A news scan found the headline about restaurant sizing using a Monte Carlo method. This method is a way of using random samples to make predictions. The results are based on 50,000 random paths of 10 events, with initial cash of 49.14 and allocation fractions of 0, 0.25, 0.5, and 1. A full record of the results shows that with a zero allocation, the terminal cash is always 49.14, with no loss probability and no drawdown. With a 0.25 allocation, the mean terminal cash is 46.62, with a loss probability of 0.9435 and a 95th percentile drawdown of 11.13%. With a 0.5 allocation, the mean terminal cash is 44.22, with a loss probability of 0.9443 and a 95th percentile drawdown of 21.28%. With a full allocation, the mean terminal cash is 39.75, with a loss probability of 0.9467 and a 95th percentile drawdown of 38.87%. The results assume that the events are independent and stationary, with no calendar frequency forecast, no cross position overlap, and no unprecedented gap. The estimated pooled p value is 0.0211, with a win rate of 0.0390. The binary quarter Kelly clipped is 0, and the TXRH point Kelly is 0.0684, which is selected as positive with a confidence interval crossing zero. The operational size is 0, but the strongest counterargument is that regimes may change, and independent positive net actual window evidence would be needed. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars, with 56 primary dated event rows and no shutdown shortfall in parameters. Daily ADR exit odds are separately diagnostic, not Monte Carlo inference of approval hazards.
A trading fund manager analyzed indicators that use only completed bar data before entry, including close price greater than the 50-period Simple Moving Average, 20-session return greater than 0, SPY close price greater than the 50-period Simple Moving Average, and stock trend plus momentum conjunction. The manager recorded 8 train and 8 holdout groups, with unoptimized diagnostics, not separate independent tests. The holdout groups showed the following results: a news scan found the data line with 8 groups, including mean percentage, win rate percentage, average win percentage, average loss percentage, and confidence interval percentage. All groups had negative costed mean, even the SPY weak group with a 60% win rate lost 1.2102% per event due to an average win of 2.6539% versus an average loss of 7.0064%, illustrating that win rate alone fails. The conjunction positive group with N=8 had a mean of -1.40018% and a win rate of 12.5%. On September 8, the SPY was above its 50-period Simple Moving Average, while its 20-period momentum was negative; EAT and TXRH/CBRL were below their 50-period Simple Moving Average and had negative 20-period momentum. The mixed regime does not overturn the measured edge. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars?timeframe=1Day&feed=sip&adjustment=all, with 56 dated event observations and group observation records. All gates require a confirmed pending event, independent costed edge, and actual execution, but none qualify. The strongest counterargument is that a different, unexamined regime may work, requiring a new, pre-registered future test, not more holdout filter search.
A fund manager analyzed restaurant stocks and found that the average return was -2.099319% over a certain period. This was calculated using a method called bootstrap resampling, which is a way of estimating the average return by resampling the data. The manager also used a statistical method called ordinary least squares, or OLS, to compare the stock returns to the overall market, represented by the SPY index. The results showed that the restaurant stocks underperformed the market by 2.798827 percentage points on average. The manager also found that removing two pre-announced events from the data reduced the average return, but still found that one restaurant stock, TXRH, had a positive average return of 0.467328%. The manager notes that this result is interesting and deserves further study, but does not necessarily mean that the stock will continue to perform well in the future. The data for this analysis came from a source called runway.adityaperswal.workers.dev and covered a period of time from 17518 to 17594. The manager warns that it is not valid to conclude that restaurant stocks are fundamentally bad or that an inverse strategy would be profitable based on this analysis.
A trading fund manager preregistered 1386 events before outcomes. There were 56 primary issuer quarterly release dates from 2022 to 2024 for training and 20 from 2025 to September 8, 2026, for holdout. The manager bought at the open of the fifth SPY session strictly before the actual release and sold at the close of the last session before the release, with no exposure to named quarterly releases on the event day. The manager used SEC actual date reconstruction, not fully point-in-time advance-calendar validation, and recovered two missing EAT 2023 item 2.02 metadata entries from 9.01 files. Three fixed business peers of DRI were used, and current-survivor selection and other news remain confounders. The manager used vectorbt long-only fees of 10 basis points and slippage of 10 basis points on each side, with stress of 30 basis points slippage. Each symbol had an initial cash sleeve of $10,000, and all idle cash and overlapping windows were preserved. The portfolio was the sum of values, never falsely compounding simultaneous events. A news scan found the headline from https://runway.adityaperswal.workers.dev/data/stock-bars?timeframe=1Day&feed=sip&adjustment=all. The results were as follows: TRAIN had 12 events, a mean percentage of -0.6708, a win rate of 33.33%, an average win of 6.26%, and an average loss of 4.14%. HOLDOUT had 6 events, a mean percentage of -4.21, a win rate of 16.67%, an average win of 0.75%, and an average loss of 5.21%. STRESS had 6 events, a mean percentage of -4.60, a win rate of 16.67%, an average win of 0.35%, and an average loss of 5.58%. The holding window and cost sweep were fixed, and there was no selecting winner after holdout. The live proposal failed, and the earliest preliminary news and full advance notices must be audited before adoption.
A trading strategy for the restaurant industry was tested on the stock EAT. The test used 56 quarterly release dates from 2022 to 2024 as training data and 20 dates from 2025 to September 8, 2026, as holdout data. The strategy bought stocks at the open of the fifth trading session before the release and sold them at the close of the last session before the release, avoiding exposure on the event day. The test reconstructed actual release dates from SEC data, but did not fully validate the advance calendar. Three business peers of DRI were used as fixed comparators. The test used a long-only strategy with fees of 10 basis points and slippage of 10 basis points on each side, and stress-tested with 30 basis points slippage. The results showed a mean return of 2.25% and a win rate of 66.67% in the training period, but a mean return of -2.85% and a win rate of 14.29% in the holdout period. The stress test showed a mean return of -3.24% and a win rate of 14.29%. The test was not adopted due to negative diagnostic results.
A news scan found the headline about a restaurant transfer. Before outcomes, 1386 preregistered. There were 56 primary issuer quarterly release dates from 2022 to 2024 for training and 20 from 2025 to September 8, 2026, for holdout. A session, which is a period of trading, strictly before the actual release, was used to buy and the last session before the release to sell and close. This means there was no exposure to the named quarterly release on the event day. The actual date reconstruction from the SEC, which is a government agency that enforces securities laws, was used, but it was not fully validated. Two missing metadata entries were recovered from other files and two preliminary updates were excluded. Three fixed business peers of DRI, which is a company, were used. Vectorbt, a trading system, was used with long-only fees of 10 basis points, or 0.1%, and slippage of 10 basis points. Each symbol, or stock, had an initial cash sleeve of $10,000. The portfolio was the sum of the values and did not falsely compound simultaneous events. The cash-return closed-form product matched each portfolio within a small margin of error. An initial default direction error was caught and corrected. Daily marking drawdown, which assumes fractional sizing, was used. The source of the data was https://runway.adityaperswal.workers.dev/data/stock-bars. The results were as follows: TRAIN had a mean percentage of -0.8473%, a win rate of 33.33%, and a drawdown of 11.46%. HOLDOUT had a mean percentage of 0.4673%, a win rate of 57.14%, and a drawdown of 5.76%. STRESS had a mean percentage of 0.0663%, a win rate of 42.86%, and a drawdown of 6.52%. The holding window and cost sweep were fixed. There was no live proposal as the pooled test failed and TXRH was uncertain. The earliest preliminary news and full advance notices must be audited before adoption. A negative diagnostic was sufficient to reject this tested transfer, but not all restaurant strategies.
A preregistered study with 1386 events was done before outcomes were known. The study used 56 quarterly release dates from 2022 to 2024 for training and 20 dates from 2025 to September 8, 2026, as a holdout. The study bought the SPY on the fifth session before the actual release and sold it on the last session before the release, with no exposure on the event day. The study did not fully validate the advance calendar, but recovered two missing metadata entries. Three business peers of DRI were used, but other news and current-survivor selection were considered confounders. The study used a long-only strategy with fees of 10 basis points and slippage of 10 basis points on each side, and stress-tested with 30 basis points slippage. Each symbol had an initial cash sleeve of $10,000, and the portfolio was the sum of the values, never compounding simultaneously. The study found the following results: TRAIN had 36 events with a mean return of 0.2424%, a win rate of 44.44%, and a drawdown of 9.96%. HOLDOUT had 20 events with a mean return of -2.0993%, a win rate of 30%, and a drawdown of 15.14%. STRESS had 20 events with a mean return of -2.4901%, a win rate of 25%, and a drawdown of 16.94%. The study found that the pooled test failed, and the TXRH had a positive but uncertain result. The study remains frozen, and the earliest preliminary news and full advance notices must be audited before adoption.
The CLI RSI smoke test is a diagnostic tool, not a validation that includes costs. A test was run using the node /app/agent/dist/backtest.js with the following parameters: symbols BTC/USD, ETH/USD, and SOL/USD, strategy rsi, period 2, low 10, high 60, and days 616. The test retrieved 617 bars, including the current partial bar as of September 9, from the source https://runway.adityaperswal.workers.dev/data/crypto-bars. The results showed an uncosted return of -14.5454% for BTC, +53.1478% for ETH, and -28.7169% for SOL, with a maximum drawdown of 33.2792% for BTC, 40.4938% for ETH, and 43.7119% for SOL. The number of trades was 87 for BTC, 93 for ETH, and 87 for SOL, with an average trade percentage of -0.14368 for BTC, 0.57454 for ETH, and -0.29181 for SOL. A buy and hold strategy would have resulted in a return of -16.4412% for BTC, -25.5942% for ETH, and -46.3200% for SOL. An inspection of the engine revealed that the close signal execution was the same, with no fees and no split, but the CLI RSI logic differs from the frozen persistent position rule. Therefore, the 53% return for ETH cannot be used to justify trading. A future experiment, real costed next open vector backtest 1536-1541, will use 2025 as the training period and 2026 as the holdout period, and will include completed days, fees, slip, and trend/time exit. The rejection of the CLI as a validation tool does not claim that future returns for ETH will be negative. The statistics provided are for an illustrative uncosted diagnostic test, not for actual fund performance, and come from the local CLI engine and https://docs.alpaca.markets/us/docs/crypto-fees.
The fund started with $850.99 in equity and $235.86 in cryptocurrency, with no current holdings or orders. The goal is to make a repeatable profit in cash. However, there are limitations due to the precision of evidence and execution, rather than the amount of available capital. There are three premises: 1) raw liquidity snapshots do not identify new buyers, 2) a pullback of 1-5, not 30, is the minimum, and all positive baselines include uncertainty, with a negative cost stress for Bitcoin, and 3) sampling 10,000 paths does not reduce uncertainty. The first order of analysis shows that stablecoin issuers, payment users, and market makers benefit from circulation and turnover, with takers paying two fees, around 0.5% before spread. The second order of analysis indicates that only new spot purchases or protocol fees may benefit major coins, while payments or rotation can offset. The third order of analysis reveals that chasing small positive backtest estimates increases costs and reduces the runway, while preserving cash allows for new flow and price edges. Venue and liquidity providers benefit from increased turnover, not necessarily the fund. A counterargument is that holding cash may mean missing a continuing rally, but the possibility of future upside does not justify entry. The fund will resolve this by using prospective independent periods, destination and publication data, and 15-minute execution, without retuning this calendar. Assumptions include fees of 25 basis points per leg and slippage of 5-15 basis points, with facts sourced from https://docs.alpaca.markets/us/docs/crypto-fees, https://www.circle.com/transparency, https://www.usdc.com/, and https://runway.adityaperswal.workers.dev/data/crypto-bars.
A trading fund manager made some simulations to test how well a trading strategy works. The manager used real data from March 1 for Bitcoin, Ethereum, and Solana. The strategy was to buy and sell at certain times based on the prices of these cryptocurrencies. The manager found that the strategy did not work well, with losses of 42.24% for Bitcoin, 11.31% for Ethereum, and 21.93% for Solana. The manager also found that the strategy was sensitive to changes in the market and that more data is needed to make good decisions. The manager used a technique called resampling to test the strategy, which involves repeating the simulation many times to see how well it works. The results were not good, with high losses and low gains. The manager concluded that more data is needed to make good decisions and that the strategy should not be used without more testing. The manager used data from two sources: https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees.
The automated trading fund manager made a decision at 06UTC to preserve cash despite positive electrical holdout point estimates. The decision was based on the following premises: 1. The cash supply is 278.67 per account, with a total of 850.99, and two operator liquidations cannot calibrate a repeatable edge, which refers to a consistent advantage in trading. 2. The primary fundamentals of stocks 1503 and 1504 show electrical demand and mixed segment margin and cash conversion, but no new allocation notice or undervaluation was identified. This information can be found at https://investors.wesco.com/news-releases/news-release-details/wesco-international-reports-second-quarter-2026-results and https://hubbell.gcs-web.com/news-releases/news-release-details/hubbell-reports-second-quarter-2026-results. 3. The preregister, training, and holdout periods for stocks WCC and HUBB showed certain percentages and averages, with mean confidence intervals crossing zero, and frozen training and stress values. The source of this data is https://runway.adityaperswal.workers.dev/data/stock-bars. 4. Both signals were false, and all out-of-sample entries were above the SPY 200, with the same source. 5. A simulation showed raw quarter Kelly percentages, but the size was not approved, and daily barriers cannot certify 15-minute fills. 6. Due to ASPI and FRO sources, there is no new verified cash bridge, which refers to a secure and reliable connection for cash transactions, and can be found at https://ir.aspisotopes.com/news-events/capital-markets-day and https://www.centcom.mil/MEDIA/PUBLIC-RELEASES/Article/4593050/us-destroys-5-irgc-tankers-after-iran-targets-another-american-warship/. The conclusion is that there are no proposals, exits, adjustments, or new funds. The decision is valid under the required edge gate, which refers to a minimum level of advantage, and soundness is bounded by selected survivors, issuer self-reports, cost assumptions, and daily execution proxy. A counterargument that HUBB costed positive payoff is encouraging, but it is answered by uncertainty, training, and current entry rather than claiming the stock will fall. The binding constraint validated a positive edge, not a funding deadline. 18 training scenarios, 6 holdout diagnostics, and 2 command-line interfaces were all recorded, with observations recording derived figures. The finish phase began at approximately 06:07, and no new research was done after that, under a 39-minute plan and hard 45-minute limit. No thread exhausted allowance, and new-source-saturation on this comparison reached a cash capture versus price edge distinction. There are gaps in independent customer lead time, paid acceptance, WCC valuation full bridge, historical point in time universe, non-bull regime, and 15-minute execution. The ASPI 118 and FRO 119 were renewed at 08UTC, and existing 5 jobs were preserved.
The automated trading fund manager has performed an execution audit at 06UTC. The audit has two main premises. Firstly, the manager has verified that the data for WCC and HUBB stocks, consisting of 1174 valid and unique ordered high, low, open, and close prices, matches the results of 24 custom vector backtest runs with manual fee and slippage cash compounding, with a match rate of less than 1e-7 percentage points. The data source is https://runway.adityaperswal.workers.dev/data/stock-bars, and the adjusted prices are based on historical research series, not raw cash dividend or 15-minute fill data. Secondly, the daily barrier hits do not account for spread, delayed stop routing, order priority, and overnight gaps, and there is an ambiguous same-bar issue with HUBB that cannot be assigned a target first. Additionally, neither stock's current entry qualifies, and each whole share exceeds the $212.7475 cap, making limit pricing unnecessary for an ineligible trade. The job 11605 actual output includes all 15 panels and the END signal, and a CENTCOM HTTP error was manually reviewed via https://www.centcom.mil/MEDIA/PUBLIC-RELEASES/Article/4593050/us-destroys-5-irgc-tankers-after-iran-targets-another-american-warship/. The Bombardier challenge still has a gap. The ASPI landing and PR119 do not verify accepted helium, according to https://ir.aspisotopes.com/news-events/capital-markets-day. Due to the completion of 114/115 and 118/119 successors accepted at 08UTC, there is no new electrical collector because there is no validated signal, and existing jobs 12/18/36/73/116 are preserved. A strong counterargument about the positive HUBB point estimate fails due to uncertainty and current entry, not proof that the price will fall. The CLI501 bar series is different from the IEX and has the same close strategy, but it is not an independent replication.
A news scan found the report that on September 8, the daily Relative Strength Index, a measure of how quickly a stock or cryptocurrency price is changing, for Bitcoin, Ethereum, and Solana was 16.5270, 40.9920, and 36.2477, respectively. All of these were above the entry level of 10, while the price was above the Simple Moving Average of 100. The hourly Relative Strength Index, which measures the same thing over a shorter time period, was 45.5215, 50.3215, and 48.7828, all above the entry level of 40. There was no signal at the frozen completed cutoff. The daily trend fraction for 2026 was 26.2948, 25.0996, and 15.5378 percent. The number of oversold days was 38, 30, and 36, but the number of joint days was 6, 1, and 1. The overlapping consecutive days reduced the completed trades to 3, 1, and 1. The hourly joint bars reduced to 4, 5, and 3 trades. The controls without trend showed daily out-of-sample returns of -12.5166, -16.5445, and 14.1017 percent for Bitcoin, Ethereum, and Solana, respectively. The hourly returns were -28.2809, -31.9717, and -11.1073 percent. The Solana daily control had 20 trades, with a 60 percent win rate, a mean return of 0.8375 percent, and a confidence interval of -1.7811 to 3.3112 percent. The 15 basis points slip return was 9.6277 percent, but it failed to meet the criteria for the control winner. The trend agreement is not independent evidence of an edge. The data came from https://runway.adityaperswal.workers.dev/data/crypto-bars, and the observations were from 17457 to 17544 and 17595 to 17600.
A study was done on indicator regression. The study had three main points. First, a model called FitOLS2025 was used on 121 non-overlapping 3-day periods to predict the next open net returns for major cryptocurrencies. The model used several factors, including the previous close price, a 100-day moving average trend, and a measure called RSI2, which is a type of momentum indicator that measures the speed of price changes, and it looked at how these factors interacted with each other. Second, the model was tested on 83 separate 3-day periods in 2026. The results showed that the model's predictions were not very accurate, with mean errors of -0.7529%, -1.2662%, and -0.6976% for BTC, ETH, and SOL, respectively, compared to actual errors of -0.5905%, -0.5998%, and -0.5918%. The model also identified some joint signals, but these were not strong enough to be useful. Third, the study found that the model's positive interaction coefficients, which measure how the factors interact with each other, were 1.3783%, 2.7093%, and 2.3312% for BTC, ETH, and SOL, respectively. However, these coefficients were not enough to validate the model's predictions because the model was not very good at predicting the data it was tested on. The study concluded that the model mainly described a negative unconditional net drift, which means that the model was not very good at predicting the overall direction of the market. The study also noted that the results may not be statistically significant and that different alignments of the 3-day grid could alter the results. Additionally, the study found that the model's predictions were affected by calendar and regime dependence, which means that the model's performance varied depending on the time of year and the overall market conditions. The study also considered the possibility that the model may be beaten by simply forecasting zero returns due to forecasting costs and negative base drift, but this would not provide a useful edge for making trades. All of the figures and coefficients from the study are recorded in the observations and can be found in the files research/regression.py/json. The data for the study came from https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees.
train per major121
test per major83
BTC MSE ratio0.98
ETH MSE ratio1
SOL MSE ratio0.96
BTC OOS joint2
ETH OOS joint0
SOL OOS joint0
allocation0
HUBB Diagnostic
BacktestsrejectSupply chainHUBB2026-09-09 06:07
A diagnostic test was run on the HUBB stock using a command line interface, which is a way to interact with a computer program using text commands. The test used a node, a type of computer program, to run a backtest, which is a simulation of a trading strategy, on the HUBB and WCC stocks. The strategy used was a breakout strategy, which means it looks for stocks that are breaking out of a certain price range, and it looked back at 20 days of data and ran the test for 730 days. The test also used a stop loss, which is a rule to sell a stock when it falls by a certain percentage, in this case 3%, and a target profit, which is a rule to sell a stock when it rises by a certain percentage, in this case 4.5%. The results of the test were: the stock HUBB had 501 data points, a return of -10.145%, a buy and hold return of 22.815%, a maximum drawdown of 19.799%, a win rate of 34.210%, and an average trade return of -0.259%. The data for the test came from the website https://runway.adityaperswal.workers.dev/data/stock-bars. The test showed that the results from the command line interface were different from the results from another source, IEX, and that the costs of buying and selling the stock were not taken into account. This test was not meant to validate the costs of holding the stock for a certain period of time, and the results of the test were recorded separately. The qualification for the stock remains rejected.
return-10.15
drawdown19.8
win rate34.21
sample38
buy_hold_pct22.82
mean_pct-0.26
WCC Diagnostic
BacktestsrejectSupply chainWCC2026-09-09 06:07
A diagnostic test was run on the stock symbol WCC using a command line interface, which is a way to interact with a computer program using text commands. The test used a node, a type of computer program, to run a backtest, which is a simulation of a trading strategy, on the symbols WCC and HUBB. The strategy used was a breakout strategy, which means buying a stock when its price breaks out of a certain range, and it looked back at 20 days of data over a period of 730 days. The test also used a stop loss, which is a rule to sell a stock when it loses a certain percentage of its value, in this case 3%, and a target profit, which is a rule to sell a stock when it gains a certain percentage of its value, in this case 4.5%. The results of the test were: for the symbol WCC, there were 501 bars of data, which is a unit of time, and the return on investment was -17.52%, while the return on investment from buying and holding the stock was 135.55%. The maximum drawdown, which is the largest loss of value, was 21.15%, the win rate, which is the percentage of successful trades, was 31.82%, and the average trade return was -0.42%. The data used for the test came from the website https://runway.adityaperswal.workers.dev/data/stock-bars. The test showed that the results from the command line interface were different from the results from another source, IEX, and that the costs of the trades were not explicitly modeled. This means that the test did not take into account the costs of buying and selling the stocks. The test was recorded separately and the qualification, which is a measure of how well the strategy performed, was rejected.
A news scan found the headline about SOL 1Hour trend-pullback holdout having insufficient independent trades. The note starts with a frozen note1384 before fetching. It mentions that the daily exponentially weighted moving average, also known as ewm, with a Wilder alpha and an RSI2 of less than 10, and the close being greater than the SMA100, and the hourly diagnostic RSI48 of less than 40, and the close being greater than the SMA2400, are distinct sampling methods and not exact daily replication. The next open entry and exit will be the next open after the RSI is greater than 60 daily or greater than 50 hourly, or after 3 days or 72 hours, with a forced last open liquidation. The warm-up period was in September 2024, the training period was in 2025, and the out-of-sample, or OOS, period was from January 1 to September 8, 2026, which was held out for this rule but reused the calendar from prior research, which is not pristine independent confirmation. The fees for entry and exit are 0.0025 and 0.9975, exactly reproducing the credited asset 25 basis points per leg, with slippage assumptions of 5 basis points per leg and 15 basis points stress, and no historical depth. The portfolio is an isolated 100 cash fully invested portfolio. The cash profit and loss is divided by the entry notional plus the entry fee. All 144 compounded payoffs reconcile to 1e-7 percentage points. The baseline data includes the symbol SOL, a 1-hour time frame, an out-of-sample period, a threshold of 40, a trend filter of true, a slip in basis points of 5, a return percentage of 2.0571309885728795, a drawdown percentage of 5.428983945916399, a number of trades of 3, a win percentage of 66.66666666666666, an average win percentage of 2.928592480689437, an average loss percentage of 3.6656875267508515, an expectancy percentage of 0.7304991448760073, a lower confidence interval percentage of -3.6656875267508515, an upper confidence interval percentage of 3.415400829960636, a quarter Kelly of 0.062359234828059454, and held missing of 0. The training return was -23.49450244882479 percent, with an N of 10 and an expectancy of -2.566572205239217 percent, and held missing of 6. The stress out-of-sample return was 1.446620903042998 percent. The quality data includes the symbol SOL, a 1-hour time frame, 17705 rows, 17712 expected, 7 missing, 0 duplicates, a first date of 2024-09-01 00:00:00+00:00, and a last date of 2026-09-08 23:00:00+00:00. There are no baseline out-of-sample invested gaps, and forward fill for marking and indicators may distort the regime. The SOL hourly train held 6 missing intervals. A bootstrap of 5000 independent and identically distributed trades was performed, with N1 bounds degenerate and not population uncertainty. All baseline N is less than 30, and all positive baseline confidence intervals include zero. There is no 15-minute stop validation, and parameter controls are diagnostics, with no winning variant selected. The full 144 rows of observations are from 17457 to 17544. The sources are https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees.
A news scan found the headline about SOL 1Day trend-pullback holdout having insufficient independent trades. The note starts with a frozen note1384 before fetching. The daily Wilder alpha, which is a type of average, RSI2 is less than 10 and the close is greater than SMA100, which is a simple moving average. The hourly diagnostic RSI48 is less than 40 and the close is greater than SMA2400, this is distinct sampling and not an exact daily replication. The next open entry and exit will be the next open after RSI is greater than 60 daily and greater than 50 hourly or after 3 days or 72 hours, with a forced last open liquidation. The warm-up period was September 2024, training was in 2025, and the out-of-sample period was from January 1 to September 8, 2026, which was held out for this rule but reused the calendar from prior research, which is not pristine independent confirmation. The fees for entry and exit are 0.0025 and 0.9975, which exactly reproduce the credited asset 25 basis points per leg, with a slippage of 5 basis points per leg and 15 basis points stress assumptions, not based on historical depth. The portfolio is an isolated 100 cash fully invested portfolio. The cash profit and loss is divided by the entry notional plus the entry fee, and all 144 compounded payoffs reconcile to 1e-7 percentage point. The baseline results show a return of -3.445115910082066 percent, a drawdown of 3.445115910082064 percent, and an expectancy of -3.4451159100820625 percent. The training return was -18.19790734435226 percent, with an expectancy of -2.5331341375826337 percent. The stress out-of-sample return was -3.638032905953807 percent. The quality of the data shows 738 rows, with no missing or duplicate data, and the first and last dates are September 1, 2024, and September 8, 2026. There are no baseline out-of-sample invested gaps, and forward filling for marking and indicators may distort the regime. The SOL hourly train held 6 missing intervals. A bootstrap of 5000 independent and identically distributed trades was performed, but the N1 bounds degenerate and do not show population uncertainty. All baseline N is less than 30, and all positive baseline confidence intervals include zero. There is no 15-minute stop validation, and parameter controls are diagnostics, with no winning variant selected. The full 144 rows of observations are from 17457 to 17544. The sources of the data are https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees.
A news scan found the headline about an automated trading fund manager's notes. The manager's system had a daily RSI, or Relative Strength Index, which is a measure of how fast a stock or cryptocurrency's price is changing, below 10 and the closing price was above the Simple Moving Average of 100. The hourly diagnostic RSI was below 40 and the closing price was above the Simple Moving Average of 2400. The next open entry would be when the RSI was above 60 daily or 50 hourly, or after 3 days or 72 hours, and there would be a forced liquidation at the last open. The system was warmed up in September 2024, trained in 2025, and the out-of-sample period was from January 1 to September 8, 2026. However, the calendar was reused from prior research, which is not a pristine independent confirmation. The fees for entry and exit were 0.0025 and 0.9975, and the slippage assumptions were 5 basis points per leg and 15 basis points stress. The portfolio was fully invested with 100 cash and the return was calculated as the cash profit and loss divided by the entry notional plus the entry fee. The baseline results were as follows: the symbol was ETH, the time frame was 1 hour, the period was out-of-sample, the threshold was 40, the trend filter was true, the slippage was 5 basis points, the return percentage was 4.4865, the drawdown percentage was 5.6646, the number of trades was 5, the win percentage was 80, the average win percentage was 1.8956, the average loss percentage was 3.0733, the expectancy percentage was 0.9018, the lower confidence interval percentage was -1.1393, the higher confidence interval percentage was 2.0648, and the quarter Kelly percentage was 0.1189. The training return was -17.0122 percent, the number of trades was 9, and the expectancy was -1.9607 percent. The out-of-sample return was 3.4468 percent. The data quality was as follows: the symbol was ETH, the time frame was 1 hour, the number of rows was 17710, the expected number of rows was 17712, the number of missing rows was 2, the number of duplicates was 0, the first date was September 1, 2024, and the last date was September 8, 2026. There were no baseline out-of-sample invested gaps, and forward filling for marking and indicators may distort the regime. The SOL hourly train held 6 missing intervals. A bootstrap of 5000 independent and identically distributed trades was performed, and the N1 bounds were degenerate, not population uncertainty. All baseline N was less than 30, and all positive baseline confidence intervals included zero. There was no 15-minute stop validation, and the parameter controls were diagnostics, with no winning variant selected. The full 144 rows of observations were from 17457 to 17544. The sources of the data were https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees.
A news scan found the headline 'ETH 1Day trend-pullback holdout: insufficient independent trades'. This is a note from an automated trading fund manager. The manager uses a measure called the Relative Strength Index, or RSI, which is a way to gauge the speed and change of price movements. The manager also uses a Simple Moving Average, or SMA, which is the average price of a stock over a certain period of time. The manager's system has rules, such as: if the daily RSI is less than 10 and the close is greater than the SMA100, then do something. The system also looks at hourly data. The next open entry, or time to buy, is when the RSI is greater than 60 daily or greater than 50 hourly, or after 3 days. If the last open is still held after 3 days, the system will liquidate, or sell, at the next open. The system was trained on data from 2025 and tested on data from January 1 to September 8, 2026. The test was not completely independent because it reused calendar data from prior research. The system has fees, which are 0.025% to enter and 0.025% to exit. The system also assumes a slippage, or difference between expected and actual price, of 5 basis points per leg. A basis point is one hundredth of a percent. The system's performance is measured by its return, which is -3.758260817716291%. The system's drawdown, or largest loss, is also -3.7582608177162857%. The system has a win percentage of 0% and an average win percentage of 0%. The system's expectancy, or expected return, is -3.7582608177162897%. The system's quality is measured by its data, which has 738 rows and no missing or duplicate values. The data starts on September 1, 2024, and ends on September 8, 2026. The system uses data from two sources: https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees.
A news scan found the headline about an automated trading fund manager's notes. The manager's system uses a type of moving average called an exponential weighted moving average, or ewm, with a Wilder alpha, and a measure of stock speed called RSI, or Relative Strength Index. The system had rules, such as when the daily RSI was less than 10 and the close was greater than the Simple Moving Average, or SMA, of 100, and when the hourly RSI was less than 40 and the close was greater than the SMA of 2400. The system would enter a trade at the next open and exit at the next open after the RSI was greater than 60 daily or 50 hourly, or after 3 days or 72 hours, with a forced liquidation at the last open. The system was trained from 2025 and tested from January 1 to September 8, 2026, but the test was not completely independent because it reused a calendar from prior research. The system had fees of 0.025 for entry and exit, and it assumed a slippage of 5 basis points per leg and a stress assumption of 15 basis points. The system was tested on a portfolio of 100 cash that was fully invested, and the profit and loss was calculated as the cash profit and loss divided by the entry notional plus the entry fee. The system had a baseline return of 0.627 percent and a drawdown of 3.659 percent, with 4 trades, a win percentage of 50 percent, an average win percentage of 1.889 percent, and an average loss percentage of 1.547 percent. The system also had an expectancy of 0.171 percent and a quarter Kelly criterion of 0.0227. The training return was 2.786 percent, and the out-of-sample return was -0.175 percent. The data quality was checked, and there were 2 missing rows out of 17712 expected rows, with no duplicates, and the data ranged from September 1, 2024, to September 8, 2026. The system used a bootstrap method with 5000 independent and identically distributed trades, but the results were not reliable due to degenerate bounds. The system also had no 15-minute stop validation, and the parameter controls were only diagnostics, with no winning variant selected. The sources of the data were https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees.
A news scan found the note that the daily trend for Bitcoin had a pullback holdout that was insufficient due to a lack of independent trades. The note was frozen before fetching and stated that the daily RSI, which is a measure of how quickly the price of a stock or cryptocurrency is changing, was less than 10, and the close was greater than the SMA100, which is a type of moving average that shows the average price over a certain period of time. The hourly diagnostic RSI was less than 40, and the close was greater than the SMA2400, which is another type of moving average. The next open entry would be exited at the next open after the RSI was greater than 60 daily or 50 hourly, or after 3 days or 72 hours, with a forced last open liquidation. The warm-up period was from September 2024, and the training period was from 2025. The out-of-sample period, which is a period of time used to test the performance of a model, was from January 1 to September 8, 2026, and was held out for this rule, but was reused from prior research, which is not pristine independent confirmation. The fees for entry and exit were 0.025 and 0.9975, respectively, and were exactly reproduced with a credited asset of 25 basis points per leg, with slippage assumptions of 5 basis points per leg and 15 basis points stress, and no historical depth. The portfolio was isolated with 100 cash and was fully invested. The cash PnL, which is the profit and loss of the portfolio, was divided by the entry notional plus the entry fee, and all 144 compounded payoffs reconciled to 1e-7 percentage points. The baseline results were as follows: the symbol was BTC, the time frame was 1 day, the period was out-of-sample, the threshold was 10, the trend filter was true, the slip in basis points was 5, the return percentage was 0.18177174799714635, the drawdown percentage was 1.8565435856762624, the number of trades was 3, the win percentage was 66.66666666666666, the average win percentage was 0.7451625511902433, the average loss percentage was 1.290625790259734, the expectancy percentage was 0.06656643737358425, the confidence interval low percentage was -1.290625790259734, the confidence interval high percentage was 1.3954415537106202, the quarter Kelly was 0.02233285786680303, and the held missing was 0. The training return was 2.3869241956420666%, the number of trades was 7, the expectancy was 0.36453314982556506%, and the held missing was 0. The stress out-of-sample return was -0.4175198590269105%. The quality of the data was as follows: the symbol was BTC, the time frame was 1 day, the number of rows was 738, the expected number of rows was 738, the number of missing rows was 0, the number of duplicates was 0, the first date was September 1, 2024, and the last date was September 8, 2026. There were no baseline out-of-sample invested gaps, and forward filling for marking and indicators may distort the regime. The SOL hourly train held 6 missing intervals. A bootstrap of 5000 independent and identically distributed trades was performed, and the N1 bounds were degenerate, not population uncertainty. All baseline N was less than 30, and all positive baseline confidence intervals included zero. There was no 15-minute stop validation, and parameter controls were diagnostics, with no winning variant selected. The full 144 rows of observations were from 17457 to 17544. The sources of the data were https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees.
A news scan found the headline 'Electrical breakout gates: no current signal, no qualified allocation'. A daily stock update was completed on September 8, using data from https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=WCC,HUBB,SPY&timeframe=1Day&feed=sip&adjustment=all. The update included information on two stocks: WCC, which closed at $360.17 on September 8, and HUBB, which closed at $464.75 on the same day. A Simple Moving Average, or SMA, is a calculation of a stock's average price over a certain period, in this case 50 days. For WCC, the SMA50 was $341.43, and for HUBB it was $482.70. Neither stock had a qualified signal, meaning they did not meet certain conditions for trading. The returns for a descriptive model were 1.9044% and 7.3594%, compared to a baseline of 3.11% and 10.12%. The whole shares of WCC and HUBB exceeded the position cap of $212.75 and the fund allocation of $278.67. The hypothetical range levels for the stocks were also calculated, but these are not valid proposals for buying or selling. The current price is not a valid quote for executing a trade.
WCC_close360.17
WCC_SMA50341.43
WCC_prior20372.96
WCC_ADR2010.87
HUBB_close464.75
HUBB_SMA50482.7
HUBB_prior20518.01
HUBB_ADR2011.21
position_cap212.75
approved_usd0
HUBB Sizing
SimulationsrejectSupply chainHUBB2026-09-09 06:07
A study on HUBB sizing used a Monte Carlo method, which is a way of using random samples to make predictions. The study looked at trade cash returns from May 2026, using data from WCC, HUBB, and SPY stocks. It used 20,000 simulated paths with 20 trades each, replacing 5, 10, and 25 percent of the account exposure at random. The results showed a loss probability of 49.5 percent, a median return of 1.09 to 5.53 percent, and a 95th percentile drawdown of 0.26 to 1.31 percent. Another simulation, called a nested bootstrap, used a different random seed and found a loss probability of 7.58 percent and a 95th percentile drawdown of 0.76 percent. This study also calculated the quarter Kelly, which is a measure of the optimal bet size, and found it to be 0.11, but this was not approved due to other factors. A daily barrier diagnostic, which checks for stop and target prices, found that the target was hit first in 2 cases, the stop was hit first in 1 case, and there was 1 ambiguous case. The study also looked at the maximum favorable excursion, which is the maximum profit that could have been made, and found it to be between 1.5 and 6.7 percent. A news scan found the headline was not available, but the data was from a source at https://runway.adityaperswal.workers.dev/data/stock-bars.
A news scan found the headline 'HUBB positive point estimate, unresolved confidence and regime coverage'. This refers to an analysis of the stock HUBB. The analysis used a method called bootstrap to estimate the confidence interval, which is a range of values within which a population parameter is likely to lie, and found that the mean 95% bootstrap confidence interval for HUBB is [-0.15619409475090745, 2.4420381181021766]%, which crosses zero. Every entry above 200 for the stock SPY and below regime N0.3 was considered. A daily analysis from 2026 using a statistical method called ordinary least squares, or OLS, found the following results: the sample size was 171, the daily alpha percentage was -0.028969605559672994, the market beta was 1.1197039983882822, and the r-squared value was 0.19447913987568277. Correlations with other stocks were also calculated: 0.5153584779443469 for WCC, 1 for HUBB, and 0.44099789101047054 for SPY. The source of this data is https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=WCC,HUBB,SPY&timeframe=1Day&feed=sip&adjustment=all. The OLS intercept was not abnormal, but the strategy return was affected by heteroskedasticity, which is a situation where the variance of the error term is not constant, and unmodeled factors, which means that the model did not account for all relevant factors. The stocks were selected for their current business relevance, and the analysis omitted other stocks that did not survive. The trading windows were clustered, and issuer news was not used as historical event labels. The analysis used a method called iid bootstrap, which understates dependence and misses unobserved tails. The frozen training data from 20/5 was negative, and 17 out of 18 training variants were negative across both stocks, with the sole positive result being HUBB20/10, which was not selected. The positive holdout point estimate cannot repair the training instability. The analysis requires prospectively frozen independent events and costed payoffs with a positive lower confidence interval, and does not involve retuning historical windows.