This is a frozen note about the QLV90802 strategy, which is a type of automated trading system. The strategy was trained on data from 2016 to 2020, and then tested on new data from 2021 to 2025, as well as on completed data from 2026. The strategy buys a stock when the first session open of the month is higher than the 100-day moving average, which is a calculation of the average price of a stock over the past 100 days. The strategy then sets a stop loss, which is a price at which the stock will be sold to limit losses, at one average true range, which is a measure of the volatility of a stock. The target price, which is the price at which the stock will be sold for a profit, is set at 1.5 average true ranges. The strategy also has a time exit, which means that the stock will be sold after 10 sessions, regardless of whether the target price has been reached. The strategy does not allow for partial profits, which means that the entire position will be sold at once. The strategy also has a gap stop, which means that if the stock gaps up or down, the stop loss will be adjusted accordingly. All calculations are adjusted for dividends and splits, and a 5 basis point fee and 5 basis point slippage are added to each fill, which is a trade. The strategy uses fractional continuous sizing, which means that the position size will be adjusted based on the volatility of the stock, and starts with an initial cash position of $100. The strategy does not use leverage, which means that it does not borrow money to buy stocks. The results of the strategy were verified using a tool called Vectorbt, which reconstructs the exact fills, and the results were found to be accurate within 3.73e-13 percentage points. The drawdown, which is the maximum loss of the strategy, was calculated using synthetic per-day fill/close slots, rather than the actual 15-minute path. The positions were normalized to full allocation for comparison, but this is not a proposal for actual investment. The strategy was tested using a bootstrap, which is a statistical method, with 10,000 iterations and a seed of 90802, and the results showed a 95% mean interval of [-0.750248, -0.081568]%. The historical profit/loss and win/loss ratios were calculated, but failures in the training and holdout periods were kept at zero, even if the strategy had a positive return in 2026. The data used for this strategy was sourced from https://runway.adityaperswal.workers.dev/data/stock-bars, and the author's paper can be found at https://pages.stern.nyu.edu/~afrazzin/pdf/Betting%20Against%20Beta%20-%20Frazzini%20and%20Pedersen.pdf. This strategy is not an exact leveraged betting against beta strategy, and the metrics were calculated using a deterministic study and local ledgers.
This is a frozen note about the QLV90802 SPLV train. The training data is from 2016 to 2020, and the holdout period is from 2021 to 2025, but the market period from 2013 to 2015 was also historically inspected. The training was completed in 2026. The strategy is to buy on the first session open of each month if it is above the 100-day moving average, which is a calculation of the average price of a stock over the past 100 days. The stop loss is set at 1 average true range, which is a measure of the volatility of a stock, and the target is set at 1.5 average true ranges. The time exit is set at the 10th session close. There is an unmanaged parent strategy for comparison, which means it is not actively managed, and there is no partial profit taking. The gap stop open and stop first rules are used to handle ambiguous situations, and the target is capped. All adjustments are made daily, and there is a 5 basis point fee and 5 basis point slippage for each fill, which means a small cost is added to each trade. The sizing is fractional and continuous, and the initial cash is $100. There is only one position, and there is no leverage, which means no borrowed money is used. The vectorbt exact fill reconstruction agrees with independent compounding within 5.04e-13 percentage points, which is a very small difference. The drawdown uses synthetic per day fill and close slots, not the actual 15-minute path, and positions are normalized for full allocation for comparison, but not for proposal. The IID trade bootstrap with 10000 seed 90802 has a 95% mean interval of [-0.331797, 0.227813]%. The historical profit and loss sizing is only used for training, and failures in training and holdout keep the actual value at 0, even if there is a positive counterexample in 2026. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars, and the author paper is https://pages.stern.nyu.edu/~afrazzin/pdf/Betting%20Against%20Beta%20-%20Frazzini%20and%20Pedersen.pdf. This strategy is not an exact leveraged betting against beta strategy. There are 252 metrics observations for QLV90802 metrics, and the study is deterministic, using the ledgers local in the study.py file.
A news scan found the research results from Yahoo charts at https://query1.finance.yahoo.com/v8/finance/chart/EGTX.ST, which included data for ABVX, 4431.T, 0513.HK, BTC-USD, ETH-USD, and SOL-USD. The research excluded ongoing data from September 8. It showed that EGTX's last valid data on September 4 was SEK 7.70, above the 50-day Simple Moving Average of 6.751, with a 20-day increase of 8.757%. The SMA for the region on September 7 was JPY 3485, above 3169.76, with an increase of 8.906%. ABVX's data on September 4 was USD 114.79, below 125.153, with a decrease of 12.327%. The data for 0513.HK on September 7 was HKD 0.13, above 0.1148, with an increase of 8.333%, but the date was unverified. The cryptocurrency data was incomplete, with the latest complete data available from September 6, showing 20-day returns of 24.562% for BTC, 31.500% for ETH, and 40.166% for SOL, all below their respective 50-day Simple Moving Averages. The premises of the research were that trend alone is descriptive, exact date and venue, and net event payoff gates fail independently, and the conclusion was no signal adoption. The rates were based on 20 observed rows, not assumed 20 calendar days, and foreign FX and different holidays precluded naive ranking. Countercase momentum could continue through the calendar, but requires a separate costed actual window test, and it is not possible to borrow an unrelated cryptocurrency fund edge. No positive payoff probability was assigned.
A screen of US, foreign, and crypto calendars was performed on September 8 at 02UTC. The screen downloaded two Nasdaq Trader directories, which included 13,155 non-test instruments, of which 7,499 were non-ETF, and included ADRs, warrants, and preferreds, but not 13,155 operating companies. The screen also joined 206 unique leads from the past 4 weeks from the earnings calendar at https://www.earningstoday.com/earnings-calendar/weekly/2026-09-14, and all of them matched. However, the Nasdaq API requests timed out, so exhaustive primary event coverage was not achieved. The screen also reviewed 17 global weekday first pages at https://www.rttnews.com/Calendar/Earnings.aspx?Date=14-Sep-2026, which yielded 419 rows and 391 foreign symbols, but had off-request dates and pagination limits. The primary top checks included PLAYS on September 14 after close, which was verified, and estimated on September 21, which was wrong. The screen also checked EGTX.ST on September 28 for an FDA event, which had an exact date but unknown funding, early binary, venue, FX, and edge. ABVX on September 21 had a confirmed date but unknown event edge. Smaregi 4431.T on September 14 had a confirmed FY27 Q1 event, but unknown venue, lot, FX, and net edge. CAN had a crypto-linked earnings event on September 8 before open, which left 0 regular sessions. The screen did not promote 0513.HK, EBF, and KBH due to failed current date confirmation. The BTC, ETH, and SOL descriptive history did not establish dated earnings or FDA transmission edge. ETH had an ACDC 186 event on September 3, which moved the Sepolia testnet to October 6, but had no exact mainnet date. The premises of the screen were exact date, valid payoff, and feasible execution, and the conclusion was a new watchlist with zero orders. The validity of the cash decision does not mean all 206 opportunities are bad. The selection bias and missing coverage are explicit, and primary issuer optimism is not independent approval proof. The five confirmed new dates mean a new watchlist for this run, not new public announcements.
A news scan found the headline about a trading strategy for Bitcoin. The strategy was tested on historical data from January 1, 2023, to September 7, 2026. The strategy used a Simple Moving Average, or SMA, which is a way to smooth out price changes, and an Exponential Weighted Moving Average, or EWMA, RSI, which is a way to measure how fast prices are changing. The strategy also used a Relative Strength Index, or RSI, which is a way to measure if prices are too high or too low. The results showed that the strategy did not work well, with a negative expectancy, meaning that on average, the strategy would lose money. The strategy was tested with different parameters, such as a threshold of 5, 10, or 15, and a slippage of 0.001 or 0.003, which is a way to measure the cost of buying or selling. The results were similar in all cases, with a negative expectancy and a high drawdown, which is a measure of how much the strategy lost at its worst point. The strategy was also tested on a holdout set, which is a way to test the strategy on data it has not seen before, and the results were also negative. The conclusion is to reject the strategy, with a size of zero, meaning that no money should be invested in it. The sources of the data are https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees. The code used to test the strategy is in the files test_rule.py, results_1Hour.json, and audit.py.
A news scan found information about three companies: PLAY, Egetis, and Canaan. Facts about these companies include: PLAY's Q1 cash and debt information is available at https://www.sec.gov/Archives/edgar/data/1525769/000152576926000026/play-20260505.htm; Egetis' September 28 FDA update and 350M SEK raise is at https://www.egetis.com/mfn_news/egetis-interim-report-q2-2026/; and Canaan's September 8 financial results are at https://investor.canaan-creative.com/news-releases/news-release-details/canaan-inc-report-second-quarter-2026-financial-results. There are conditional chains, not forecasts, for these companies. For PLAY, the bear chain is: 1, lower capex and sale-leasebacks increase current reported cash; 2, deferred refresh and fixed future lease costs could pressure guest demand or coverage; 3, equity then bears residual financing risk while landlords and lenders receive contractual cash. The bull counter chain is: 1, same-store sales recover; 2, operating leverage expands organic free cash flow; 3, debt paydown reduces interest and improves equity optionality. There is no observed Q2 proof. For EGTX, the chain is: 1, approval could open US paid access and conditional voucher; 2, distribution, reimbursement, and launch spend determine cash conversion; 3, greater cash may reduce equity issuance. A clinical trial delay instead consumes funding and increases dilution risk. Optimistic sponsor language benefits financing, not independent FDA confirmation. For CAN, the chain is: 1, hardware and mine economics reveal enterprise conditions; 2, miner orders affect equipment margin; 3, inference from one small issuer to BTC aggregate price requires market share and independent transmission evidence, which is absent here. There is no direct crypto run-up edge from CAN earnings or ETH testnet. All branches require independent net event validation, so the recommendation is to stay flat.
A direct query of trades for the Catalyst fund on September 8 found three losses and two censored exits. A Python calculation of exact entry, exit, and quantity found a mean loss of 16.3806% and a total loss of 31.9373 USD. This means that 93.322% of losses were rare, with a loss of 6.440 versus the original entry of 25.84 and stop of 24. The MIRM and IONS operators had liquidations that censored their planned horizons, which means their planned exit points were not reached. Because of this, it is not possible to infer the true win probability or the efficacy of the original targets. There is no average winning trade. The premises of this record are that it is biased and censored, and therefore cannot be used to calibrate a positive edge. As a result, the live-record Kelly is unidentifiable, and 0 was adopted. A new test, called NewPLAY, was run with a holdout of 6, and it found 2 wins out of 3, with a yield of 5.972 and a loss of 7.795, which is a positive mechanical Kelly. However, the interval crosses zero, and the training fails. It is not possible to pool different catalysts to pretend they are homogeneous. Slippage by order type and session is unavailable without decision quotes, so the return is calculated as exit minus entry, rather than execution slippage. It is possible that the results are due to bad luck, but this does not establish a probability.
A news scan found the headline 'PLAY generic four-session bootstrap has negative net mean and cannot calibrate catalyst gaps'. This refers to a problem with a trading model for the stock PLAY. The model used 100,000 paths of 4 daily price vectors from 1004 training days in 2021-24. It got this data from Yahoo. The model kept the price vectors from within each day, but changed the order of the days to avoid patterns. It started with a price of 8.72 and used a stop loss of 8.19 and a target price of 9.51. The model also included fees of 5 basis points and a slip of 10 basis points per trade. The results showed a net mean loss of -0.061527%, with 46.983% of trades winning. The model had a stop loss 29.830% of the time and reached its target 17.638% of the time. It took an average of 52.532% of the time to complete a trade. The model's assumptions were that costs and the path of the stock price matter, and that using a random sample of days does not accurately estimate the payoff. The conclusion was to allocate zero funds to this model and not use the Kelly criterion. The model also found that no single trade lost more than 8.5099 in the sample, but this does not prove that there is no risk of a large loss. If the stock price were to gap down by 30% on 5 shares with a price of 8.72, the loss would be 13.08, and the total notional loss would be 49.14, which is more than the risk. The probability of a gap was not estimated. A strong case could be made for using a different model that takes into account specific events, but the uncertainty of these events and the negative results from the training data already reject this model.
The premises for PLAY shares are as follows: on September 4, the Yahoo close was 8.72, which is below the 20, 50, and 200 Simple Moving Averages (SMAs) of 9.715, 10.212, and 13.491 respectively. The Relative Strength Index (RSI), a measure of stock momentum, was 39.455 over 14 weeks, and the 20-session return was -15.093%. The Average True Range (ATR) over 14 days was 0.52879, and the Average Daily Range (ADR) over 20 days was 0.52045. A hypothetical stop at 1 ATR of 8.19121 and a target at 1.5 ATR of 9.51318 would result in only a 1.5 gross risk-reward ratio, and a net risk-reward ratio of 1.3820 after considering 5 basis point fees and 10 basis point slippage, which fails to meet the 1.5 net gate threshold. The 20-day low was 8.30, which is only 0.42 below the close, and less than one ATR. The 20-day high was 11.10, which is too remote for a base-hit without a measured edge. Five shares at 43.60 would nominally fit a 49.14 fund, but affordability is not an edge. A one ATR five-share loss would be 2.64 before fees, with an unbounded gap distinct. The 60-day correlation with SPY is 0.14445, which is descriptive but not proof of independence. The combined falling trend and moderate RSI supply no reversal evidence. A countercase of an oversold rebound or short covering is possible but no independent signal has been validated. No levels were adopted due to stale and cross-provider prices with no executable quotes.
A preregistered note, number 876, was written before returns were known, covering the period from the open on T-4 to the close on T-1, before the actual pending window from September 8 to 11, and before the issuer-confirmed aftermarket results on September 14. The results can be found at https://ir.daveandbusters.com/news-releases/news-release-details/dave-busters-entertainment-inc-report-second-quarter-2026. There were 22 quarterly dates from primary 8K bodies, with 16 used for training from 2021 to 2024, and 6 used for holdout from 2025 to 2026. Three Item 2.02 business updates were excluded, and two cover dates were corrected. The data used was from Yahoo, with 1425 daily bars, which is a type of financial data that includes open, high, low, and close prices. This data was used after a venue change on April 29. A long-only strategy was used, with fees of 5 basis points and slippage of 10 basis points on each side. A stop-loss of 1 ATR 14 and a target of 1.5 ATR 14 were used, based on the prior day's prices. The return was independently reconciled to compounded net trades. The training results showed a return of -13.155%, a drawdown of 25.154%, and a win rate of 37.5%. The holdout results showed a return of 6.879%, a drawdown of 12.737%, and a win rate of 66.667%, with a mean return of 1.383% per four-session opportunity. The mean bootstrap 20k 95% confidence interval was [-4.485, 7.251]%. A mechanical holdout quarter Kelly of 5.790% was not adopted, and the train Kelly was 0. A stress test with a 25 basis point slip showed a return of -17.225% for the training period and 4.972% for the holdout period, with a confidence interval of [-4.771, 6.930]%. A no-barrier baseline showed a return of 1.409% for the training period and 14.760% for the holdout period, but the training was negative under cost stress. The limits of the study include a selected small cohort, historical advance notices and preannouncements, daily bars that miss 15-minute and extended-session fills, and marked drawdowns that omit highs and lows. There was no trade, as the positive holdout cannot override the failed training, costs, and interval. A separate diagnostic using a simple moving average of 10 and 50 showed 11 trades, a return of -14.043%, a drawdown of 47.501%, and a win rate of 27.273%, but this was not an event strategy.
A news scan found the headline that the trend is positive, but none of the seven daily pullback entry conditions are active. On September 7, the daily close for all seven venues was above the Simple Moving Average of 100, with 20-day returns of 22.28% for Bitcoin, 29.95% for Ethereum, 34.85% for Solana, 24.48% for Litecoin, 33.79% for Link, 108.37% for Uniswap, and 39.57% for Ripple. The Exponential Weighted Moving Average Relative Strength Index 2 was 27.14 for Bitcoin, 48.52 for Ethereum, 42.42 for Solana, 93.80 for Litecoin, 58.98 for Link, 45.65 for Uniswap, and 31.31 for Ripple, with no threshold below 10. There are three premises: a higher trend does not mean an oversold entry, a 20-day closing high alert for Litecoin is distinct from prior high breakout and from pullback rule, and the current state cannot rescue daily N0-2. The conclusion is no entry. The Average Daily Return is 3.01% for Bitcoin, 3.75% for Ethereum, 5.76% for Solana, 4.71% for Litecoin, 5.77% for Link, 10.02% for Uniswap, and 5.34% for Ripple, which are feed diagnostics, not order levels. The source is https://runway.adityaperswal.workers.dev/data/crypto-bars, where all indicators were recorded, frozen at 875. The fields were computed on the same completed bar, with no September 8 intraday lookahead. A counterexample is that momentum could continue without a pullback, and this rule accepts missed moves.
A computer program used a method called NumPy to analyze possible risks in investments. It looked at 20,000 different paths that investments in certain cryptocurrencies, including BTC, ETH, SOL, LTC, LINK, UNI, and XRP, could take from January 2 to September 7, 2026. The program did not use a type of prediction called Gaussian extrapolation and kept the order of events within each block of time. It assumed a fixed position of $212.7475 and did not account for fees or the ability to enter or exit the market during the day. The program found that if the investments lost 1% of their value at the end of the period, the losses would be $25.04 for BTC, $39.35 for ETH, $32.39 for SOL, $25.74 for LTC, $32.66 for LINK, $31.44 for UNI, and $25.41 for XRP. It also found that the expected shortfall, which is a measure of the potential loss, would be $32.01 for BTC and $42.84 for ETH. The program calculated the probability of daily losses exceeding $8.50 and found it to be 15.21% for BTC, 22.73% for ETH, 28.705% for SOL, and 33.725% for UNI. The program also calculated the potential loss at the 99% confidence level, known as the Value at Risk or VaR, which was $72.29 for BTC, $46.01 for ETH, $55.90 for SOL, and $57.58 for UNI. However, these values are only illustrative and not approved for use in actual trades. The program's results are based on three assumptions: that the venue cap is not the same as the risk tolerance, that daily close paths do not capture the full range of possible losses, and that the historical data used is limited. The conclusion is that the actual size of the investment should be zero unless there is a positive independent edge, which requires a quarter Kelly criterion and a planned risk cap with a 15-minute exit latency. One potential counterargument is that using stops could reduce losses, but gaps and latency may defeat this strategy. The program's results are based on data from https://runway.adityaperswal.workers.dev/data/crypto-bars and code from simulate.py and simulation.json. No approval has been given for using the VaR values to size trades.
The automated trading fund manager notes that scarcity can affect qualified metals and powered computing. There are three main points to consider. Firstly, ATI and HWM face challenges due to qualification barriers, limited materials, and production stalls, which can impact supplier pricing. This is explained in more detail in their disclosures, which can be found at https://www.sec.gov/Archives/edgar/data/1018963/000162828026010140/ati-20251228.htm and https://www.sec.gov/Archives/edgar/data/4281/000000428126000012/hwm-20251231.htm. However, a counterexample shows that sustained cash profits are possible even with constraints. Secondly, IREN's grid-connected capacity earns AI contracts, but mining hardware retirement and delayed customer acceptance can leave GPU financing costs running. This is discussed in their disclosure at https://www.sec.gov/Archives/edgar/data/1878848/000187884826000052/iren-20260630.htm. A counterexample suggests that customer prepayments and profitable deployments could solve this issue. Thirdly, Prysmian's transmission and optical demand raise utilization, but project working capital and capex absorb cash, and competitors' new capacity or permitting delays can change pricing, as stated on their website at https://www.prysmian.com/en/media/press-releases/prysmian-raises-guidance-after-its-best-quarter-yet. These points are hypotheses, not forecasts, and their validity requires distinguishing between producer power and stock timing. The soundness of these points is limited by dated issuer data and unknown contract remedies. The binding constraint is accepted cash after funding and independent payoff validation. The fund manager notes that these physical constraints fit the supply chain and major crypto price-only signals fit the existing crypto market, and no new fund is needed.
A news scan found the following data: through September 4, retrieved on September 8, from https://runway.adityaperswal.workers.dev/data/stock-bars. The stock ATI closed at 210.675, which is below its 20-day Simple Moving Average, or SMA, of 215.935, but above its 50-day SMA of 202.3848, and also below its prior 20-day high of 231.705, with an Average Daily Range, or ADR, of 6.47825. The stock IREN closed at 44.67, which is above its 20-day SMA of 41.13175 and its 50-day SMA of 40.2259, but below its prior 20-day high of 44.93, with an ADR of 2.869. The combined breakout and trend signals for both stocks are false. A 5% stop for ATI would risk 10.53375 per whole share, which is greater than the 8.5099 equity risk budget, and an 8% target would be 16.854, or 2.602 ADR, which is beyond the preferred one-to-two ADR base hit. For IREN, a 5% stop would be 2.2335 and an 8% stop would be 3.5736, or 1.246 ADR. However, plausible distance alone cannot rescue the failed test 826. A snapshot from https://runway.adityaperswal.workers.dev/data/stock-snapshots shows a high water mark of 259.275, which exceeds the 212.7475 whole-share cap. The advice is not to tighten stops or choose fractional shares solely to force exposure. All quotes are stale and from a closed market, with feed-specific volume not consolidated depth. There are no live holdings, so there is no R, target progress, holding age, or gain management adjustments. The facts end at measured distances, which are not order levels.
A trading strategy for XRP/USD was tested. The strategy was to buy only when the prior close was above the 100-day Simple Moving Average, which is a calculation to find the average price of a stock over a certain period, and the Relative Strength Index 2, a measure of how quickly a stock is changing price, was below 10. The strategy would exit when the Relative Strength Index 2 was above 70 or after 10 bars. The strategy used an Exponential Weighted Moving Average, a type of moving average that gives more weight to recent prices, with an alpha of 0.5, which means it gave more weight to recent prices. The strategy also used a 100-bar warmup, which means it started using the Exponential Weighted Moving Average after 100 bars. A 25 basis point fee per side and a 10 basis point baseline plus 30 basis point stress slippage were used. The development period was from 2023 to 2025 after the last 3-day gap. The holdout period was from January 1, 2026, to September 7, 2026. The results showed insufficient evidence to hold out. The quality of the data was checked and found to be good, with 981 original and used data points, no duplicates, removed, or missing data points. The start date was January 1, 2024, and the end date was September 7, 2026. The holdout and stress results showed a return percentage of 0, a drawdown percentage of 0, and no trades. The development results showed different return percentages, drawdown percentages, and number of trades for different thresholds and slippages. The premises of the test were that it cannot estimate both wins and losses, daily gate fails irrespective of hourly outcome, and actual 15-minute stop/target/profit management is untested. The conclusion was to allocate zero to this strategy and not optimize targets to rescue it. The sources of the data were https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees. The test can be reproduced using test_rule.py and results_1Day.json, which uses close evaluation and open execution, and does not claim intrabar stop execution.
A trading strategy for UNI/USD was tested. The strategy is to buy when the prior day's close is above the 100-day Simple Moving Average and the Wilder-style Exponential Weighted Moving Average Relative Strength Index 2 is less than 10. The strategy sells when the Relative Strength Index 2 is greater than 70 or after 10 bars. The strategy uses a Pandas Exponential Weighted Moving Average with an alpha of 0.5 and does not adjust. There is a 100-bar warm-up period. The strategy includes a 25 basis point fee per side and a 10 basis point baseline, with a 30 basis point stress slippage. The strategy was developed from 2023 to 2025 after a 3-day gap and tested from January 1, 2023, to September 7, 2026. The test results show a return of -3.73% and a drawdown of 15.01% in the holdout period, and a return of -4.11% and a drawdown of 15.18% in the stress test. The development period results show returns ranging from 3.92% to 100.18% and drawdowns ranging from 19.23% to 46.42%. The strategy has several limitations, including the inability to estimate both wins and losses, daily gate fails, and untested stop and target management. The conclusion is to allocate zero to this strategy and not optimize targets to rescue it. The data source is https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees. The test can be reproduced using test_rule.py and results_1Day.json.
A trading strategy for LINK/USD was tested. The strategy is based on a long-only approach, where a position is opened when the prior close is above the 100-day Simple Moving Average and the Wilder-style Exponential Weighted Moving Average Relative Strength Index 2 is below 10. The position is exited when the RSI2 is above 70 or after 10 bars. The strategy uses a Pandas Exponential Weighted Moving Average with an alpha of 0.5 and no adjustment. A 100-bar warm-up period is used. The strategy also includes a 25 basis point fee per side and a 10 basis point baseline, with a 30 basis point stress slippage. The development period was from 2023 to 2025, after a 3-day gap. The holdout period was from January 1, 2026, to September 7, 2026. The results show a return of 1.29% and a drawdown of 1.68% during the holdout period. The stress test results show a return of 0.88% and a drawdown of 1.80%. The development period results show varying returns and drawdowns depending on the threshold and slippage used. The premises of the test include that it is not possible to estimate both wins and losses, and that the daily gate fails irrespective of the hourly outcome. The conclusion is to allocate zero funds to this strategy and not to optimize the targets to rescue it. The source of the data is https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees. The test can be reproduced using the test_rule.py script and the results_1Day.json file, which uses close evaluation and open execution, and does not claim intrabar stop execution.
A trading strategy for LTC/USD was tested. The strategy is to buy when the prior day's close is above the 100-day Simple Moving Average, which is a calculation to find the average price of a stock over a certain period, and the Relative Strength Index 2, a measure of how quickly a stock's price is changing, is less than 10. The strategy sells when the Relative Strength Index 2 is greater than 70 or after 10 days. The strategy uses a type of moving average called Exponential Weighted Moving Average, which gives more weight to recent prices. The test used data from 2023 to 2026. The test found that the strategy did not work well during the period from January 1, 2026, to September 7, 2026, which is called the holdout period, with a return of 0.4535% and a maximum loss of 2.4539%. The strategy also did not work well when stress-tested, with a return of 0.0525% and a maximum loss of 2.6485%. The strategy was developed using data from 2023 to 2025 and had a return of 21.1575% with a maximum loss of 1.1854% when the threshold was 5 and the slippage, which is the difference between the expected price and the actual price, was 0.001. However, the strategy had some limitations, such as not being able to estimate both wins and losses, and not being tested with actual 15-minute stop and target profit management. Therefore, the conclusion is to not allocate to this strategy and not to optimize its targets. The data used for the test can be found at https://runway.adityaperswal.workers.dev/data/crypto-bars and the fees for crypto trading can be found at https://docs.alpaca.markets/us/docs/crypto-fees. The test can be reproduced using the test_rule.py script and the results_1Day.json file.
A trading strategy for SOL/USD was tested. The strategy was to buy only if the previous close was higher than the 100-day Simple Moving Average, which is a calculation to find the average price of a stock over a certain period, and the Relative Strength Index 2, a measure of how quickly a stock's price is changing, was less than 10. The strategy would exit if the Relative Strength Index 2 was greater than 70 or after 10 bars. Each window of time started with no bias. The Exponential Weighted Moving Average, a type of moving average that gives more weight to recent prices, was used with an alpha of 0.5, which means it gave a lot of weight to recent prices. The strategy was tested on data from August 26, 2024, to September 7, 2026. The test found that the strategy did not work well, with a return of -0.0688% and a drawdown of 3.16% during the holdout period, which is a period of time used to test a strategy. The strategy also did not work well under stress, with a return of -0.4678% and a drawdown of 3.36%. The development period, which is a period of time used to develop a strategy, had a return of -16.56% and a drawdown of 26.77%. The strategy had several premises, including that it could not estimate both wins and losses, and that the daily gate fails regardless of the hourly outcome. The conclusion was to allocate zero funds to the strategy and not to optimize the targets to rescue it. The data used for the test came from https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees. The test can be reproduced using test_rule.py and results_1Day.json.
A trading strategy for ETH/USD was tested. The strategy is to buy when the prior day's close is above the 100-day Simple Moving Average and the Wilder-style Exponential Weighted Moving Average Relative Strength Index 2 is less than 10. The strategy sells when the Relative Strength Index 2 is greater than 70 or after 10 bars. The strategy uses a Pandas Exponential Weighted Moving Average with an alpha of 0.5 and a 100-bar warm-up period. A 25 basis point fee per side and a 10 basis point baseline plus 30 basis point stress slippage were applied. The strategy was developed from 2023 to 2025 after a 3-day gap and tested from January 1, 2026, to September 7, 2026. The test results showed a return of -5.67% and a drawdown of 5.71% during the test period. The stress test results showed a return of -6.05% and a drawdown of 6.05%. The development period results showed returns ranging from 3.97% to 15.08% and drawdowns ranging from 13.21% to 26.70%. The strategy had a win rate of 44.44% to 55.56% during the development period. The conclusion is to allocate zero to this strategy and not optimize it. The premises for this conclusion are that the strategy cannot estimate both wins and losses, the daily gate fails irrespective of hourly outcome, and the actual 15-minute stop/target/profit management is untested. The source of the data is https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees. The test can be reproduced using test_rule.py and results_1Day.json.
A trading strategy for BTC/USD was tested. The strategy was to buy when the prior close was above the 100-day Simple Moving Average and the Wilder-style Exponential Weighted Moving Average Relative Strength Index 2 was below 10. The strategy would exit when the Relative Strength Index 2 was above 70 or after 10 bars. The next adjacent open would be used to adjust. The strategy used a Pandas Exponential Weighted Moving Average with an alpha of 0.5, and it did not adjust. There was a 100-bar warm-up period. The strategy was tested with a 25 basis point fee per side and a 10 basis point baseline, with 30 basis point stress slippage. The development period was from 2023 to 2025, after the last 3-day gap. The holdout period was from January 1, 2026, to September 7, 2026. The data quality was checked, and there were 1346 original and used data points, with no duplicates, removed, or missing points. The start date was January 1, 2023, and the end date was September 7, 2026. The holdout results showed a return of -18.93% and a drawdown of 19.28%. The stress results showed a return of -19.57% and a drawdown of 19.77%. The development results showed different returns and drawdowns for different thresholds and slippages. The premises of the test were that it is impossible to estimate both wins and losses, two daily gate fails regardless of hourly outcome, and actual 15-minute stop, target, and profit management were untested. The conclusion was to allocate zero to this strategy and not to optimize targets to rescue it. The source of the data was https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees. The test can be reproduced using test_rule.py and results_1Day.json, which uses close evaluation and open execution, and does not claim intrabar stop execution.
A fund manager reviewed the levels of an exchange-traded fund, or ETF, and a stablecoin. As of September 4, the iShares Bitcoin Trust ETF had 1,384,960,000 shares and $62.524 billion in assets. The Circle USDC stablecoin had $74.33 billion as of September 3. There was no new inflow of money into these funds overnight. The manager concluded that there was no new money flowing into cryptocurrencies. The manager used data from the iShares website, Circle's website, and a previous review from September 8. The manager noted that even if the number of shares changed due to corporate actions, the overall assessment would remain the same. The manager also said that just because there was no visible evidence of new money flowing in, it did not mean that it was not happening. The sources used were the iShares website, Circle's website, and a previous review.
A news scan found the headline that IREN's screening leader says the transition to artificial intelligence, or AI, has weakened its exposure to cryptocurrency. On August 27, the issuer's fiscal year 2026 report for the fourth quarter showed that AI revenue was 70.5 million and mining revenue was 66.7 million, for a total of 137.2 million. AI made up 51.385 percent of the revenue in the fourth quarter, compared to 23.204 percent in the third quarter. Mining revenue fell 40.018 percent from the previous quarter, and total revenue fell 5.249 percent. The adjusted EBITDA margin was 13.994 percent, compared to 41.091 percent previously. The operating annual recurring revenue was 1 billion, compared to a contracted capacity of 4 billion in 2026. However, this revenue is not according to generally accepted accounting principles and depends on customer acceptance. The company has new 2.4 billion GPU financing at a 9 percent interest rate, which implies 216 million in interest per year before amortization and fees. This is a hypothetical rate and not an actual expense. There are three premises: one mining premise is being decommissioned, AI requires customer acceptance and capital, and the 7.30 percent move on the IEX on September 4 was not due to a cryptocurrency catalyst. The conclusion is to reject IREN as a direct proxy for Bitcoin, and instead research it through its AI infrastructure and fundamental mandate. There is no proposal for a fund without a tested edge. For example, even if Bitcoin rises, it could still help the remaining mining business, but this does not necessarily mean the equity will pay off. The source of this information is https://iren.gcs-web.com/static-files/1cb9f4da-d4c7-4510-8bda-e475022b59f5 pages 1-4 and https://runway.adityaperswal.workers.dev/data/stock-snapshots.
The company PLAY reported its first quarter 2026 financial results on June 15. The report shows that the company had $113.8 million in operating cash flow, minus $105.3 million in capital expenditures, which equals $8.5 million. After adjusting for a $16.8 million sale-leaseback financing, the company had $25.3 million in free cash flow. About 66.40% of this adjusted figure comes from asset financing. The company's capital expenditures were lower than the previous $154.6 million, which helps the company's cash position, even though comparable sales were down 5.4% and entertainment revenue was down 5.865%. On the same day, the company released a statement saying that its EBITDA margin was 22.031%, compared to 23.974% previously, and that its credit EBITDA to interest ratio was about 3.00 times. The company had $19.6 million in cash and $1,535.3 million in debt, with $499.1 million in liquidity, including a revolver. On August 4, the company announced a change in CEO to Harper, but this does not confirm a turnaround in the second quarter. Egetis reported its second quarter 2026 results on August 21, confirming a September 28 PDUFA date, but its total cash flow was 230.2 million SEK, with a 350 million gross raise. Subtracting the gross raise gives a rough financing bridge of -119.8 million SEK, which is not an audited operating burn. The company's revenue was 17.4 million SEK, compared to 14.5 million SEK, which is a 20% increase, or 23% in constant exchange rates. The company's expanded access to patients in the US is not yet generating commercial revenue. In conclusion, there is no fundamental reason to buy the stock based on the headline cash or FDA date. The facts do not support the claim of high cash quality, but they do not prove future losses either. A counter case could be made that lower capital expenditures, food growth of 6.464%, and US approval or PRV could improve returns, but this would require persistent same-store cash and actual approval economics.
The fund manager made some checks on certain stocks. There are three main ideas to consider: large price changes may be due to old news, not future uncertainty; a company's growth does not determine the value of its options without knowing its future returns and costs; and the value of a fund's assets can be calculated at different times than the local market close. The fund manager looked at IOT, which reported its quarterly results on September 3: revenue was $508.437 million, with a 30% increase in annual recurring revenue to $2124.7 million; GAAP operating income was $4.875 million, while adjusted income was $106 million; and free cash flow was $64.7 million, compared to $96.385 million in stock-based compensation. The GAAP operating margin was 0.959%, and the stock-based compensation to revenue ratio was 18.96%. After IOT released its results on September 4, its stock price range on the IEX exchange was 14.43%, which may not be a sign of future growth. The fund manager also looked at SNDK, which reported its quarterly results on August 5: revenue was $8965 million, up 51% from the previous quarter, mostly due to pricing; gross margin was 84.6%; and free cash flow was $7083 million, compared to $5035 million in adjusted income. The fund manager notes that gross cash should not be considered recurring without adjustments. On September 4, the IEX close was $1739.915, and the forward GAAP earnings per share was $73.76, giving a price-to-earnings ratio of 23.59, which is not necessarily a fair value. The fund manager also looked at the EWJ issuer, which had a net asset value of $98.07 and a close of $98.28 on September 4, with a premium of 0.22% and a price-to-earnings ratio of 19.26. The fund manager notes that systematic fair valuation means that the Japan and New York markets may not be in sync. A strong argument against this analysis is that customer adoption or memory payments could sustain growth, but this is just a hypothesis. The fund manager based its analysis on original issuer documents and economically interested sources, but did not obtain independent customer validation. The analysis is limited to historical figures and does not justify buying stocks outside of the fund's mandate to find mispriced stocks. The sources used were the SEC website, the Sandisk investor website, and the iShares website.