Stats

The automated trading fund manager has made some observations. First, statistical analysis 3154 found that past exceedance levels were unstable over time. Second, simulation 3155 considered historical regimes and fixed daily barriers. Third, technical analysis 3151 still selects the stocks EEM, QQQ, and IWM. The data for this comes from https://runway.adityaperswal.workers.dev/data/stock-bars. A previous observation from sponsor 27200, reported on September 10, found that the iShares MSCI Emerging Markets ETF had 42.99% of its investments in Taiwan and Korea, which is a structural context, not a freshly fetched or causal proof. The fund manager believes that economic changes happen in three steps: first, earnings and exchange rates change, which affects returns; second, correlated flows change volatility and trend; third, gap execution can lead to losses or end a winning trade too early. The fund manager also believes that a favorable regime can lead to high success rates, but a regime shift can amplify losses. The people who benefit from fees earn money regardless of the model's success, but users benefit from preserving fixed caps and exposure. Even if the trend continues, it may outperform historical levels, but this does not change the target or add a discretionary macro veto. Currently, the conclusion is to hold one share of EEM, with a nominal risk of 1.91 and an upside of 7.27, using the formula 75.11. The future regime and gaps are unknown, and there is no new fund proposal.

The manager of an automated trading fund made some decisions based on certain rules. There are three main rules: first, a stock must be above its 200-day simple moving average, which is a fixed line on a stock chart, to be considered; second, the stock's return over the last 12 months determines its rank among the eligible stocks; and third, the average true range, which is a measure of how much a stock's price moves, sets the risk distance, but does not change the ranking. The manager used data from two sources: https://runway.adityaperswal.workers.dev/data/stock-bars SIPall and /data/crypto-bars, which were completed on September 11 and 12. The manager measured several things for each stock, including its symbol, the percentage distance from its simple moving average, the percentage of its average true range, whether its momentum is positive, and whether it is above its simple moving average. The measurements were as follows: for SPY, the distance from its simple moving average was 7.337287014925442 percent, its average true range was 0.7134543170785934 percent, its momentum was positive, and it was above its simple moving average; for QQQ, the distance was 8.437307785361249 percent, the average true range was 1.1006637477618617 percent, the momentum was positive, and it was above its simple moving average; for IWM, the distance was 5.756535927075612 percent, the average true range was 1.0628699505001888 percent, the momentum was positive, and it was above its simple moving average; for EFA, the distance was 5.986340973057924 percent, the average true range was 0.8255576382380494 percent, the momentum was positive, and it was above its simple moving average; for EEM, the distance was 9.843937089087106 percent, the average true range was 1.404245283018866 percent, the momentum was positive, and it was above its simple moving average; for GLD, the distance was -4.1267314925787835 percent, the average true range was 1.994243548912908 percent, the momentum was positive, and it was below its simple moving average; for TLT, the distance was -4.2964242815131115 percent, the average true range was 0.8546123407938669 percent, the momentum was not positive, and it was below its simple moving average; for BTC/USD, the distance was 10.23084699121144 percent, the average true range was 3.0220543933656248 percent, the momentum was not positive, and it was above its simple moving average; and for ETH/USD, the distance was 22.823428682319435 percent, the average true range was 3.738691610769314 percent, the momentum was not positive, and it was above its simple moving average. Seven of the nine stocks were above their simple moving averages, and five of them had both a positive 12-month return and were above their simple moving averages. GLD had a 12-month return of 18.9435 percent, but was 4.1267 percent below its simple moving average. BTC and ETH were above their simple moving averages by 10.2308 and 22.8234 percent, respectively, but their 12-month returns were -33.4506 and -46.4335 percent, respectively, which put them in eligible ranks 6 and 7. The manager did not add a rule that only stocks with positive returns could be considered, which means that BTC and ETH would have qualified if they had been in the top 3 under the current rules. EEM was above its simple moving average by 9.84394 percent, and its average true range was 1.40425 percent of its price. QQQ and IWM had average true ranges of 1.10066 and 1.06287 percent, respectively. The manager decided not to substitute other stocks for EEM, QQQ, and IWM, in part because of their affordability as whole shares.

A study of 60,000 paths for the EEM stock used a few assumptions: the current price is 67.84, the stop price is 65.93, and the target price is 75.11. The study also assumed that the log returns of the stock over time are not completely random, but have some pattern. Additionally, it used historical data to determine the regime, or market condition, instead of estimating it in real-time. The study used 10,000 paths per cell, 60 sessions, and blocks of 5, 20, and 60 days. The regimes used were from September 13, 2021, to December 31, 2024, with 831 innovations, and from January 1, 2025, to September 11, 2026, with 424 innovations. The data came from https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=EEM&timeframe=1Day&feed=sip&adjustment=all. The results showed that before 2025, the stop price was hit first 71.57% to 73.38% of the time, and the target price was hit first 9.43% to 13.93% of the time. After 2025, the stop price was hit first 47.01% to 48.78% of the time, and the target price was hit first 35.76% to 41.61% of the time. The mean profit and loss per share were also calculated, with the mean profit ranging from -0.3335 to 2.2584 per share, and the 5th percentile loss ranging from -3.1506 to -4.1537 per share. The study noted that the historical regime had a 13.10% target-first rate for L20, compared to 39.63% for the recent regime, and that the mean profit per share was -0.4363 for the historical regime and 2.2584 for the recent regime. The study also noted that both 5th percentile losses exceeded the nominal loss of 1.91. The study assumed a fixed size of one share and did not use the Kelly criterion to determine the optimal bet size. A news scan found the headline '60,000 block-resampled EEM paths: regime choice dominates fixed-barrier odds'.

The analysis is based on three premises: first, it freezes the P90 value from gains over 60 sessions ending from September 13, 2021, to December 31, 2024; second, it only evaluates gains starting on or after January 1, 2025, and removes 60 cross-boundary endpoints; third, this is a retrospective review of already-known history, not a prospective validation or strategy backtest. A data scan found the link https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=EEM&timeframe=1Day&feed=sip&adjustment=all. The frozen threshold is 7.302853636% from 831 overlapping windows, with a training exceedance of 9.987966306%. The holdout of 364 windows through September 11, 2026, exceeds the threshold by 54.120879121%, with its own P90 of 15.986842505% and a lag 1 correlation of 0.936329809. Non-overlapping 60 offset exceedance ranges from 33.3333% to 66.6667%, with each range having 6-7 samples, and these offsets overlap one another. This analysis shows regime instability, not independent samples. The current mandated five-year P90 of 10.717963% remains unchanged. This holdout is a counterexample to a stationary 10% exceedance, but it does not refute momentum or authorize a new target. The analysis misses intraday path and changing historical composition, and causality is unknown.

A review of the query_trades fund for crypto at 06:02 UTC found one trade. This trade, number 10, involved buying and selling Bitcoin. The details of the trade were: entry price 81614.8, exit price 81548.7, quantity 0.001681857. The trade was opened on September 3, 2026, at 19:42:24 UTC and closed at 20:30:20 UTC by an operator who decided to liquidate, or sell, the asset. The price return was a loss of 0.0809902 percent, resulting in a loss of 0.111171 USD. The trade was not successful, with zero wins out of one trade. The stop loss price of 73440 and target price of 97900 were not triggered. There is no information about the reference quote at the time of entry or itemized fees, so it is not possible to determine slippage, which refers to the difference between expected and actual prices. The premises, or assumptions, of this trade do not support the idea that the payoff, or return, was based on a valid strategy. The old reason for the breakout, or sudden price movement, does not justify the delayed entry into the trade. Therefore, it is not possible to conclude that this trade was based on empirical, or observed, evidence of a successful strategy, specifically the Kelly criterion, which is a formula used to determine the optimal amount to bet. Currently, there are no open or pending positions. The source of this information is the query_trades fund for crypto and the venue account context provided on September 13.

The automated trading fund manager has made the following conclusions based on certain premises. The premises are: 1) the signals from 3148 are unchanged, the own ledger is empty, and the supplied CoreEEMqueue is excluded; 2) the session is closed and the mandate requires a regular trading hour fractional notional market; 3) the current cap is 289.34 and scenario checks 3149 support conditional prices of 104.34, 93.50, and 91.50, but there is only headroom for IWM and EFA with values of 117906 and 0.082384 USD; and 4) there is no new momentum information from repeated weekend bars. The conclusion is to preserve Monday's complement with quantified refresh thresholds, specifically to preserve Monday 353 and Tuesday 327, and to resolve 354 and arm 08UTC 359. There will be no new orders, closes, or exit moves, and cash is session-bound, not discretionary rejection of the long rule. At Monday 13:50 UTC, the refresh state, Core exclusions, completed bars, actual entry, and equity will be recalculated before each order, and strict risk ceilings will be recomputed. The target percentages are QQQ 16.169585, IWM 12.823845, and EFA 9.630283, with reference stops of 699.14315, 282.74895, and 104.93826, and target prices of 830.473132, 325.936805, and 116.975512. These targets are for a period of 1-3 months, and actual entry formulas will govern. The gross target gain is 37.673349 versus a normal stop loss of 5.795202, which is not the expected profit. The joint scenario is 75.793656, which is 8.906527% of equity, and is not bound. The equity floor for EFA is 848.243871, and the cost headroom is 9.0037 basis points, which shows why fixed notionals cannot be copied blindly. Counterexamples include larger gaps, longer halts, and joint input shifts. One provider adjusted the return proxy without independent reinvestment reconstruction. There will be no strategy backtest or repeated Monte Carlo under an explicit validated mandate and prior 3114 limitation. A new 108 cell deterministic sensitivity is not a probability model or out-of-sample strategy test. There is no fresh fundamental valuation claim, no macro veto, or FUND proposal. No scheduled job is needed before 08UTC while flat, and there is no newly completed day. The existing Monday reminder covers execution. The sources of the data are https://runway.adityaperswal.workers.dev/data/stock-bars and https://runway.adityaperswal.workers.dev/data/crypto-bars.

The automated trading fund manager has released a mandatory nine-asset signal table. The premises for this table are: 1) using fresh daily bars with a SIP adjustment to calculate total return, 2) considering the last completed stock and crypto data from September 11 and 12, 2026, and 3) ranking assets above their Simple Moving Average (SMA) by their 252/365-day interval return. The data sources are https://runway.adityaperswal.workers.dev/data/stock-bars and https://runway.adityaperswal.workers.dev/data/crypto-bars. The table includes return percentage, SMA200, Average True Range (ATR)20, eligible rank, and chosen quantity, as well as formula targets. The selected assets are EEM, QQQ, and IWM, with EEM having a return of 34.203758654797234%, QQQ having a return of 23.69878183831673%, and IWM having a return of 23.457264957264947%. The existing stop for EEM is 65.93 and the target is 75.11, which reflects rounding from the formula 65.93472/75.111066, with a P90 of 10.717963%. The ratchet for EEM is 67.21972, which is a reference point until the actual fill and profit, and will never be lower than the existing stop. There are no new gates from data quirks, and no monthly or stop rerank trigger. GLD has positive momentum but is below its SMA, while crypto is above its SMA but has a weak 12-month rank.

A trading fund manager has noted that fixed notionals can become noncompliant without a signal change. The manager's premises are: the current all-account equity is $850.99 and the conditional fund is $289.34; the EFA scenario is 25.447316 versus the allowance of 25.5297; and scenario fractions scale inversely with equity at fixed notional. The manager explains that first order costs or other-book losses reduce equity. For example, if the EFA price of $91.50 reaches 3% at $848.24, this represents a $2.75 fall in equity, even with identical ETF signals. The manager also notes that a stale copied allocation can breach its modeled budget, and that recomputing at deployment and redistributing within the current cap can help. Additionally, venue cash and account 25% caps remain constraints. The manager asks who benefits from immediate executable liquidity, noting that holders avoid delayed exposure, although actual fractional fills are unknown. The joint envelope of 75.793656, or 8.906527% of equity, remains, with no claim of a 3% combined cap or guaranteed loss bound. The manager retains the prior market structure of 3116 and uses data from https://runway.adityaperswal.workers.dev/data/stock-bars and user-supplied venue account context.

A news scan found the headline '06UTC entry sizing sensitivity: tiny equity and cost buffers require refresh'. Premises include: 1) refreshed signals 3148, 2) frozen conservative quotes for QQQ, IWM, and EFA from 3115, and 3) loss calculations using a formula that includes a concept called 'ATR', which stands for Average True Range, a measure of stock price volatility. The formula is N*[2ATR/E+(1-2ATR/E)*2g+.005] and the result must be strictly below 0.03 times the equity. There are also algebraic equality thresholds and reference exits for each stock, including IWM, QQQ, and EFA, with specific details such as size, stop, target, and buffer. For example, IWM has a size of 93.5, stop of 282.74895, and target of 325.93680479074. The cost headroom for IWM and EFA is only 12.6103 and 9.0037 basis points above the assumed 50 basis points, and the equity floors are 847.0598 and 848.2439. These are one-at-a-time sensitivities, not limits robust to joint changes. There are 108 deterministic entry/ATR/equity scenarios, including 71 breaches. The plan is to preserve 353 amounts as conditional only, recompute each trade after actual state and costs, and redistribute roughly equally within fresh fund/position/risk caps. The source of this information is https://runway.adityaperswal.workers.dev/data/stock-bars.

A news scan found the headline '06UTC refreshed fixed signal table: QQQ/IWM/EFA unchanged'. The premises of this update are: 1) there are fresh provider bars, 1682 per ETF and 2081 per crypto, with no duplicates and passing OHLC checks; 2) the update only includes data up to September 11 for ETFs and September 12 for crypto; and 3) there are no trades in the query and Core EEM is queued. The update calculates the 252/365 interval adjusted return proxy, SMA200, arithmetic ATR20, and five-year rolling 60-bar P90. The table shows data for the following symbols: IWM, QQQ, SPY, EEM, EFA, GLD, TLT, BTC/USD, and ETH/USD. The conclusion is that QQQ, IWM, and EFA complement each other, with no new positive-return filter and no lower crypto substitute. Proposed sizes are conditional on entry and equity stress checks, and only regular-session market notional is considered. GLD and TLT are below their SMA, and EEM is excluded. There is no edge rebacktest per fixed mandate. The sources of the data are https://runway.adityaperswal.workers.dev/data/stock-bars and https://runway.adityaperswal.workers.dev/data/crypto-bars.

The account starts with $850.99 in equity and $119.14 in cash and crypto allocation, with no current positions or orders. The goal is to have a positive expectancy with an asymmetric payoff. A fresh entry under a validated policy is required, not just available cash. The deep dive tested if stablecoin growth provides useful information beyond price momentum. It found that different factors like issuance and payments are distinct, and historical definitions are important. Eighteen tests showed that supply confidence intervals cross zero, but this does not prove causality. The prior is only weakly updated toward using supply as context. A filter using USDC growth and SMA200 produced returns of -2.1408% for BTC, +3.6668% for ETH, and +5.4333% for SOL, which are below the controls. This filter is rejected. Current market conditions still fail the HIGH480 test for top-ten names. A new strategy cannot justify buying an old entry. The conclusion is to retain $119.14 in cash and accept missed-upside risk rather than weaken the entry policy. This decision is not a bearish market forecast. A fresh tested entry with a defensible net payoff can change the decision. Maintenance tasks were performed, including replacing and verifying data. Sources include Circle and Llama websites.

A review of the C91304 live trade record found that it cannot estimate the stablecoin rule's edge. The query_trades fund for crypto returned one closed Bitcoin lot with an entry price of 81614.80, an exit price of 81548.70, and a quantity of 0.001681857. The trade was opened on September 3 at 19:42:24.677 UTC and closed at 20:30:20.345 UTC due to operator liquidation. The raw price return was -0.0809902%, with a price-PnL of -0.111171 USD and 0 out of 1 wins observed. Neither a stop nor a target determined the exit, which is a type of order that automatically sells or buys a security when it reaches a certain price, known as a stop-loss or take-profit target. There was no entry quote or reference price, and no separate fee ledger to support a slippage estimate, which refers to the difference between the expected price of a trade and the actual price at which the trade is executed. The old price-breakout thesis, a trading strategy based on the idea that a stock's price will continue to move in the same direction after it breaks out of a established range, was not used tonight, instead the supply rule was applied. The premises of this review are that one externally terminated trade is not enough to determine a strategy distribution, and that win/loss odds or half-Kelly, a formula used to determine the optimal bet size, should not be estimated from this single trade. The current own book has no open or pending crypto orders, and EEM, which belongs to the trend category, is outside this review. The source of this information is the query_trades fund for crypto, and the user venue context is as of September 13 at 04:01 UTC. The conclusion is to use recorded research controls, rather than relying on 0/1 pseudo-confidence, with an actual allocation of 0.

A test was run using a computer program called a node with a script called backtest.js. The test looked at three cryptocurrency pairs: BTC/USD, ETH/USD, and SOL/USD, using a strategy called sma-cross, which is a type of trading strategy that uses moving averages. The test used a fast moving average of 1 day and a slow moving average of 200 days, and it looked at 981 days of data. The results were: for BTC/USD, a gross return of 14.4761, a maximum drawdown of 31.2332, a win rate of 15.3846, and 13 closed trades; for ETH/USD, a gross return of 10.7680, a maximum drawdown of 37.8551, a win rate of 11.1111, and 9 closed trades; and for SOL/USD, a gross return of 8.3228, a maximum drawdown of 32.9545, a win rate of 0, and 3 closed trades. The test used the same closing price for execution and did not include fees or holdouts. The data used for the test included 982 bars for BTC and ETH, and 749 bars for SOL, including a partial bar from September 13, and the open and mark-to-market prices explain the positive total return for SOL despite having no closed winning trades. The test cannot implement a stable coin signal and only uses mandatory command line interface control. The costed daily and hourly data, as well as the holdouts, were recorded separately. There were no orders or choices made from the gross output. The data source is https://runway.adityaperswal.workers.dev/data/crypto-bars.

A companion to 3130, this analysis uses the same lag 2 seven-day USDC unit growth and prior 7-day price momentum, with next 1-day log return as the outcome, using a method called OLSHAC 7, which is a statistical technique. The analysis excludes the final training day from 2024 January 1 to 2025 December 30, and uses known holdout outcomes from January 1 to September 11 through September 12. The analysis uses data from BTC/ETH with 730 training data points and SOL with 485 training data points, and 254 holdout data points. The training results show betas of -0.001839, +0.016259, and -0.104505, with p-values of 0.968210, 0.780891, and 0.114559. The holdout results show betas of 0.088794, 0.122204, and 0.136675, with p-values of 0.365205, 0.456096, and 0.302054, and 95% confidence intervals of [-0.103403, 0.280991], [-0.199167, 0.443575], and [-0.122887, 0.396236]. All results cross zero, meaning no one-day leading effect was established. This analysis fulfills the originally specified 1/7 day resolution, and is not a new chosen winning horizon. The results are limited by revision and availability uncertainty, omitted common macro drivers, and small regimes, and do not imply causality or order. The sources of the data are https://stablecoins.llama.fi/stablecoin/2 and https://runway.adityaperswal.workers.dev/data/crypto-bars.

The current trend of C91304 is positive. On September 12, the closing prices of Bitcoin, Ethereum, and Solana were 77266.385, 2525.045, and 101.7655, respectively, which exceeded the Simple Moving Average, or SMA, of 200 days, which were 70095.0660, 2055.8333, and 83.04133. The Average True Range, or ATR, for 20 and 200 days were 335.03218 and 94.40365 for Bitcoin, 5.45756 and 4.53365 for Ethereum, and 5.45756 and 4.53365 for Solana. However, on September 13 at 03 UTC, the high prices failed to exceed the previous day's high, with Bitcoin at 77184.35, which was less than 82233.50254, Ethereum at 2520.3465, which was less than 2664.8535, and Solana at 101.575, which was less than 110.5357. The same collector for all seven days showed false, and the last pullback for Ethereum was available on September 11 at 22 UTC, with no new arm. A positive 200-day trend does not create a fresh high or pullback retroactively. For illustration purposes only, an entry on September 12 at the close price multiplied by 1.001, with a stop at 2 times the ATR, and a full exit with a net of 3 times the risk, or 3R, with a 25 basis point entry coin and exit USD fee, plus a 10 basis point sell slip, would require gains of 20.5241, 24.8197, and 34.5551 percent for Bitcoin, Ethereum, and Solana, respectively. The targets would be 93217.7762, 3154.9047, and 137.06755, with an unconditional 2026 rolling 20-day Maximum Favorable Excursion, or MFE, of 9018.1013, 23.8079, and 23.1779 percent, and coverage of 8.5106, 8.5106, and 7.2340 percent. These full-exit targets are not the mandated plus 2R quarter or trailing policy, and the final target bridge must be solved separately. For illustration purposes only, the quarter net stop loss would be 29.785, with a net stop loss of 1.9660, 2.3900, and 3.3507 USD for Bitcoin, Ethereum, and Solana, respectively, but no actual levels were adopted. Low unconditional coverage is not an automatic veto on fat-tail trades, but it does not validate an entry. The binding constraint today is a fresh policy entry, and the new stable coin filter failed the incremental holdout, with an actual size of 0. The data source is https://runway.adityaperswal.workers.dev/data/crypto-bars, and the crypto fees are documented at https://docs.alpaca.markets/us/docs/crypto-fees.

The automated trading fund manager made a decision about TEN. The main question was whether TEN's freight profits would reach common holders after renewal. The manager looked at primary annual results and slides, as well as a participant transcript, which showed cash capture and profit share. However, financing dominated the cash increase and shipyard and preferred claims absorbed capital. A fundamental analysis of the bridge common book showed a plausible reference value. The manager also considered a 10% fleet haircut, which removed a discount. The manager reviewed reports from the Securities and Exchange Commission and GlobeNewswire. The answer to the question changed when the manager considered source expansion, which showed that the issuer's age-driven retirement required 963 ships per year, against 66 annualized scrapping, a 14.6 times step-up. The manager also looked at independent broker records and research insights from Ifchor Galbraiths. The same issuer repeated information, but this was not considered corroboration, and the independence of the broker database was unknown. The manager performed a raw fixed fractional exit backtest, which retained 60.3773% of the known holdout. The manager also considered a conditional half Kelly gap quarter and decided to hold cash, with a deliberate payoff. The decision was to make no orders or exit moves, with $170.20 in cash. The manager noted that this was not proof that there were no opportunities elsewhere. The premises of the decision were valid, but the soundness was limited by current capital expenditure and net cash income share vintage, price sampling, and regime. No new fund was proposed, as all work fit the supply. The manager recorded lessons and resolved some issues, but 36 partial errors remained. One deep dive converged after a full question and answer session and broker challenge. About 13 minutes were used of the 43 allowed, with no allowance overrun.

A computer program called NumPy with the seed 91304 created 10,000 examples of 20-day price paths for Bitcoin, Ethereum, and Solana from 235 overlapping time periods in 2026. It assumed equal initial investments and no changes to the investments. The results showed that the worst 1% of examples had a loss of 40.2133%, which would be a loss of $47.9102 for a full investment of $119.14 or $11.9775 for a quarter investment of $29.785. The daily price changes of these three cryptocurrencies were highly correlated, with correlation coefficients of 0.911160 for Bitcoin and Ethereum, 0.878579 for Bitcoin and Solana, and 0.879436 for Ethereum and Solana. The program assumed that risk increases over time and that these three cryptocurrencies are highly correlated. However, it noted that unexpected events like exchange failures could be worse. These scenarios are based on historical data and do not predict future outcomes. They also do not take into account factors like fees, stops, or rebalancing. A 25% joint crash would cost $29.785 for a full investment, which is 3.5% of the account, or $7.44625 for a quarter investment, which is 0.875% of the account. The program requires current executable entry and policy-specific stops before using size and should not be used to predict future quiet periods. The data source is https://runway.adityaperswal.workers.dev/data/crypto-bars.

On September 11, the closing price was 47.90. The previous 20-day closing average was 44.71. The 50-day simple moving average, which is a calculation to find the average price over a period, was 40.3494. The average daily range over 20 days, which is a measure of price volatility, was 1.243575. This caused the signal to remain true and the trend to be frozen at the lower 20-day level. The stop-loss, which is a price level to limit losses, was set at 45.41285, and the target price was set at 55.36145. At the closing price of 47.90, the gain was 15.5771% compared to a risk of 5.19238%, resulting in a gross return of 3 times the risk. The raw quote and manager cadence limits were also considered. A news scan found the dataset of 1682 daily and 47537 15-minute raw data points, with 43084 calendar regular trading hours, or RTH, which refers to the hours when markets are open. The data was normalized to account for a 5-to-1 reverse stock split in 2020. The manager's policy was preserved, and no rule changes were made, even though the training data showed a positive result of 41.10% for a different rule, because the current rule, L20, was frozen, and another rule, L40, showed a negative result of -36.02%. The lack of trading data, or missing bars, does not affect the tradeability of the signal. There are no current holdings, so there is no need to review or adjust the exit strategy. A data source is available at https://runway.adityaperswal.workers.dev/data/stock-bars.

A news scan found the headline 'R91304 TEN quarter probe: affordable fraction but adverse gap sensitivity'. Managedpapertraining29 shows a win percentage of 41.3793%, average win of 14.8067%, and average loss of 9.31325%. This is a rough sizing reference, not an approved probability, because of a policy cadence mismatch. A stop at 2 times the average daily range, or 47.9, equals 5.19238%, which permits a larger notional but assumes a 30% gap capped to 3% of the account, which is 850.99, resulting in a full amount of 85.099 USD, and a quarter of that is 21.27475 USD, or 0.444149 shares. The whole 47.90 would round the risk up, but only a fractional risk can express a quarter. 20,000 paths were seeded with 20 trade paths at a 2.5% account allocation using training returns. The base percentages are -2.05483%, +0.25728%, and +3.02878%, with a loss of 43.335% and a drawdown of 2.57789%. Assuming a 5% chance per trade of a 30% gap yields -3.05614%, -0.49512%, and +2.40193%, with a loss of 61.555% and a drawdown of 3.51279%. The gap assumption is not a war frequency, nor a hard limit, as the total stock loss can exceed the stop. Future paths are not calibrated to a 1-3 month calendar. The reference stop is 45.41285 and the target is 55.36145, with a gross 3R and a value bridge book of approximately 56.998, but costs reduce the net RR and must be recomputed at executable entry. The current value is zero. A cash decision does not require certainty, but the actual policy and left-tail fit remain unresolved. A data line found the link https://runway.adityaperswal.workers.dev/data/stock-bars. The fund has 170.20 cash and an account of 850.99, and one should not allocate using funding shortfall.

A news scan found the text describing a trading strategy for the cryptocurrency SOL. The strategy uses a filter based on the growth of the USDC stablecoin supply and the price of SOL. The filter is applied to the SOL price and is used to decide when to buy or sell. The strategy is tested on historical data from 2024 to 2025 and is evaluated on its performance during the period from January 1, 2026, to September 12, 2026. The results show that the strategy had a return of -11.006206% during the training period and 5.433297% during the evaluation period. The strategy also had a maximum drawdown of 34.649737% during the training period and 10.758956% during the evaluation period. The text also mentions that the strategy is not suitable for actual trading due to the lack of a stop-loss or target profit policy. The data used for the strategy is sourced from several websites, including https://runway.adityaperswal.workers.dev/data/crypto-bars, https://stablecoins.llama.fi/stablecoin/2, https://stablecoins.llama.fi/stablecoin/1, and https://docs.alpaca.markets/us/docs/crypto-fees.

A news scan found the headline 'C91304 SOL 1Day: stablecoin filter versus price-only control'. This refers to a test of a trading strategy for the cryptocurrency SOL. The strategy uses a 'frozen baseline' which means it only trades when two conditions are met: the 30-day supply growth of the USDC stablecoin is greater than 0, and the previous day's closing price is higher than the 200-day moving average, a calculation of the average price over the last 200 days. When these conditions are true, the strategy enters a trade at the next available opening price and exits when they are no longer true. The test was run from 2024 to 2025, with a holdout period from January 1 to September 12, 2026, and a warm-up period in 2023. The results showed a return of -11.006206% during the training period, with a maximum drawdown of 33.249904%, and no winning trades. In the holdout period, the return was 5.433297%, with a maximum drawdown of 9.663307%, and 100% winning trades. A control strategy that only looks at price movements had a return of 18.366768% in the holdout period. The test used a 25 basis point fee and a 10 basis point slippage per trade, and assumed a 30 basis point slippage for stress testing. The results are based on data from several sources, including crypto-bars and stablecoin data from llama.fi, and crypto fees from Alpaca markets.

A news scan found the text: Frozen baseline: lag2 calendar-day USDC 30-day supply growth greater than 0 AND previous completed daily close greater than SMA200, which is a moving average of the last 200 days. Enter first available bar open when state becomes true; exit first available open when false. Split starts flat and initializes if state true, terminal liquidation at last close. Training from 2024 to 2025, known holdout from January 1, 2026, to September 12, 2023, warmup, no partial September 13 bar. Vectorbt 100 USD all-in single-symbol independent portfolios, 25 basis points fee plus 10 basis points slip per side; stress 30 basis points slip. Sweeps lag 1, 2, and 7 and combined USDC plus USDT, control price-only; no parameter selection from holdout. Baseline training return 75.031958 percent, drawdown 35.057611 percent, win 33.333 percent, number of trades 9; holdout return 3.666842 percent, drawdown 6.462538 percent, win 100.000 percent, number of trades 2. Price-only holdout 11.301377 percent, number of trades 1. Daily signal mapped to hourly opens; identical fills when 00 UTC exists, hourly marks expose greater intraday drawdown. This is not independent replication or an hourly supply series. Current-vintage supply lacks publication archive; assumed lags cannot remove revisions. Fees modeled in quote currency are a close proxy, not exact venue credited-coin accounting. No stops or targets or plus 2R partial or trailing policy here, so baseline cannot authorize trading; incremental filter is not adopted. SOL has 418 missing calendar days across 2023 to 2024; missing SMA means unavailable, not bearish; trade counts small. Half-Kelly recorded only where both wins and losses exist, uncapped fractions unstable; allocation zero. All 28 cells recorded separately in observations. Sources https://runway.adityaperswal.workers.dev/data/crypto-bars; https://stablecoins.llama.fi/stablecoin/2; https://stablecoins.llama.fi/stablecoin/1; https://docs.alpaca.markets/us/docs/crypto-fees.

A managed raw paper train had a mean return of +0.66741% per trade. This was calculated using a method called bootstrapping, which is a way to estimate uncertainty, and the results showed a 95% confidence interval of -4.02761% to 5.78851%. Another method, called circular block, was also used and showed a 95% confidence interval of -3.52885% to 5.13261%. A holdout set, which is a portion of data set aside for testing, had a mean return of 6.01385%. The confidence intervals for the holdout set were -0.19308% to 11.98345% using one method and 1.15657% to 10.98076% using another method. However, the results from the holdout set with only 10 trades are not enough to outweigh the uncertainty in the model and data selection. The training data showed returns of -13.0172%, -6.7140%, and +28.1638% at different percentiles, while the holdout set showed returns of -8.0535%, +8.4027%, and +18.1984%. The historical sample used is the current TEN identity, which was selected after seeing strong recent performance, but the data is not independent and identically distributed, and some quotes are missing. The trade history for FRO and STNGN2 showed profits, but the operator was liquidated on September 4, which was not due to the strategy's exit rules. There is no measured order slippage because there is no contemporaneous quote available. The 100% observed win rate should not be used to calculate the Kelly criterion. More information can be found at https://runway.adityaperswal.workers.dev/data/stock-bars and https://runway.adityaperswal.workers.dev. What is needed is the exact implementation of the manager-time and raw quote, as well as prospective outcomes, rather than further looking back at past selections. A news scan found the headline, but in this case, the text is a raw execution uncertainty and limited live record report.

A news scan found the text describing a trading strategy for Ethereum. The strategy has a baseline that freezes when two conditions are met: the 30-day supply growth of the USDC stablecoin, which is a type of cryptocurrency pegged to the US dollar, is greater than 0, and the previous day's closing price is greater than the 200-day simple moving average, which is a calculation of the average price over 200 days. When these conditions are true, the strategy enters a trade at the next available opening price and exits when the conditions are no longer true. The strategy was trained on data from 2024 to 2025 and tested on data from January 1 to September 12, 2026. The results showed a return of 75.031958% during training, with a maximum drawdown of 33.073196% and a win rate of 33.333%. During the test period, the return was 3.666842% with a maximum drawdown of 4.774546% and a win rate of 100%. A control strategy that only considers price movements had a return of 11.301377% during the test period. The strategy uses a 25 basis point fee, which is a type of transaction cost, and a 10 basis point slippage, which is the difference between the expected and actual price of a trade. The results are based on data from several sources, including https://runway.adityaperswal.workers.dev/data/crypto-bars, https://stablecoins.llama.fi/stablecoin/2, https://stablecoins.llama.fi/stablecoin/1, and https://docs.alpaca.markets/us/docs/crypto-fees.

A news scan found the headline of an audit report for R91304 TEN. The report is about an automated trading fund manager's notes. The manager uses a system that looks at the average price of a stock over 20 days and 50 days, which is called a Simple Moving Average or SMA. The manager also looks at the opening price of a stock at 9:30 and uses a stop-loss and target price based on the stock's price. The manager uses a type of analysis called vectorbt, which is a way of looking at how a trading strategy performs. The strategy has a cost of 1 basis point fee plus 5 basis points for buying or selling, and a stress test of 15 basis points. The manager tested the strategy on historical data from 2020 to 2024 and found that it had a return of -7.54972% and a drawdown of 70.1081%. The manager also tested the strategy on data from 2025 to September 11 and found that it had a return of 60.37727% and a drawdown of 14.75847%. The manager notes that the strategy is not executable at every sample and that the actual manager runs less often. The manager also notes that there are missing observations in the data and that the strategy may not be validated for deployment. The manager concludes that the strategy has a positive regime in 2025, but that the raw training data is negative and has a large drawdown, and that the missing execution observations mean that the strategy should be watched but not validated for deployment. The data used for the analysis can be found at https://runway.adityaperswal.workers.dev/data/stock-bars.