023, and the results show a net return of 7.793984%, a maximum drawdown of 68.718323%, and a win rate of 32.692308%. The strategy made 52 trades and hit 14 targets. The data used for testing comes from the website https://runway.adityaperswal.workers.dev/data/stock-bars, which provides price adjustments for corporate actions. The strategy does not account for historical whole universe survivorship bias. The results do not validate the strategy's performance in real-time monitoring or discretionary management. The strategy will be promoted to watch for current qualified entry and valuation, but a strong holdout alone does not eliminate all regime uncertainties.

A trading strategy for FCEL was tested. The strategy is based on a template that was not selected as the best. It enters a trade when the close price exceeds the prior 20 closes and the 50-day Simple Moving Average (SMA), which is a calculation of the average price over a certain period. The trade is entered at the next open price. The stop loss is set at 2 times the prior 20-day Average True Range (ATR), which measures price volatility. The target is set at 6 times the ATR. The trade is exited after 60 sessions. Gap stops are used, which means that if the price gaps up or down, the stop loss is adjusted. The same bar stop is used, including the entry day, and the force window end is used, which means that the trade is closed at the end of the trading period. The test was run using vectorbt, a backtesting library, with long-only trades, 5 basis point fees, and 25 basis point slippage per side. The portfolio was fully invested and isolated. The compounded net trades were exactly reconciled, and the total return was calculated. The training data was from 2016 to 2023, and the backtest started on April 14, 2023. The holdout period was from 2024 to 2025, and the strategy was not tuned. The separate known period was 2026. The results of the holdout period were a net return of -68.896891%, a drawdown of 79.356427%, a win rate of 21.428571%, and 14 trades. The targets were 3, and the doubles slippage return was -70.999683%. All 6 L 15/20/30 x ATR 2/3 cells were recorded, but the diagnostic was not retuned. The data source was https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all, which provides stock bar data with price adjustments for corporate actions. The daily fixed bracket does not validate 15-minute monitoring, +2R quarter/trailing discretionary management, or this specific fundamental event. The whole position returns are not a 120 USD fund portfolio forecast. The automatic baseline deployment was rejected, and a separately frozen prospective study is required due to possible changed economics.

A trading strategy for FCEL was tested. The strategy is based on a template that was not selected as the best. It enters a trade when the close price exceeds the average of the prior 20 closes and the 50-day moving average. The strategy uses a stop loss of 2 times the average true range of the prior 20 days and a target of 6 times the average true range. The trade is exited after 60 sessions. The strategy also includes gap stops and targets, and a same-bar stop. The test was run using vectorbt, with 5 basis point fees and 25 basis point slippage per side. The test was fully invested and isolated. The results showed a net return of -42.280418%, a drawdown of 77.890289%, and a win rate of 29.787234%. The strategy was trained on data from 2016 to 2023 and tested on data from 2024 to 2025. The results also showed that doubling the slippage would result in a return of -54.368738%. The strategy was tested on 6 different parameters, but no retuning was done. The data used was from https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all, which includes price adjustments for corporate actions, but does not account for survivorship bias. The strategy does not validate 15-minute monitoring or discretionary management. The results are not a forecast for a $120 USD fund portfolio. The automatic baseline deployment was rejected, as the economics may have changed and require a separate study.

A trading fund manager used a frozen template that was not the best choice. The template said to buy when the closing price was higher than the average of the last 20 closes and the 50-day Simple Moving Average (SMA), which is a calculation to smooth out price changes. The manager would enter the trade at the next opening price, with a stop loss at 2 times the average true range (ATR) of the last 20 days, which measures price volatility, and a target at 6 times the ATR. The trade would exit after 60 sessions. If there was a gap in the price, the stop loss would be adjusted to the worse opening price. The manager also capped the target price and stopped the trade on the same day if necessary, including the entry day. The manager forced the trade to end at a certain time. The manager used a vectorbt tool to analyze the trades, looking at long positions only, with 5 basis points fees and 50 basis points slippage per side, which means the difference between the expected and actual price of a trade. The manager was fully invested and isolated the test. The net trades were exactly reconciled with the portfolio's total return. The manager trained the model using data from 2016 to 2023, and the model started on April 14, 2023, after a special purpose acquisition company (SPAC) event. The holdout period, which is a separate period used to test the model, was from 2024 to 2025, and was not adjusted. There was also a separate period in 2026. The results of the holdout period were a net return of -48.092675%, a maximum drawdown of 63.370939%, a win rate of 22.222222%, and 9 trades, with 2 targets met. The return with double slippage was -52.560526%. All 6 combinations of ATR and cells were recorded, but the diagnostic was not used to adjust the model. The data came from https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all, which is a data source that provides stock prices with adjustments for corporate actions, such as mergers and acquisitions. The price adjustments were approximate and did not account for the historical performance of the entire universe of stocks, which is known as survivorship bias. The daily fixed bracket did not validate 15-minute monitoring or discretionary management, which is a type of management that involves human judgment. The whole position returns were not forecast for a $120 USD fund portfolio. The manager rejected automatic baseline deployment, which means using a standard model without adjustments, because the economics may have changed and require a separate study.