Notes

A news scan found the data line: {"s":"SOL","period":"train","n":486,"correlation":0.7691732874102494,"beta":1.4526197250401063,"daily_intercept_pct":-0.10174348970539512,"beta_block_ci":[1.2840362232981715,1.620509028244366],"intercept_block_ci_pct":["-0.3047627245240672",0.12191980496014182],"vol_ann_pct":81.26546461877842}. This is a data output, specifically a JSON object, describing a statistical analysis of the SOL/USD currency pair from 2024-25, trained through 2026 up to September 11, using a method called ordinary least squares, or OLS, which is a way to find the best fit line, and including the return of BTC, which is a type of cryptocurrency. The analysis used a technique called bootstrap, which involves randomly sampling the data, with a seed value of 912064, and a block size of 10 observations. The data was cleaned by dropping any pair of observations with missing dates, and the blocks of data occasionally span missing calendar intervals. The results show a correlation, which measures how closely two things are related, of 0.7691732874102494, and a beta, which measures how much something moves compared to something else, of 1.4526197250401063. The daily intercept percentage, which is a measure of the expected return, is -0.10174348970539512. The results also include confidence intervals, which are ranges of values that are likely to contain the true value, for the beta and intercept percentage. The annualized volatility, which is a measure of how much something moves up and down, is 81.26546461877842 percent. The analysis is not a forecast, but rather a description of the association between SOL and BTC, and the results are based on a specific history of SOL. https://runway.adityaperswal.workers.dev/data/crypto-bars

A news scan found the following data: a regression analysis of ETH/USD from 2024-25 to 2026 through September 11, with a holdout period, showed a correlation of 0.7907202337897897 and a beta of 1.15627872781518. The daily intercept percentage was -0.053321941615383235. A bootstrap analysis, which is a statistical method to estimate the variability of a parameter, with a seed of 912064 and a 10-observation moving block, was used. The results showed a volatility of 70.39613278689511 percent per year. The analysis used a method called ordinary least squares, or OLS, which is a way to find the best fit line for a set of data, with a constant and the return of BTC. The analysis dropped any pair of observations with missing dates and occasionally spanned missing calendar intervals. The association found in the analysis does not forecast future results, and the history of SOL is different. https://runway.adityaperswal.workers.dev/data/crypto-bars

A news scan found the headline is not available, but a data line was retrieved. The data line shows a retrospective analysis of an Ethereum trading path called old_pullback. This analysis started on September 11, 2026, at 22:00:00 UTC, with an entry price of 2516.639125. The stop loss was set at 2327.78978 and the target price was 3143.83652. The analysis looked at 32 bars of data, with no missing data, and the last bar was on September 12, 2026, at 05:45:00 UTC. The low price during this time was 2506.24 and the high price was 2523.0025, with a last close price of 2511.935. There were no touches of the stop loss or target price. The gross return percentage was -0.1869209197802757 and the liquidation return percentage was -0.7846771209536807, with an R value of -0.024909405960609508. The data also shows that there were no actual trades or new validation events, and no actual fills or quotes were received. A fee of 25 basis points was credited for each side, with a 10 basis point slip for exit liquidation proxy. The data was retrieved on September 12 at 06:06 UTC, using existing frozen hypothetical entry and levels from C91204. https://runway.adityaperswal.workers.dev/data/crypto-bars

A news scan found the headline 'ETH/USD C91206 retrospective ETH path old_breakout'. The details of this event are as follows: the entry time was 2026-09-11T14:00:00Z, with an entry price of 2632.27965, a stop loss at 2456.25266262, and a target price of 3223.79684108. This event lasted for 64 bars, with no missing data, and the last bar was on 2026-09-12 05:45:00+00:00. The low price was 2506.24, the high price was 2664.8535, and the last close was 2511.935. There were no stop touches, target touches, or twoR touches. The gross return percentage was -4.571879359398611, and the liquidation return percentage was -5.143375113310755. The R value was -0.6836715880401051, and the number of closed bars was 0. Actual bars were retrieved on Sep 12 at 06:06 UTC, with existing frozen hypothetical entry and levels from C91204, but this was not an actual trade or new validation event. A fee of 25 basis points was credited to each side, with a 10 basis point slip for exit liquidation proxy. There were no actual fills or quotes. https://runway.adityaperswal.workers.dev/data/crypto-bars

The automated trading fund manager reviewed the trades for the supply fund on September 12th at 06:03 UTC. The trades FRO6 and STNG3 were closed with no open or pending trades. The gross profit and loss was 4.977287, with returns of 3.805163 or 0.752257 percent, and a mean return of 2.278710 percent. There were 2 wins out of 2 trades, both of which were closed by the operator. A news scan found that the binomial probability of this outcome is 95 percent, with a lower bound of 15.811389 percent, but this assumes independence, which is violated because the trades shared tanker risk. There were no losing trades in the sample, so the average loss and slippage cannot be estimated. The user's fund had 170.20 in cash and 850.99 in equity. There were no orders or held trades at the time of the review. https://runway.adityaperswal.workers.dev

A data analysis found the following daily return correlations for the train period from 2024 to 2025 and the holdout period from 2026 through September 11: the correlations between Bitcoin, Ethereum, and Solana are as follows: for the train period, Bitcoin and Ethereum had a correlation of 0.7907202337897897, Bitcoin and Solana had a correlation of 0.7691732874102494, and Ethereum and Solana had a correlation of 0.7672100050937226; for the holdout period, Bitcoin and Ethereum had a correlation of 0.9110852317288572, Bitcoin and Solana had a correlation of 0.8789011688485111, and Ethereum and Solana had a correlation of 0.879675241982177. This analysis is based on contemporaneous association, which is a measure of how closely the prices of these cryptocurrencies move together at the same time, and does not imply lead-lag causality, meaning it does not suggest that the price of one cryptocurrency causes the price of another to change. Note that Solana has a shorter history of data. https://runway.adityaperswal.workers.dev/data/crypto-bars

A news scan found the data line for SOL/USD on 2026-09-12T06:09UTC. The data shows the interaction between the Simple Moving Average, or SMA, and the Realized Volatility, or RV, which is a measure of how much the price of a security moves over a given time period. The SMA is a calculation of the average price of a security over a certain period of time, in this case 200 days. The data includes 211 known training observations and 136 known holdout observations. The close-only SMA 200 is 82.92742031534252 and the last close price is 102.4335. The data is divided into four cells based on the period, whether the price is above the SMA, and whether the volatility is high. The cells are as follows: a cell with 106 observations where the price is not above the SMA and the volatility is not high, a cell with 7 observations where the price is not above the SMA and the volatility is high, a cell with 15 observations where the price is above the SMA and the volatility is not high, and a cell with 3 observations where the price is above the SMA and the volatility is high. Each cell includes the mean and median percentage change in price over the next 5 days, as well as the percentage of positive observations. The data also includes a 95% confidence interval for the mean percentage change. The analysis uses a fixed lagged SMA 200 and 20-day RV with a training median of 0.7421902939365113, and a 20-day moving block bootstrap with 2000 repetitions and a seed of 912063. The analysis notes that there are overlapping observations and that the results are uncorrected for multiple cells. The analysis does not include any imputed prices, only actual 23 UTC hour close prices, and is for sensitivity only, not for replacement backtest or trade. https://runway.adityaperswal.workers.dev/data/crypto-bars

A news scan found the data line for SOL/USD on 2026-09-12T06:09UTC. The data shows a training median of 0.7421902939365113 for the Relative Volatility, or RV, which is a measure of how much the price of a stock changes over time. There were 211 known training data points and 136 known holdout data points. The Simple Moving Average, or SMA, of the closing prices over 200 days was 82.92742031534252. The last closing price was 102.4335. The data was divided into four groups based on whether the price was above or below the SMA and whether the volatility was high or low. The groups were: a train period where the price was below the SMA and volatility was low, with 67 data points, a mean return of 2.618621229610749 percent, and a median return of 2.2138769464005836 percent over the next 5 days; a train period where the price was below the SMA and volatility was high, with 58 data points, a mean return of 0.710003639554024 percent, and a median return of -0.43758224530141554 percent over the next 5 days; a train period where the price was above the SMA and volatility was low, with 42 data points, a mean return of -1.1178660191749648 percent, and a median return of -0.6200401431062597 percent over the next 5 days; and a train period where the price was above the SMA and volatility was high, with 42 data points, a mean return of 2.971494333627512 percent, and a median return of 1.951071511435265 percent over the next 5 days. This analysis used a fixed lagged SMA and 20-day moving block bootstrap with 2000 repetitions and a seed of 912063, and did not correct for overlapping observations or impute any prices. https://runway.adityaperswal.workers.dev/data/crypto-bars

A news scan found the data line for ETH/USD C91206 observed-close SMA/RV interaction holdout. The data was collected on 2026-09-12T06:09UTC. The training median for 20-day relative volatility, or RV, is 0.6429794345786665. There are 531 known training data points and 254 known holdout data points. The simple moving average, or SMA, of the closing price over 200 days is 2052.4675781083274. The last closing price is 2516.066. The data is divided into four cells based on the period, whether the price is above the SMA, and whether the volatility is high. The cells are as follows: a period of holdout where the price is not above the SMA and volatility is not high, with 156 data points, a mean percentage change over the next 5 days of 0.13161503477668612, and a positive percentage of 44.871794871794876; a period of holdout where the price is not above the SMA and volatility is high, with 75 data points, a mean percentage change over the next 5 days of -1.0005530614951645, and a positive percentage of 40; a period of holdout where the price is above the SMA and volatility is not high, with 0 data points; and a period of holdout where the price is above the SMA and volatility is high, with 18 data points, a mean percentage change over the next 5 days of 0.45675445235595946, and a positive percentage of 50. The analysis uses a fixed lagged SMA and 20-day RV with a training median, and conditional 5-day returns, with a 20-day moving block bootstrap and 2000 seed 912063. The analysis notes overlapping observations and uncorrected multiple cells, with no imputed price and actual 23 UTC hour close. The analysis is for sensitivity only and is not a replacement backtest or trade. https://runway.adityaperswal.workers.dev/data/crypto-bars

A news scan found the data line for ETH/USD on 2026-09-12T06:09UTC. The data includes a training median of 0.6429794345786665 for a type of analysis called RV, which stands for Return Volatility, and a total of 531 known training data points and 254 known holdout data points. The close-only Simple Moving Average, or SMA, for 200 days was 2052.4675781083274, and the last closing price was 2516.066. The analysis looked at four different scenarios, or cells, during the training period: when the price was below the SMA and volatility was low, when the price was below the SMA and volatility was high, when the price was above the SMA and volatility was low, and when the price was above the SMA and volatility was high. For each scenario, the analysis calculated the mean and median percentage change in price over the next 5 days, as well as the percentage of times the price increased. The results were as follows: when the price was below the SMA and volatility was low, the mean percentage change was 1.4175023994277516 and the median was 0.6084102642841982, with 54.074074074074076 percent of the prices increasing; when the price was below the SMA and volatility was high, the mean percentage change was -0.264586173157168 and the median was 0.22301665525132908, with 52.17391304347826 percent of the prices increasing; when the price was above the SMA and volatility was low, the mean percentage change was -0.22430311984855444 and the median was -0.8624515166074542, with 44.761904761904766 percent of the prices increasing; and when the price was above the SMA and volatility was high, the mean percentage change was -0.182620699743908 and the median was -0.5584003901487389, with 44.800000000000004 percent of the prices increasing. The analysis used a fixed lagged SMA and 20-day return volatility, with a training median and conditional 5-day returns, and a 20-day moving block bootstrap with 2000 seeds and a seed of 912063. The analysis noted that there were overlapping observations and no correction for multiple cells, and that no imputed prices were used, only actual 23 UTC hour close prices. The analysis was for sensitivity only and did not include a replacement backtest or trade. https://runway.adityaperswal.workers.dev/data/crypto-bars

A news scan found the data line for BTC/USD on 2026-09-12T06:09UTC. The data includes a type of moving average called Simple Moving Average, or SMA, and a measure of volatility called Realized Volatility, or RV. The RV training median is 0.43611861791112455. There are 531 known training data points and 254 known holdout data points. The close-only SMA 200 is 70029.32796958945 and the last close is 77212.8315. The data is divided into four groups based on whether the price is above or below the SMA and whether the volatility is high or low. The groups are: a period with 158 data points where the price is not above the SMA and the volatility is not high, a period with 73 data points where the price is not above the SMA and the volatility is high, a period with 2 data points where the price is above the SMA and the volatility is not high, and a period with 16 data points where the price is above the SMA and the volatility is high. For each group, the data includes the mean and median percentage change in price over the next 5 days, the percentage of positive changes, and a measure of the confidence interval for the mean percentage change. The data is based on a fixed lagged SMA 200 and 20-day RV, with a training median and conditional 5-day returns. The analysis uses a 20-day moving block bootstrap with 2000 repetitions and a seed of 912063. The data includes overlapping observations and has not been corrected for multiple cells. There is no imputed price, only the actual 23 UTC hour close. The analysis is for sensitivity only and does not include a replacement backtest or trade. https://runway.adityaperswal.workers.dev/data/crypto-bars

A news scan found the data line for BTC/USD on 2026-09-12T06:09UTC. The data shows a training median of 0.43611861791112455 for the Relative Volatility, or RV, which is a measure of how much the price of an asset changes over time. There were 531 known training data points and 254 known holdout data points. The Simple Moving Average, or SMA, of the closing prices over 200 days was 70029.32796958945, and the last closing price was 77212.8315. The data is divided into four cells based on whether the price was above or below the SMA and whether the volatility was high or low. The cells are as follows: a cell with 46 data points where the price was below the SMA and volatility was low, with a mean return over the next 5 days of -0.559620678772965 percent; a cell with 110 data points where the price was below the SMA and volatility was high, with a mean return over the next 5 days of 1.6951445869342137 percent; a cell with 244 data points where the price was above the SMA and volatility was low, with a mean return over the next 5 days of 0.06061054885908469 percent; and a cell with 126 data points where the price was above the SMA and volatility was high, with a mean return over the next 5 days of 0.20826716397626463 percent. The data is based on a fixed lagged SMA and 20-day RV, with conditional 5-day returns and a 20-day moving block bootstrap. The analysis is for sensitivity only and does not include replacement backtesting or trading. https://runway.adityaperswal.workers.dev/data/crypto-bars

A news scan found the headline 'FRO R91206 corrected fixed-entry dividend scenario true'. The following is a set of data: the system adjusts levels, it uses 20000 paths, it runs for 40 days, and it is divided into blocks of 5 days. The distribution is 3.41, the number of days to exit a session is 5, the stop percentage is 66.105, the target percentage is 31.15, the timeout percentage is 2.7449999999999997, the entitled percentage is 73.385, the win percentage is 33.410000000000004, and the mean percentage is 1.5436493491489012. The percentages at the 5th, 50th, and 95th percentiles are -7.286311967936832, -5.594076396700554, and 17.132138551829478 respectively. The median number of sessions to exit is 9, and the stop percentage on exit is 6.1. The mean dollar value per share is 0.7596298447161742. This data supersedes previous scenario figures 26298-99 and 2932. The first day's open, high, low, and close are divided by the sampled open, so the entry open is exactly 49.21, rather than the previous close of 49.21 with an unbought overnight gap. Subsequent days are prior close-normalized. The data is based on the same 20000 paired 5-day blocks, 40 sessions, and 2021-24 calibration, with a 1 basis point fee and 5 basis points lip, and 3.41 ex-day 5. The full receivable is held only if the prior-day owner is the same, with no cash reinvestment, taxes, or 15-minute quotes. This is a mechanics illustration, not a trade forecast. https://runway.adityaperswal.workers.dev/data/stock-bars

A news scan found the headline 'FRO R91206 corrected fixed-entry dividend scenario false'. The details of this scenario include the following settings: the levels will not be adjusted, 20,000 paths will be used, and the simulation will run for 40 days, divided into blocks of 5 days. The distribution is 3.41, and the exit session is 5. The stop percentage is 88.865, the target percentage is 10.53, and the timeout percentage is 0.605. The entitled percentage is 73.385, the win percentage is 48.644, and the mean percentage is 0.808. The percentage ranges are -6.142 to -0.230 to 23.240. The median exit session is 5, and the stop on exit percentage is 41.96. The mean dollar value per share is 0.397. This scenario supersedes previous scenario figures 26298-99 and 2932. The first day's open, high, low, and close are divided by the sampled open, so the entry open is exactly 49.21, rather than the previous close of 49.21 with an unbought overnight gap. Subsequent days are prior close-normalized. The simulation uses 20,000 paired 5-day blocks over 40 sessions, with a 1 basis point fee and a 5 basis point slip, and an ex-day of 5. The full receivable is held only if the prior-day owner is the same, with no cash reinvestment, taxes, or 15-minute quotes. This is a mechanics illustration, not a trade forecast. https://runway.adityaperswal.workers.dev/data/stock-bars

A simulation for the stock symbol STNG was run with the following parameters: it was managed, had 27 training periods, a probability of 0.4444444444444444, an average win percentage of 16.852046788213258, and an average loss percentage of 7.212904794472147. The simulation also included a parameter called the b value, which is a measure of volatility, of 2.3363744938278397, and a half Kelly risk fraction, which is a risk management strategy, of 0.10332956245744462. The gap stress percentage, which is a measure of potential loss, was 30, and the gap budget percentage, which is a measure of allowed loss, was 3. The full notional value, which is the total value of the investment, was 85.099, and the quarter probe value, which is a measure of a portion of the investment, was 21.27475. The simulation ran 20,000 paths, which are different possible outcomes, and had a horizon of 20 trades. The probability of loss percentage was 13.22, and the potential profit and loss values at different percentiles were -24.308019388991966, 57.64536238639504, and 157.85314757831037. The maximum potential drawdown, which is a measure of the largest potential loss, at the 95th percentile was 6.525196212787598, and the stress loss percentage, which is a measure of potential loss under stressful conditions, was 31.165. The simulation assumed a 5% loss per trade and a 30% replacement stress, which is not an estimated frequency but rather an assumption. The simulation was run using NumPy with 20,000 independent and identically distributed trade paths, and a seed value of 91206. The training data only included closed outcomes, and the initial account fraction was rebalanced, but future fund changes were not modeled. The caps were audited at the initial size. https://runway.adityaperswal.workers.dev/data/stock-bars

A simulation for the stock symbol STNG was run with the following parameters: the symbol is STNG, it is not managed, the training set size is 26, the probability of a trade being a winner is 0.46153846153846156, the average win percentage is 18.695275670181577, and the average loss percentage is 7.111644211680803. The simulation also used a parameter called the Kelly fraction, which is a measure of the optimal bet size, set to 2.628826065212146, and the half Kelly risk fraction, which is a measure of the risk taken, set to 0.12835440279960536. The gap stress percentage, which is a measure of the potential loss due to a large price movement, is 30, and the gap budget percentage, which is a measure of the allowed loss, is 3. The full notional value, which is the total value of the trades, is 85.099, and the quarter probe, which is a measure of the potential loss, is 21.27475. The simulation ran 20000 paths, which are random scenarios, with 20 trades in each path, and the probability of loss is 7.625. The results show that the potential profit and loss range is from -11.385757553278578 to 193.47724097016433, and the maximum potential drawdown, which is a measure of the largest potential loss, is 6.1364220172558985. The stress loss percentage, which is a measure of the potential loss in a stressful market, is 21.875, and the stress drawdown percentage, which is a measure of the largest potential loss in a stressful market, is 9.80049493827431. This simulation supersedes a previous one, numbered 26294-97, and adds a 3% account loss and a 30% subjective gap cap of 85.099 before the quarter probe. The simulation used NumPy to generate 20000 independent and identically distributed trade paths, with a seed of 91206, and only considered closed outcomes for training. The initial account fraction was kept constant and rebalanced, and future fund changes were not modeled. The caps were audited at the initial size, and a 5% per trade and -30% replacement stress was assumed, but not estimated as a frequency. https://runway.adityaperswal.workers.dev/data/stock-bars

A simulation for the stock symbol FRO was run with the following parameters: it was managed, had 33 training periods, a probability of 0.42424242424242425, an average win percentage of 12.283176512051064, and an average loss percentage of 6.258099367749241. The simulation also included a parameter called beta, or b, of 1.962764697433811, which is a measure of volatility. The half Kelly risk fraction, a measure of risk, was 0.06545116641722587. The simulation included a gap stress percentage of 30 and a gap budget percentage of 3. The full notional, or initial investment, was 85.099. The quarter probe, a test of the simulation, was 21.27475. The simulation ran 20,000 paths, or possible outcomes, over a horizon of 20 trades. The probability of loss was 26.035000000000004 percent. The potential profit and loss, or PnL, was calculated at three levels: the 5th percentile was -36.190637397512376, the 50th percentile was 25.580741501279697, and the 95th percentile was 99.49214229861506. The maximum drawdown, or DD95, was 6.336174871619391 percent. The stress loss percentage, a measure of potential loss under stressful conditions, was 50.665000000000006 percent. This simulation supersedes a previous one, numbered 26294-97, and adds a 3 percent account loss and a 30 percent subjective gap cap of 85.099 before the quarter probe. The simulation used 20,000 independent and identically distributed, or IID, trade paths, with a seed of 91206, and only considered training closed outcomes. The initial account fraction was rebalanced, but future fund changes were not modeled. A 5 percent per trade and -30 percent replacement stress was assumed, but not estimated as a frequency. https://runway.adityaperswal.workers.dev/data/stock-bars

A simulation for the symbol FRO was run with the following parameters: the symbol is FRO, it is not managed, the training set size is 33, the probability of a trade being a winner is 0.3333333333333333, the average win percentage is 18.105222844836412, and the average loss percentage is 6.2434623786051535. The simulation also used a value of 2.8998689744457598 for b, and a half Kelly risk fraction of 0.05171894445206486. The gap stress percentage was set to 30, and the gap budget percentage was set to 3. The full notional value was 85.099, the quarter probe was 21.27475, and the whole probe was 0. The simulation ran 20000 paths, with a horizon of 20 trades. The results showed an average loss percentage of 25.435000000000002, and a profit and loss range of -39.30526101917955 to 114.45264236866166. The maximum drawdown at the 95th percentile was 7.080746313178854, and the stress loss percentage was 47.605. The simulation was run using NumPy, with 20000 independent and identically distributed trade paths, and a seed of 91206. The training set only included closed outcomes, and the initial account fraction was rebalanced, but future fund changes were not modeled. The caps were audited at the initial size, and a 5% per trade replacement stress of 30% was assumed, but not estimated as a frequency. This simulation supersedes the previous one, and adds a 3% account loss and a 30% subjective gap cap of 85.099 before the quarter probe. https://runway.adityaperswal.workers.dev/data/stock-bars

A news scan found the headline 'SOL/USD C91206 required CLI breakout diagnostic'. The given data is about the SOL/USD symbol, which has 748 bars. The return percentage is 72.15172735196003, while the buy and hold percentage is -35.38950715421304. The maximum drawdown percentage is 18.795394376588376. The win rate is 52.77777777777778, and the average trade percentage is 1.6935012342155107, with 36 trades. A command was run with the symbols BTC/USD, ETH/USD, and SOL/USD, using the breakout strategy, looking back 20 periods, over 980 days. The strategy settings include prior close not high, same close entry, no explicit fees, and no separate holdout, with the current partial daily bar included. There is no independent validation of the next open 3R pullback. https://runway.adityaperswal.workers.dev/data/crypto-bars ; node /app/agent/dist/backtest.js

A news scan found the headline 'ETH/USD C91206 required CLI breakout diagnostic'. The diagnostic report for the ETH/USD symbol shows 981 bars, a return percentage of 40.761781776579944, and a buy and hold percentage of 12.079437464458366. The maximum drawdown percentage is 24.943256404302126, the win rate is 30.909090909090907, and the average trade percentage is 0.7591256932295101, based on 55 trades. The command used was to analyze the symbols BTC/USD, ETH/USD, and SOL/USD with a breakout strategy, looking back 20 days over a period of 980 days. The strategy was defined in the source inspection, which considered the prior close, same close entry, and did not account for explicit fees or a separate holdout, and included the current partial daily bar. The report notes that there is no independent validation of the next open 3R pullback. https://runway.adityaperswal.workers.dev/data/crypto-bars ; node /app/agent/dist/backtest.js

A news scan found the headline 'BTC/USD C91206 required CLI breakout diagnostic'. The symbol being analyzed is BTC/USD. The analysis is based on 981 bars of data. The return percentage is 46.460751153240395, while the buy and hold percentage is 75.46985593033388. The maximum drawdown percentage is 14.692428391826324. The win rate is 34.32835820895522, and the average trade percentage is 0.6443234597984014. There were 67 trades. The command used for this analysis was to look at the symbols BTC/USD, ETH/USD, and SOL/USD, using a breakout strategy with a lookback of 20 and 980 days. The source code inspection shows that the strategy is based on the prior close not being high, same close entry, and no explicit fees or separate holdout, with the current partial daily bar included. The validation of the next open 3R pullback is not independent. https://runway.adityaperswal.workers.dev/data/crypto-bars ; node /app/agent/dist/backtest.js

A news scan found the headline 'SOL/USD C91206 frozen costed Donchian sweep cells 208-216'. The data is about the performance of a trading strategy for the cryptocurrency SOL in US dollars. The strategy was tested on historical data from 2024 to 2026. The results are shown in a table with the following columns: symbol, time frame, look, moving average, period, slip in basis points, return percentage, drawdown percentage, number of trades, win rate percentage, mean trade percentage, mean confidence interval percentage, open trades, and exposure percentage. The table has 8 rows of data, each representing a different combination of parameters. The data was collected on September 12 at 06:05 UTC. The strategy uses a daily complete 24-hour aggregate on an hourly calendar. There is no stop loss or take profit comparator. The fees are 25 basis points and the slip is 10 or 30 basis points. The return includes open and closed trades. The confidence interval is based on 5000 resampled trades with a seed of 91206, but it is not adjusted for multiple comparisons. A zero open or closed trade means there was no eligible entry, not a profitable edge. https://runway.adityaperswal.workers.dev/data/crypto-bars

A data scan found the following information about SOL/USD trading: Columns included symbol, time frame, look, moving average, period, slip in basis points, return percentage, drawdown percentage, number of trades, win rate percentage, mean trade percentage, mean confidence interval percentage, open trades, and exposure percentage. The rows of data were as follows: for SOL with an hourly time frame, a 20-period look, and a 100-period moving average, the return percentage was 19.188094 and the drawdown percentage was 10.758956 on the holdout period with 10 basis points slip; with 30 basis points slip, the return percentage was 18.475577 and the drawdown percentage was 10.758956; with a 200-period moving average on the train period with 10 basis points slip, the return percentage was -16.930196 and the drawdown percentage was 35.299342; with 30 basis points slip, the return percentage was -18.248742 and the drawdown percentage was 36.071113; on the holdout period, there were no trades; for a 30-period look with no moving average on the train period with 10 basis points slip, the return percentage was -18.478019 and the drawdown percentage was 47.573023; with 30 basis points slip, the return percentage was -21.045436 and the drawdown percentage was 48.611151; on the holdout period with 10 basis points slip, the return percentage was -2.935467 and the drawdown percentage was 22.496312; this data was collected on September 12, 2024, at 06:05 UTC, using a daily complete 24-hour aggregate and hourly calendar, with 25 basis points fees, slip of 10 or 30 basis points, and return marked including open and closed trades, with a confidence interval of 5000 resampled trades and seed 91206, not adjusted for multiple comparisons. https://runway.adityaperswal.workers.dev/data/crypto-bars

A news scan found the following data: Columns are the categories of information, and rows are the individual data points. The categories include the symbol of the asset, which is SOL, the time frame, which is hourly, the look, the moving average, the period, the slip in basis points, the return percentage, the drawdown percentage, the number of trades, the win rate percentage, the mean trade percentage, the mean confidence interval percentage, the number of open trades, and the exposure percentage. The data points are as follows: for SOL with a hourly time frame, a look of 15, a moving average of 200, a train period, a slip of 30 basis points, the return percentage is -13.806183, the drawdown percentage is 32.597064, the number of trades is 4, the win rate percentage is 25, the mean trade percentage is -2.814878, the mean confidence interval percentage is between -13.701267 and 10.630335, the number of open trades is 0, and the exposure percentage is 13.794854. The data was collected on September 12, 2024, at 06:05 UTC, and includes 2024 and 2025 as the train period and 2026 as the known holdout period. The daily complete 24-hour aggregate is based on the hourly calendar. The next open trade has no stop loss or take profit comparator. The fees are 25 basis points, and the slip is 10 or 30 basis points. The return is marked and includes open and closed trades. The confidence interval is based on 5000 resampled trades with a seed of 91206, and is not adjusted for multiple comparisons. A zero open or closed trade means there is no eligible entry and not a profitable edge. https://runway.adityaperswal.workers.dev/data/crypto-bars

A news scan found the headline 'SOL/USD C91206 frozen costed Donchian sweep cells 181-189'. The data shows the results of a trading strategy for SOL/USD, which is a cryptocurrency pair. The strategy was tested on hourly data, with a lookback period of 15 hours, and a moving average of 0 or 100 or 200 hours. The results are divided into training and holdout periods. The training period is used to train the model, and the holdout period is used to test it. The results include the return percentage, drawdown percentage, number of trades, win rate percentage, mean trade percentage, and mean confidence interval percentage. The data also includes the exposure percentage, which is the percentage of time the strategy is invested in the market. The strategy was tested with different parameters, including a slip of 10 or 30 basis points, and fees of 25 basis points. The results show that the strategy performed differently in the training and holdout periods. The data was collected on September 12, 2024, at 06:05 UTC, and includes 24 hours of hourly data. The confidence interval is based on 5000 resampled trades, with a seed of 91206. The results are not adjusted for multiple comparisons. A zero open or closed trade means that there was no eligible entry, not that there was no profitable edge. https://runway.adityaperswal.workers.dev/data/crypto-bars