Notes

A news scan found the trade results for BTC/USD with the following statistics: the return percentage was -23.668860167219393, the drawdown was 30.328471266594047, and the win rate was 36.84210526315789. The sample size was 19. The trade used a 1Day time frame, immediate15 mode, and 10 basis points slippage. The trade returns percentage was as follows: -12.7252881448484, 24.191241448020584, -7.776468489038823, -10.068658428499678, 5.764010686768617, -7.726424274460319, -10.878623242158792, -0.09127024185183741, 0.28698764125554277, 18.34505067464651, 5.338985465006241, -8.90067782784204, -7.3099019726048375, 9.115203455159726, -5.88819831546068, 5.91090167579571, -4.77802938445061, -4.749415420005787, -6.165658759408965. The trade strategy included 15-minute open checks, a 2ATR stop, and a residual target for a net 3R. A 2ATR stop means a stop-loss order set at 2 times the average true range, which is a measure of price volatility. The trade also had a quarter profit target and a break-even point at a selected cadence. Horizon exits waited for the selected cadence. The first event was retained on collisions and expiry was after 2 hours. Fees were 25 basis points each plus 10 or 30 basis points slippage. The training data was from 2024 to 2025, with 2026 held out for testing, and included a forced terminal close. The full sample missed count included pre-2024 warm-up data with no 15-minute history and should not be interpreted as within-window misses. The model did not fill all orders. All 48 cells were used, and timestamps were managed in the results.json file. https://runway.adityaperswal.workers.dev/data/crypto-bars

A trading plan for SOL/USD, which is a pair of the Solana cryptocurrency and the US dollar, was completed on September 12 and retrieved on September 13. The plan's details include a closing price of 101.7655 and an entry point of 101.86726549999999. The average true range, or ATR, which is a measure of price volatility, is 5.457558567074999 over 20 days. The risk fraction, which is the amount of risk taken, is 0.10715038909285338. The stop loss, which is the price at which to sell to limit losses, is 90.95214836585. The gross 3R target, which is a profit goal, is 134.61261690245, and the net 3R target, which is the profit goal after fees, is 137.06755180090053. This represents a potential gain of 34.55505174122941 percent. The potential loss if the stop loss is reached is 11.249744778048354 percent. The simple moving average over 200 days, or SMA200, is 83.0413278153425. The current trend is positive. The net 3R target and gross 3R target are different because the quarter's policy requires a larger target after fees. No orders or actual levels were adopted. https://runway.adityaperswal.workers.dev/data/crypto-bars

A trading plan for Ethereum, symbol ETH, was completed on September 12, UTC, and retrieved on September 13. The closing price was 2525.045, and the entry price was 2527.570045. The average true range, or ATR, which is a measure of price volatility, was 94.40364558315002 over 20 days. The risk fraction, which is the amount of risk taken, was 0.07469913308230397. The stop loss, which is the price at which to sell to limit losses, was 2338.7627538337. The gross 3R target, which is a profit goal, was 3093.9919184989003, and the net 3R target, which is the profit goal after fees, was 3154.904714000892. The net 3R gain percentage, which is the percentage profit after fees, was 24.819674938064562, and the net stop loss percentage, which is the percentage loss after fees, was 8.02405344321876. The simple moving average over 200 days, or SMA200, was 2055.8333431083274. The current trend is positive. The entry price was illustrated at a close price multiplied by 1.001, not a quote. The quarter policy requires a larger residual target, which is why the net 3R full target and gross 3R are different. No order or actual adopted levels were made. https://runway.adityaperswal.workers.dev/data/crypto-bars

A trading regime for BTC/USD, labeled C91306, was completed on September 12, UTC time, and the results were retrieved on September 13. The symbol for Bitcoin is BTC, and it closed at a price of 77266.385. The entry point was 77343.65138499999. A measure of volatility, known as the average true range or ATR, was 2335.032182487298 over 20 days, which is a way to calculate the range of an asset's price movement. The risk fraction, or the amount of risk taken, was 0.06038070716015235. A stop-loss, which is a price level that triggers a sale to limit losses, was set at 72673.5870200254. The gross 3R target, which is a profit goal before fees, was 91353.84447992379, and the net 3R target, which is the profit goal after fees, was 93217.77621527233. This represents a potential net gain of 20.52414716141908 percent. The potential net stop loss, or loss after fees, was 6.600785807276743 percent. The simple moving average over 200 days, or SMA200, was 70095.06604458945, which is a way to smooth out price data over time. The current trend is positive. The net 3R target and gross 3R target are different due to a quarter policy that requires a larger residual target. No orders or actual levels were adopted. https://runway.adityaperswal.workers.dev/data/crypto-bars

A news scan found the headline 'C91306 holdout quarter2R_BE_net3R synchronized Monte Carlo'. The automated trading fund manager used a holdout window, a policy called quarter2R_BE_net3R, and the symbol three29.785probes. The manager ran 10,000 paths, using 254 days of source data. The results showed that 89.355 was deployed, with a mean profit and loss of -0.8122506651302756. The worst profit and loss was -10.852960220554175, and there was a 0 probability of a 3 percent account loss. The manager used a seed of 91306, running 10,000 paths over 480 hours, with 20 synchronized 24-hour blocks sampled by future day of the week, starting on Sunday. The manager trained on data from 2024 to 2025, and tested on a holdout pool from 2026. The manager initially used a spot entry with a 1.001 accounts buy slip, and fees of 25 basis points, plus a 10 basis points sell slip. The manager checked hourly, with partial checks every 2 hours, using a proxy, not actual manager or 15-minute live entry conditioned odds. The expected return was not forecast, with a fixed current average true range, and the historical pool could not model unseen losses. https://runway.adityaperswal.workers.dev/data/crypto-bars

A news scan found the headline related to a trading simulation for SOL/USD. The simulation used a method called Monte Carlo, which is a way of making predictions using random samples. It was run on a set of data called the holdout quarter, using a policy called quarter2R_BE_net3R, which is a set of rules for making trades. The symbol being traded was SOL. The simulation ran 10,000 times, using 254 days of data from the past. The results showed a 46.27% chance of stopping a trade, a 1.76% chance of reaching a target, a 51.97% chance of running out of time, a 13.68% chance of making a partial trade, and a 39.51% chance of winning. On average, the trades resulted in a return of -1.22%, with an average win of 12.32% and an average loss of -10.07%. The worst-case scenario was a loss of -15.50%. The simulation also calculated a value called the quarter probe p99 loss, which was $4.22. The target gain was 38.93%. The average time it took to exit a trade was 362 hours. The simulation was run using a seed number of 91306, and it sampled data from future days of the week, starting on Sunday. The data was split into two groups: one for training, from 2024-2025, and one for testing, from 2026. The simulation assumed an initial buy with a 0.1% slip, and a 25 basis point fee, plus a 10 basis point sell slip. It checked the trades every hour, but only updated the partial trades every 2 hours. This was not an actual trading manager, but rather a simulation. The expected return was not forecast, and the simulation used a fixed current average true range, which is a measure of price volatility. The historical data used in the simulation cannot model unseen losses, which are losses that have not happened before. https://runway.adityaperswal.workers.dev/data/crypto-bars

A news scan found the following data: the window is a holdout period, the policy is called quarter2R_BE_net3R, and the symbol is ETH, which is a type of cryptocurrency. The report is based on 10,000 paths, using 254 days of source data. The stop probability is 59.61%, the target probability is 6.92%, and the time probability is 33.47%. The partial probability is 24.81% and the win probability is 36.91%. The mean return is -0.60%, the mean win is 12.13%, and the mean loss is -8.05%. The worst sample loss is -12.22% and the target gain is 28.11%. The report also mentions a seed, 91306, and 10,000 paths over 480 hours, with 20 synchronized 24-hour complete UTC day blocks sampled by future day of week starting Sunday. The training data is from 2024-25 and the holdout data is from 2026, and they are separate. The spot initially has an entry and a 0.1% buy slip, with 25 basis point fees and a 10 basis point sell slip. The report checks hourly, with partial checks every 2 hours, but this is not an actual manager or 15-minute live entry conditioned odds. The expected return is not a forecast, the current average true range is fixed, and the historical pool cannot model unseen losses. https://runway.adityaperswal.workers.dev/data/crypto-bars

A news scan found the following data: the window is a holdout period, the policy is called quarter2R_BE_net3R, and the symbol is BTC. The simulation used 10000 paths and considered 254 days of past data. The stop probability is 56.08%, the target probability is 3.56%, the time probability is 40.36%, the partial probability is 20.53%, and the win probability is 36.71%. The mean return is -0.9019300226542327%, the mean win is 8.636151571050062%, and the mean loss is -6.4342886149260705%. The worst 1% return is -8.782240210887945%, and the worst sample loss is -10.342024717816%. The target gain is 23.340149071215286%. The simulation was run with a seed of 91306, using 10000 paths over 480 hours, with 20 synchronized 24-hour blocks, starting on Sunday. The data was split into training and holdout periods, from 2024-2025 and 2026, respectively. The simulation assumed an initial spot entry with a 0.1% buy slip and 25 basis point fees, plus a 10 basis point sell slip. The simulation checked conditions hourly, with partial checks every 2 hours, but this is not an actual manager or live entry condition. The expected return is not a forecast, and the simulation used a fixed current average true range, which cannot model unseen losses. https://runway.adityaperswal.workers.dev/data/crypto-bars

A news scan found the headline about a trading simulation called C91306. This simulation used a method called Monte Carlo to test a trading policy called full_net3R on a symbol named three29.785probes. The simulation ran 10,000 paths, which is a way to test many possible outcomes, over 254 days of data. The results showed a mean profit and loss, or pnl, of -0.8384589462807206. The worst loss was -10.852960220554175 and there was a 0 percent probability of losing 3 percent of the account. The simulation started with a deployed amount of 89.355. It used a technique called synchronized sampling, which means it looked at data from the same time of day, every day, for 480 hours. The data was split into training and testing sets, from 2024 to 2025 and 2026, respectively. The simulation assumed a 0.1 percent fee to buy and a 0.25 percent fee to sell, and it checked the results every hour, but only looked at partial data every 2 hours. The simulation did not try to predict future returns, and it used a fixed measure of market volatility called ATR, which stands for Average True Range, a way to measure how much prices move. The simulation also did not try to model losses that had not been seen before. https://runway.adityaperswal.workers.dev/data/crypto-bars

A news scan found the following data line: a trading simulation for SOL/USD used a holdout window, a full_net3R policy, and the SOL symbol. The simulation had 10,000 paths, used 254 days of source data, and had a 45.7% stop probability, 3.07% target probability, 51.23% time probability, and 0% partial probability. The win probability was 39.19%, the mean return was -1.23%, the mean win was 12.45%, and the mean loss was -10.04%. The worst 1% return was -14.17% and the worst sample loss was -15.49%. The simulation used a seed of 91306, 10,000 paths, and 480 hours, with 20 synchronized 24-hour blocks sampled by future day of the week starting on Sunday. The training data was from 2024-2025 and the holdout data was from 2026. The simulation initially used a spot entry with a 0.1% buy slip and 25 basis point fees plus a 10 basis point sell slip. Hourly checks were used, with partial checks every 2 hours as a proxy, not actual manager or 15-minute live entry conditioned odds. The expected return was not forecast, the current average true range was fixed, and the historical pool cannot model unseen losses. https://runway.adityaperswal.workers.dev/data/crypto-bars

A news scan found the following data line: a holdout window with a policy of full_net3R for the symbol ETH. The data includes 10,000 paths, 254 source days, a stop probability of 57.1%, a target probability of 9.77%, a time probability of 33.13%, and a win probability of 34.51%. The mean return is -0.66%, the mean win is 13.17%, and the mean loss is -7.94%. The worst sample loss is -12.22%. The data also includes a target gain of 24.82% and a mean exit time of 296.05 hours. This data is based on a simulation with a seed of 91306, using 10,000 paths and 480 hours, with 20 synchronized 24-hour complete UTC day blocks sampled by future day of week starting on Sunday. The training data is from 2024-2025 and is compared to a known 2026 holdout pool. The simulation includes a spot initially entry with a 0.1% buy slip and 25 basis point fees plus 10 basis point sell slip. Hourly checks are used, with partial checks every 2 hours as a proxy, but not actual manager or 15-minute live entry conditioned odds. The expected return is not forecasted, with a fixed current average true range, and the historical pool cannot model unseen losses. https://runway.adityaperswal.workers.dev/data/crypto-bars

A news scan found the following data line: a Monte Carlo simulation for BTC/USD with the following parameters: window is holdout, policy is full_net3R, symbol is BTC, 10000 paths, 254 source days, stop probability is 54.72%, target probability is 5.73%, time probability is 39.55%, partial probability is 0%, win probability is 35.37%, mean return is -0.93%, mean win is 9.05%, mean loss is -6.39%, worst 1% return is -8.79%, and worst sample is -10.34%. The simulation used seed 91306, 10000 paths, and 480 hours, with 20 synchronized 24-hour complete UTC day blocks sampled by future day of week starting Sunday. The training data is from 2024-2025 and the holdout data is from 2026. The simulation assumes an initial spot entry with a 0.1% buy slip and 25 basis point fees plus 10 basis point sell slip. Hourly checks are used, with partial only every 2 hours as a proxy, not actual manager or 15-minute live entry conditioned odds. The expected return is not forecasted, with a fixed current average true range, and the historical pool cannot model unseen losses. https://runway.adityaperswal.workers.dev/data/crypto-bars

A train quarter2R_BE_net3R synchronized Monte Carlo simulation was run. The simulation used a window for training, with a policy called quarter2R_BE_net3R, and a symbol called three29.785probes. It used 10000 paths and 488 source days. The simulation was deployed with 89.355, and the mean profit and loss was 0.0392655997184675. The worst profit and loss was -12.093032967877422, and the probability of a 3 percent loss of the account was 0. The seed used was 91306, with 10000 paths and 480 hours. The simulation was run in 20 synchronized 24-hour complete UTC day blocks, starting on Sunday. The simulation was trained on data from 2024-25 and tested on known holdout pools from 2026, separately. The spot initially had an entry and a 1.001 accounts buy slip, with 25 basis point fees and a 10 basis point sell slip. The simulation checked hourly, with partial checks every 2 hours, but this was not an actual manager or a 15-minute live entry conditioned odds. The expected return was not forecast, the current average true range was fixed, and the historical pool cannot model unseen losses. https://runway.adityaperswal.workers.dev/data/crypto-bars

A news scan found the following data line: a train quarter2R_BE_net3R synchronized Monte Carlo simulation for SOL/USD, also known as a computer model that predicts prices, with the following results: window is train, policy is quarter2R_BE_net3R, symbol is SOL, paths are 10000, source days are 488, stop probability percentage is 56.44, target probability percentage is 6.91, time probability percentage is 36.65, partial probability percentage is 24.61, win probability percentage is 39.24, mean return percentage is -0.096, mean win percentage is 16.93, mean loss percentage is -11.09, p01 return percentage is -14.75, worst sample percentage is -16.94, half kelly is 0, quarter probe p99 loss USD is 4.39, target gain percentage is 38.93, mean exit hours is 304.16. The simulation used seed 91306, 10000 paths times 480 hours, 20 synchronized 24 hour complete UTC day blocks sampled by future day of week starting Sunday. The training data is from 2024-25 and the known holdout pools are from 2026, which are separate. The spot initially had an entry of 1.001, with a buy slip, 25 basis points fees, and 10 basis points sell slip. The simulation had hourly checks, partial only every 2 hours proxy, and is not an actual manager or 15 minute live entry conditioned odds. The expected return is not forecast, the current ATR is fixed, and the historical pool cannot model unseen losses. https://runway.adityaperswal.workers.dev/data/crypto-bars

A news scan found the following data: the automated trading fund manager used a synchronized Monte Carlo method for the ETH/USD currency pair. The training window used 488 days of data and 10,000 paths to estimate probabilities. The results showed a 66.06% stop probability, 11.05% target probability, 22.89% time probability, 29.63% partial probability, and 36.08% win probability. The mean return was -0.47%, with a mean win of 14.12% and mean loss of -8.70%. The worst sample showed a -18.65% loss. The target gain was 28.11%. The training data was from 2024-2025 and was compared to a holdout pool from 2026. The simulation used a 25 basis point fee and a 10 basis point sell slip. The results are based on hourly checks and do not reflect actual management or live entry conditions. https://runway.adityaperswal.workers.dev/data/crypto-bars

A news scan found the following data line: a train quarter2R_BE_net3R synchronized Monte Carlo simulation for BTC/USD had the following results. The simulation used 10000 paths and considered 488 days of data. The stop probability was 49.59%, the target probability was 5.1%, and the time probability was 45.31%. The partial probability was 27.59%, and the win probability was 44.81%. The mean return was 0.6948911525055191%, the mean win was 9.444632733281523%, and the mean loss was -6.40922046616766%. The worst sample loss was -10.334308390030266%, and the half Kelly criterion was 0.573979658083215. The quarter probe p99 loss in USD was 2.498397406158138, and the target gain was 23.340149071215286%. The mean exit time was 329.4203 hours. This simulation, known as C91306, used 10000 paths and 480 hours, with 20 synchronized 24-hour complete UTC day blocks sampled by future day of week starting on Sunday. The training data was from 2024-25 and was compared to a known 2026 holdout pool. The spot initially had an entry and 1.001 accounts with a buy slip, with 25 basis point fees and 10 basis point sell slip. Hourly checks were used, with partial only every 2 hours as a proxy, not actual manager or 15-minute live entry conditioned odds. The expected return was not forecasted, with a fixed current average true range, and the historical pool cannot model unseen losses. https://runway.adityaperswal.workers.dev/data/crypto-bars

A train full_net3R synchronized Monte Carlo simulation was run. The simulation used a window for training, a policy called full_net3R, and a symbol called three29.785probes. It generated 10000 paths and used 488 days of source data. The deployed amount was 89.355 and the mean profit and loss was 0.08600822750196574. The worst profit and loss was -12.093032967877422 and the probability of a 3 percent loss in the account was 0. The simulation used a seed of 91306 and ran 10000 paths over 480 hours, with 20 synchronized 24-hour blocks sampled by the day of the week, starting on Sunday. The training data was from 2024 to 2025 and was compared to a holdout pool from 2026. The initial spot entry had a 1.001 accounts buy slip and 25 basis point fees, plus a 10 basis point sell slip. The simulation checked the data hourly, but only used partial data every 2 hours as a proxy, and did not use actual manager or 15-minute live entry conditioned odds. The expected return was not forecasted, the current average true range was fixed, and the historical pool could not model unseen losses. A news scan found the seed and simulation details. https://runway.adityaperswal.workers.dev/data/crypto-bars

A news scan found the following data line: a train full_net3R synchronized Monte Carlo simulation for SOL/USD with the following parameters: window is set to train, policy is full_net3R, symbol is SOL, 10000 paths were used, source days were 488, stop probability percentage is 54.32, target probability percentage is 9.85, time probability percentage is 35.83, partial probability percentage is 0, win probability percentage is 37.63, mean return percentage is -0.0392, mean win percentage is 18.10, mean loss percentage is -10.98, p01 return percentage is -14.75, worst sample percentage is -16.94, half Kelly is 0, quarter probe p99 loss in USD is 4.39, target gain percentage is 34.56, and mean exit hours is 300.83. This simulation used seed 91306, 10000 paths, and 480 hours, with 20 synchronized 24-hour complete UTC day blocks sampled by future day of the week starting Sunday. The training data is from 2024-25 and the known holdout pools are from 2026, separate from the training data. The spot initially had an entry and 1.001 accounts with a buy slip, 25 basis point fees, and 10 basis point sell slip. Hourly checks were used, with partial only every 2 hours as a proxy, not actual manager or 15-minute live entry conditioned odds. The expected return is not forecast, the current ATR is fixed, and the historical pool cannot model unseen losses. https://runway.adityaperswal.workers.dev/data/crypto-bars

A news scan found the following data line: a train full_net3R synchronized Monte Carlo simulation for ETH/USD, also known as Ethereum versus the US dollar, was run with the following parameters: the window was set to train, the policy was full_net3R, and the symbol was ETH. The simulation used 10,000 paths and considered 488 days of historical data. The stop probability percentage was 62.24%, the target probability percentage was 14.51%, the time probability percentage was 23.25%, and the partial probability percentage was 0%. The win probability percentage was 33.13%, the mean return percentage was -0.42%, the mean win percentage was 16.08%, and the mean loss percentage was -8.59%. The worst sample percentage was -18.70% and the worst 1% sample return percentage was -13.80%. The half Kelly criterion was 0, and the quarter probe p99 loss in US dollars was $4.11. The target gain percentage was 24.82%. The simulation was run with a seed of 91306, using 10,000 paths and 480 hours of data, with 20 synchronized 24-hour complete UTC day blocks sampled by future day of the week starting on Sunday. The data was split into training and holdout sets, with the training set covering 2024-2025 and the holdout set covering 2026. The simulation assumed an initial spot entry with a 0.1% buy slip and 25 basis point fees plus 10 basis point sell slip. Hourly checks were used, with partial checks every 2 hours, but this was not an actual manager or 15-minute live entry conditioned odds. The expected return was not forecast, the current average true range was fixed, and the historical pool cannot model unseen losses. https://runway.adityaperswal.workers.dev/data/crypto-bars

A news scan found the following data line: a train full_net3R synchronized Monte Carlo simulation for BTC/USD, also known as a computer model that uses random sampling to estimate outcomes, was run with the following parameters: the window was set to train, the policy was set to full_net3R, a type of decision-making rule, and the symbol was BTC. The simulation used 10000 paths, which are like different possible scenarios, and considered 488 days of past data. The results showed a stop probability of 47.93%, a target probability of 8.53%, a time probability of 43.54%, and a partial probability of 0%. The win probability was 43.68%, the mean return was 0.747636942579093%, the mean win was 9.900968568569386%, and the mean loss was -6.351395824169061%. The worst 1% of returns was -8.393692050829705%, and the worst sample was -10.334308390030266%. The half Kelly criterion, a measure of risk, was 0.5944480354894206. The quarter probe p99 loss in US dollars was 2.5000611773396275, and the target gain was 20.52414716141908%. The mean exit time was 326.0381 hours. This simulation, known as Seed 91306, used 10000 paths and 480 hours of data, with 20 synchronized 24-hour blocks, starting on Sunday. The data was split into training and holdout sets, from 2024-2025 and 2026, respectively. The simulation assumed an initial spot entry with a 0.1% buy slip, 25 basis point fees, and a 10 basis point sell slip. Hourly checks were used, with partial checks every 2 hours, but this was not an actual manager or live entry condition. The expected return was not forecasted, the current average true range, a measure of volatility, was fixed, and the historical pool of data cannot model unseen losses. https://runway.adityaperswal.workers.dev/data/crypto-bars

A news scan found the following data: the symbol is SOL, the time frame is 1 hour, and the window is holdout. The sample size is 6, the mean trade percentage is 1.1647773281099318, and the mean confidence interval 95 percentage is between -5.5853095053415105 and 8.97919902663138. The percentage without the best return is -11.421423222400984. There are 1 target and 2 stops, with a maximum stop overshoot percentage of 0.37118211791479255. The subsets are as follows: a group called weekend with a flag set to false, a sample size of 6, a mean percentage of 1.1647773281099323, and a win percentage of 50; a group called weekend with a flag set to true, a sample size of 0, and no mean or win percentage; a group called trend with a flag set to false, a sample size of 5, a mean percentage of 0.2757357664312579, and a win percentage of 40; and a group called trend with a flag set to true, a sample size of 1, a mean percentage of 5.609985136503304, and a win percentage of 100. This is based on a gross return calculation with a delay of 0 and a slip of 10 basis points, using 10000 resamples with a seed of 913061. The trades are non-overlapping and per coin, but the regimes are not independent, and the confidence interval is conditional and descriptive. The simple moving average of 200 uses the previous completed day, with no missing trend promotion. Removing the best return is a sensitivity test, not an instruction to cut winners. https://runway.adityaperswal.workers.dev/data/crypto-bars

A news scan found the following data: the symbol is SOL, the time frame is 1 hour, and the window is train. The sample size is 11, the mean trade percentage is 2.906327207954995, and the mean confidence interval 95 percentage is between -4.609960769367043 and 10.363997420303992. The mean percentage without the best return is 2.736657228135364. There are 0 targets and 4 stops, with a maximum stop overshoot percentage of 2.2849050899438037. The subsets are as follows: a group called weekend with a flag set to false, with 7 samples, a mean percentage of -1.6859163455918582, and a win percentage of 42.857142857142854, and a group called weekend with a flag set to true, with 4 samples, a mean percentage of 10.94275342666199, and a win percentage of 75. Another group called trend with a flag set to false, with 7 samples, a mean percentage of 8.195046826130488, and a win percentage of 71.42857142857143, and a group called trend with a flag set to true, with 4 samples, a mean percentage of -6.348932123852117, and a win percentage of 25. The base gross is 3R, with a delay of 0 and a slip of 10 basis points. The trade resamples have a seed of 913061, with 10000 independent and identically distributed trades. The positions are non-overlapping per coin, but the regimes are not independent. The confidence interval is conditional and descriptive. The simple moving average 200, a calculation of the average price over 200 days, uses the previous completed day, with no missing trend promotion. Remove best is a sensitivity test, not an instruction to cut winners. https://runway.adityaperswal.workers.dev/data/crypto-bars

A news scan found the following data line: the symbol is SOL, the time frame is 1 Day, and the window is holdout. The sample size is 7, the mean trade percentage is -4.466468344010586, and the mean confidence interval 95 percentage is between -10.895772108043028 and 3.0765596213045514. The mean percentage without the best return is -38.98400621614424. There are 1 target and 4 stops, with a maximum stop overshoot percentage of 5.013033639018412. The subsets are as follows: a group called weekend with the flag set to false, with 6 samples, a mean percentage of -3.8710428891206927, and a win percentage of 33.33333333333333, and a group called weekend with the flag set to true, with 1 sample, a mean percentage of -8.03902107334995, and a win percentage of 0, and a group called trend with the flag set to false, with 5 samples, a mean percentage of -7.438417918780978, and a win percentage of 20, and a group called trend with the flag set to true, with 2 samples, a mean percentage of 2.9634055929153913, and a win percentage of 50. This is based on a gross return calculation with a delay of 0 and a slip of 10 basis points, with 10000 independent and identically distributed trade resamples, and a seed of 913061, using non-overlapping positions per coin, but the regimes are not independent, and the confidence interval is conditional and descriptive. The simple moving average 200 uses the previous completed day, with no missing trend promotion, and remove best is a sensitivity test, not an instruction to cut winners. https://runway.adityaperswal.workers.dev/data/crypto-bars

A news scan found the following data: the symbol is SOL, the time frame is 1 Day, and the window is train. The sample size is 9, the mean trade percentage is 4.142065346208124, and the mean confidence interval 95 percentage is between -4.405303078920585 and 12.463416365819457. The mean percentage without the best return is 9.746627434173828. There are 0 targets and 3 stops, with a maximum stop overshoot percentage of 2.780443987306969. The subsets are as follows: a weekend group with a flag set to false, a sample size of 7, a mean percentage of 3.662423446692022, and a win percentage of 57.14285714285714, and a weekend group with a flag set to true, a sample size of 2, a mean percentage of 5.8208119945144805, and a win percentage of 50. There is also a trend group with a flag set to false, a sample size of 6, a mean percentage of 7.742566450197366, and a win percentage of 66.66666666666666, and a trend group with a flag set to true, a sample size of 3, a mean percentage of -3.0589368617703596, and a win percentage of 33.33333333333333. The base gross is 3R, with a delay of 0 and a slip of 10 basis points. The analysis is based on 10000 independent and identically distributed trade resamples, with a seed of 913061. The positions are non-overlapping per coin, but the regimes are not independent, and the confidence interval is conditional and descriptive. The simple moving average 200 uses the previous completed day, with no missing trend promotion. The remove best is a sensitivity test, not an instruction to cut winners. https://runway.adityaperswal.workers.dev/data/crypto-bars

A news scan found the following data: the symbol is ETH, the time frame is 1 hour, and the window is a holdout period. The sample size is 7, the mean trade percentage is 2.0967462839483537, and the mean confidence interval at 95 percent is between -2.683090252876763 and 7.7079149427449005. The mean percentage without the best return is -1.3074192943370755. There is 1 target and 1 stop, with a maximum stop overshoot percentage of 0.36179997528747254. The subsets are as follows: a group called weekend with the flag set to false, which has 7 samples, a mean percentage of 2.0967462839483537, and a win percentage of 57.14285714285714, and another group called weekend with the flag set to true, which has 0 samples, a mean percentage of null, and a win percentage of null, a group called trend with the flag set to false, which has 5 samples, a mean percentage of 2.718900291117916, and a win percentage of 60, and another group called trend with the flag set to true, which has 2 samples, a mean percentage of 0.5413612660244485, and a win percentage of 50. The base gross is 3R, with a delay of 0 and a slip of 10 basis points. There are 10000 IID trade resamples with a seed of 913061, and the positions are non-overlapping per coin, but the regimes are not independent. The confidence interval is conditional and descriptive. The simple moving average, or SMA, uses the previous completed day, and there is no missing trend promotion. The remove best is a sensitivity test, not an instruction to cut winners. https://runway.adityaperswal.workers.dev/data/crypto-bars