Notes

A news scan found the headline 'BTC/USD C91304 costed 1Day USDC lag1 holdout slip0.003'. This refers to a trading strategy for Bitcoin in US Dollars. The strategy used a 1-day time frame, a rule based on US Dollar Coin, and a lag of 1 day. The results were calculated for a holdout period with a slip of 0.003. The return was -5.87 percent and the drawdown was 6.24 percent. The win percentage was 0 percent. There were 2 trades, with a mean trade percentage of -2.98 percent. The exposure percentage was 7.84 percent. For comparison, buying and holding had a return of -11.71 percent. The average win percentage was 0 percent and the average loss percentage was 2.98 percent. Fees were 25 basis points per side and slip was 10 or 30 basis points. The number of closed trades includes those that were forced to close at the end of the period. The results assume current-vintage supply and availability, with daily state and hourly fill proxy, and no managed exits, with a size of 0. 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 news scan found the headline 'BTC/USD C91304 costed 1Day USDC lag1 holdout slip0.001'. The trading results for Bitcoin, symbol BTC, are as follows: the time frame, or period of time, tf, is 1 day, and the rule used is USDC, which refers to a type of stablecoin. The lag, or delay, is 1, and the period is holdout, meaning a test period. The slip, or difference between expected and actual price, is 0.001. The return percentage, or profit, is -5.11%, the drawdown percentage, or largest loss, is 5.82%, and the win percentage, or percentage of successful trades, is 0%. There were 2 trades, and the mean trade percentage, or average profit per trade, is -2.59%. The half Kelly, a measure of optimal bet size, is not available. The exposure percentage, or percentage of assets used, is 7.84%, and the buy and hold percentage, or profit if the asset was held the entire time, is -11.71%. The average win percentage is 0%, and the average loss percentage is 2.59%. The return, drawdown, and win percentages are given in percent. The fees are 25 basis points, or 0.25%, per side, and the slip is 10 or 30 basis points, or 0.10% or 0.30%. The number of closed trades, N, includes forced terminal trades. The current supply and assumed availability are used, with a daily state hourly fill proxy, and no managed exits, with a size of 0. 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 news scan found the headline 'BTC/USD C91304 costed 1Day USDC lag1 train slip0.003'. The trading results for Bitcoin, symbol BTC, are as follows: the time frame, or period of time, tf, is 1 Day, the rule used is USDC, there is a lag of 1 day, and the period of training is train with a slip of 0.003. The return percentage is 23.08879862953303 percent, the drawdown percentage, which is the maximum loss, is 35.75217783598569 percent, and the win percentage is 27.77777777777778 percent. There were 18 trades, the mean trade percentage is 2.0111196771498627 percent, the half Kelly, a measure of bet size, is 1.8392739173734496, and the exposure percentage is 64.56908344733242 percent. The buy and hold percentage, which is the return if the asset was held the entire time, is 106.95486135300149 percent, the average win percentage is 16.079974531995493 percent, and the average loss percentage is 3.399978343944611 percent. The return, drawdown, and win are in percent. Fees are 25 basis points, or 0.25 percent, per side, and the slip is 10 or 30 basis points. The number of closed trades, N, includes forced terminal trades. The supply is current vintage and availability is assumed, with a daily state hourly fill proxy, no managed exits, and a size of 0. 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 trading strategy for Bitcoin, symbol BTC, was tested on a 1-day time frame, which is the length of time used to analyze price movements. The rule used to make trades was based on the US Dollar Coin, a type of digital currency. The lag, or delay, was 1, meaning the strategy looked at past data to make decisions. This test was done during the training period, which is a time used to teach the strategy. The slip, or difference between expected and actual trade prices, was 0.001. The results showed a return of 32.27807651260602 percent, a drawdown, or largest loss, of 33.67330575673646 percent, and a win percentage, or percentage of successful trades, of 27.77777777777778 percent. There were 18 trades, with a mean trade percentage, or average gain per trade, of 2.4209851163331395 percent. The half Kelly, a measure of how much to bet, was 2.4290523534551687. The exposure percentage, or percentage of time invested, was 64.56908344733242 percent. If the strategy had simply bought and held Bitcoin, the return would have been 106.95486135300149 percent. The average win percentage was 16.54622833649388 percent, and the average loss percentage was 3.0118007375748372 percent. The return, drawdown, and win percentages are given as percentages. The fees were 25 basis points, or 0.25 percent, per side, and the slip was 10 or 30 basis points, or 0.1 or 0.3 percent. The number of closed trades includes those that were forced to close at the end of the test. The test assumed that the current supply of Bitcoin and its availability were constant, and used a daily state hourly fill proxy, which is a way of estimating how often trades can be made. There were no managed exits, which are trades that are closed based on a specific strategy, and the size of the trades was 0. 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 news scan found the headline 'BTC/USD C91304 costed 1Day SMA_only lag2 holdout slip0.003'. The details are as follows: the symbol is BTC, the time frame is 1 day, and the rule used is SMA_only, which means Simple Moving Average only, a method to make decisions based on the average price of a stock or currency over a certain time period. The lag is 2, the period is holdout, which refers to a period of time used to test a strategy, and the slip is 0.003. The return percentage is 10.2381512174003, the drawdown percentage, which is the decrease in value from a peak to a trough, is 5.824592245664551, and the win percentage is 100. There was 1 trade, the mean trade percentage is 10.263746595443804, and the average win percentage is also 10.263746595443804, as there were no losses. The exposure percentage is 9.411764705882353, and the buy and hold percentage, which is the return if the asset was held for the entire period, is -11.70932375964865. Fees are 25 basis points per side, and slip is 10 or 30 basis points. The number of closed trades includes those that were forced to close at the end of the period. The supply and availability assumptions are based on the current vintage, and the daily state hourly fill proxy is used, with no managed exits and a size of 0. 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 news scan found the headline 'BTC/USD C91304 costed 1Day SMA_only lag2 holdout slip0.001'. The trading fund manager analyzed the Bitcoin symbol, using a 1-day time frame and a rule called Simple Moving Average only, with a lag of 2. This was during the holdout period, with a slip of 0.001. The return percentage was 10.67998882299392, and the drawdown percentage was 5.824592245664539. The win percentage was 100, with 1 trade. The mean trade percentage was 10.706688795051402. Fees were 25 basis points per side, and slip was 10 or 30 basis points. The number of closed trades includes forced terminal trades. The analysis assumed current supply and availability, with daily state and hourly fill proxy, no managed exits, and a size of 0. 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 news scan found the headline 'BTC/USD C91304 costed 1Day SMA_only lag2 train slip0.003'. The results of a trading strategy for Bitcoin, symbol BTC, are as follows: the time frame, or period of time used to make decisions, was 1 day, and the rule used to make decisions was based on a Simple Moving Average, or SMA, which is a way to smooth out price changes over time. The lag, or delay, was 2, meaning the strategy looked at prices from 2 days ago. The strategy was trained, or tested, on a set of data. The slip, or difference between expected and actual prices, was 0.003. The return, or profit, was 31.315361031442833 percent, and the drawdown, or largest loss, was 34.98666793245082 percent. The win percentage, or percentage of profitable trades, was 28.57142857142857 percent. There were 14 trades in total, and the average trade return was 2.5089058570530858 percent. The half Kelly criterion, a measure of risk, was 2.2372651228907374. The exposure, or percentage of time the strategy was invested, was 76.74418604651163 percent. The return from buying and holding, or not using the strategy, was 106.95486135300149 percent. The average win was 17.018106464481974 percent, and the average loss was 3.29477438591847 percent. The results include fees of 25 basis points, or 0.25 percent, per trade, and a slip of 10 or 30 basis points, or 0.1 or 0.3 percent. The number of closed trades includes those that were forced to close at the end of the testing period. The results assume the current supply and availability of Bitcoin, and use a proxy for hourly price changes. 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 news scan found the headline 'BTC/USD C91304 costed 1Day SMA_only lag2 train slip0.001'. The results of a trading strategy for Bitcoin (BTC) are as follows: the symbol is BTC, the time frame is 1 day, and the rule used is a Simple Moving Average (SMA) only with a lag of 2. This was a training period with a slip of 0.001. The return percentage was 38.87885536734376, the drawdown percentage was 33.67330575673647, and the win percentage was 28.57142857142857. There were 14 trades, with a mean trade percentage of 2.9207664372233757. The half Kelly criterion was 2.873438594594975, and the exposure percentage was 76.74418604651163. For comparison, buying and holding had a return percentage of 106.95486135300149. The average win percentage was 17.488120328108756, and the average loss percentage was 2.906175119130776. The return, drawdown, and win percentages are given in percent. Fees were 25 basis points per side, and slip was 10 or 30 basis points. The number of closed trades includes those that were forced at the end of the period. The results assume current and vintage supply and availability, with a daily state and hourly fill proxy, no managed exits, and a size of 0. 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 news scan found the data line: symbol SOL, supply USDC_USDT, period holdout, n 248, corr_future 0.24231072016528576, corr_past 0.3212925726449041, beta 3.5180412097723686, p 0.16378912509449273, ci_lo -1.433865415005653, ci_hi 8.46994783455039, R2 0.06097264133797409. This data is from a training model that ignores the last 7 days of data, and the holdout period is from January 1 to September 5, with outcomes through September 12. The model uses a current-vintage proxy, meaning it uses the most recent available data, and does not have a publication archive or causal identification, which is a statistical method to determine cause and effect. https://stablecoins.llama.fi/stablecoin/2 ; https://stablecoins.llama.fi/stablecoin/1 ; https://runway.adityaperswal.workers.dev/data/crypto-bars

A news scan found the data line: symbol SOL, supply USDC_USDT, period train, n 479, corr_future -0.08791560995518553, corr_past 0.21346555027260244, beta -0.9402788742104095, p 0.4124911692371632, ci_lo -3.189056383090606, ci_hi 1.3084986346697867, R2 0.011704665478711851. Training purges the last 7 days of data, and the holdout period is from January 1 to September 5, with outcomes through September 12. This is a current-vintage proxy, meaning it uses the most recent data available, with no publication archive or causal identification, which is a statistical method to determine cause and effect. https://stablecoins.llama.fi/stablecoin/2 ; https://stablecoins.llama.fi/stablecoin/1 ; https://runway.adityaperswal.workers.dev/data/crypto-bars

A news scan found the data line: {"symbol":"SOL","supply":"USDC","period":"holdout","n":248,"corr_future":0.2724551287738069,"corr_past":0.24848091809533443,"beta":1.4255104876403193,"p":0.12347941706657495,"ci_lo":-0.38835106255528196,"ci_hi":3.2393720378359205,"R2":0.07740393523602906}. This is a data output, specifically a JSON object, that appears to be related to a statistical analysis of the SOL/USD market, where supply is measured in USDC, a type of digital currency. The analysis covers a holdout period from January 1 to September 5, with outcomes observed through September 12. The data was trained on a set that excludes the final 7 days. The analysis uses a current-vintage proxy, meaning it uses the most recent available data, and does not include a publication archive or causal identification, which refers to the process of determining cause-and-effect relationships. https://stablecoins.llama.fi/stablecoin/2 ; https://stablecoins.llama.fi/stablecoin/1 ; https://runway.adityaperswal.workers.dev/data/crypto-bars

A news scan found the data line: {"symbol":"SOL","supply":"USDC","period":"train","n":479,"corr_future":-0.1015479569416903,"corr_past":0.20451185379322837,"beta":-0.5044603083563741,"p":0.2791122273551556,"ci_lo":-1.417986688865422,"ci_hi":0.40906607215267365,"R2":0.014456988764393275}. This is a data output. Training purges the final 7 days; the holdout period is from January 1 to September 5, with outcomes through September 12. The data uses a current-vintage proxy, with no publication archive or causal identification. https://stablecoins.llama.fi/stablecoin/2 ; https://stablecoins.llama.fi/stablecoin/1 ; https://runway.adityaperswal.workers.dev/data/crypto-bars

A news scan found the data line: symbol is ETH, supply is USDC_USDT, period is holdout, n is 248, corr_future is 0.22647973585167205, corr_past is 0.3973467622406515, beta is 3.3427147809722557, p is 0.22923534380461796, ci_lo is -2.106367913485606, ci_hi is 8.791797475430117, R2 is 0.051320881773904925. Training purges the final 7 days, and the holdout is from January 1 to September 5, with outcomes through September 12. This is a current-vintage proxy, meaning it uses the most recent data available, with no publication archive or causal identification, which is a statistical method to determine cause and effect. https://stablecoins.llama.fi/stablecoin/2 ; https://stablecoins.llama.fi/stablecoin/1 ; https://runway.adityaperswal.workers.dev/data/crypto-bars

A news scan found the following data: a training model for ETH/USD, also known as Ethereum to US Dollar, was run with a focus on the relationship between Ethereum and a type of stablecoin called USDC_USDT. The model looked at 724 data points and found a weak correlation between the past and future values, with a correlation of -0.015786950258688755 for future values and 0.21795522512425444 for past values. The beta, which measures how much the value of Ethereum changes when the value of USDC_USDT changes, was -0.19052146750521365. The p-value, which is a measure of how likely it is that the results occurred by chance, was 0.8094678709486551. The confidence interval, which is a range of values within which the true value is likely to lie, was between -1.739238795317355 and 1.3581958603069277. The R-squared value, which measures how well the model fits the data, was 0.00035980356510523226. The training data excluded the last 7 days and the holdout period was from January 1 to September 5, with outcomes measured through September 12. This was a current-vintage proxy, meaning it used the most recent data available, and did not include any archived publications or attempts to identify cause-and-effect relationships. https://stablecoins.llama.fi/stablecoin/2 ; https://stablecoins.llama.fi/stablecoin/1 ; https://runway.adityaperswal.workers.dev/data/crypto-bars

A news scan found the following data: the symbol is ETH, the supply is USDC, the period is holdout, the number of observations is 248, the correlation with future values is 0.27721960769121723, the correlation with past values is 0.3195029298934457, the beta is 1.453205749060937, the p-value is 0.1579118803949744, the lower bound of the confidence interval is -0.5637702066106516, the upper bound of the confidence interval is 3.4701817047325254, and the R-squared value is 0.07693976754133436. This data is from a training model that excludes the last 7 days of data, and the holdout period is from January 1 to September 5, with outcomes measured through September 12. The model uses a current-vintage proxy, and does not include a publication archive or causal identification. https://stablecoins.llama.fi/stablecoin/2 ; https://stablecoins.llama.fi/stablecoin/1 ; https://runway.adityaperswal.workers.dev/data/crypto-bars

A news scan found the data line: symbol is ETH, supply is USDC, period is train, n is 724, corr_future is -0.04826179629361642, corr_past is 0.11004928496870682, beta is -0.22959184673351835, p is 0.43611734410135383, ci_lo is -0.8074182152592295, ci_hi is 0.3482345217921928, R2 is 0.002489153544793843. Training purges final 7 days, holdout Jan 1 to Sep 5 outcomes are through Sep 12. This is a current-vintage proxy, there is no publication archive or causal identification. https://stablecoins.llama.fi/stablecoin/2 ; https://stablecoins.llama.fi/stablecoin/1 ; https://runway.adityaperswal.workers.dev/data/crypto-bars

A news scan found the data line: symbol is BTC, supply is USDC_USDT, period is holdout, n is 248, corr_future is 0.25232611452714726, corr_past is 0.4490967424960792, beta is 2.6989622258088057, p is 0.11533711510557354, ci_lo is -0.660429845379972, ci_hi is 6.058354296997583, R2 is 0.06376135885242296. Training purges the final 7 days, holdout is from January 1 to September 5, with outcomes through September 12. This is a current-vintage proxy, with no publication archive or causal identification. https://stablecoins.llama.fi/stablecoin/2 ; https://stablecoins.llama.fi/stablecoin/1 ; https://runway.adityaperswal.workers.dev/data/crypto-bars

A news scan found the data line: symbol is BTC, supply is USDC_USDT, period is train, n is 724, corr_future is -0.05302740396675218, corr_past is 0.3347596012155946, beta is -0.3695896953898996, p is 0.4430846854560575, ci_lo is -1.3140323647126229, ci_hi is 0.5748529739328236, R2 is 0.002834744042537918. Training purges the final 7 days, holdout is from January 1 to September 5, with outcomes through September 12. This is a current-vintage proxy, meaning it uses the most recent data available, with no publication archive or causal identification, which refers to the process of determining cause-and-effect relationships. https://stablecoins.llama.fi/stablecoin/2 ; https://stablecoins.llama.fi/stablecoin/1 ; https://runway.adityaperswal.workers.dev/data/crypto-bars

A news scan found the data line: symbol is BTC, supply is USDC, period is holdout, n is 248, corr_future is 0.25438490824679005, corr_past is 0.3105377195200329, beta is 0.9384159546319287, p is 0.14468720575883984, ci_lo is -0.3225989167910007, ci_hi is 2.1994308260548583, R2 is 0.06663822525350138. Training purges the last 7 days of data, and the holdout period is from January 1 to September 5, with outcomes through September 12. This is a current-vintage proxy, meaning it uses the most recent data available, with no publication archive or causal identification, which is a statistical method to determine cause and effect. https://stablecoins.llama.fi/stablecoin/2 ; https://stablecoins.llama.fi/stablecoin/1 ; https://runway.adityaperswal.workers.dev/data/crypto-bars

A news scan found the data line: symbol is BTC, supply is USDC, period is train, n is 724, correlation with future is -0.0027926577536458467, correlation with past is 0.2230648864677853, beta is -0.00008319784390666517, p is 0.9996515870887822, confidence interval low is -0.3735104164474833, confidence interval high is 0.37334402075967, and R-squared is 0.0002685429942016482. This data is from a training period that excludes the last 7 days, with outcomes from January 1 to September 5 evaluated through September 12, using a current-vintage proxy, with no publication archive or causal identification. https://stablecoins.llama.fi/stablecoin/2 ; https://stablecoins.llama.fi/stablecoin/1 ; https://runway.adityaperswal.workers.dev/data/crypto-bars

A news scan found the following data line: a return percentage of 72.33927809785669, a drawdown percentage of 15.716086078967429, a win rate of 70, a sample size of 10, a mean percentage of 6.036252749048554, an average win percentage of 11.576452167289105, and an average loss percentage of -6.8908792268460655. The data also includes a half Kelly value of 0.2607125508670452, a partial value of 6, missing checks of 10, trades missing of 3, and an endpoint of 1. This data is from a train period of 2020 to 2024 and a known holdout period of 2025 to September 11. The prices were split normalized, with no cash dividend credit, and an adjusted entry signal with split-only execution. The data uses 15-minute opens, filtered by a regular trading hours calendar, and assumes a managed variant with a plus 2R quarter per sample, but the actual manager trades less frequently, so this is not a live replication. The drawdown is a daily mark approximation, not an intraday one. A vector backtest long only compounded return assertion was passed. https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=TEN&feed=sip

A news scan found the headline 'TEN R91304 raw-price fixed-path m15_True_L40_holdout_0.0006'. The fund's return percentage is 75.82076626413259, with a drawdown percentage of 15.547349387881749. The win rate is 70, based on a sample of 10 trades. The mean percentage is 6.248537698710038, with an average win percentage of 11.799828615604348 and an average loss percentage of -6.704474440710017. The half Kelly criterion, a measure of bet size, is 0.2647723921365619. There are 6 partial trades and 10 missing checks, with 3 trades missing. The endpoint is 1. The fund was trained on data from 2020 to 2024 and tested on known holdout data from 2025 to September 11. The prices used were split-normalized, without cash dividend credit, and the entry signal was adjusted. The execution was split-only, using 15-minute opens on a regular trading hours calendar. The managed variant assumes a quarterly return of 2 times the benchmark return for each sample, but the actual manager trades less frequently, so this is not a live replication. The drawdown is approximated daily, not intraday. The Vectorbt long-only compounded return assertion passed. https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=TEN&feed=sip

A news scan found the following data: the return percentage is -39.257732084578564, the drawdown percentage is 61.824487508845124, the win rate is 38.46153846153847, the sample size is 26, the mean percentage is -0.9708691193050293, the average win percentage is 14.218597372922503, and the average loss percentage is -10.464285676947238. The data also includes a half Kelly value of 0, a partial value of 8, missing checks of 77, and trades missing of 11, with an endpoint of 0. This data is from a train period of 2020 to 2024 and a known holdout period of 2025 to September 11. The prices were split and normalized, with no cash dividend credit, and the entry signal was adjusted for split-only execution. The data uses 15-minute opens, filtered by a regular trading hours calendar, and assumes a managed variant with a plus 2R quarter per sample, although the actual manager trades less frequently, so this is not a live replication. The drawdown is approximated daily, not intraday. A vector backtest long only compounded return assertion was passed. https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=TEN&feed=sip

A news scan found the following data line: {"return_pct":-36.01556482818553,"drawdown_pct":61.325007981286376,"win_rate":38.46153846153847,"sample":26,"mean_pct":-0.7726124521840025,"avg_win_pct":14.447263405200166,"avg_loss_pct":-10.285034863049109,"halfKelly":0,"partial":8,"missing_checks":77,"trades_missing":11,"endpoint":0}; this is a raw data output from a trading fund manager, showing performance metrics from 2020 to 2024, with a known holdout period from 2025 to September 11. The data is based on normalized prices, without cash dividend credits, and uses a split-only execution strategy. The manager uses a 15-minute calendar filter and assumes a quarterly return of 2 times the benchmark, but the actual manager trades less frequently, so this is not a live replication. The drawdown is approximated on a daily basis, not intraday. The data also passed a vectorbt long-only compounded return assertion test. https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=TEN&feed=sip

A news scan found the following data line: return percentage is 68.184862141503, drawdown percentage is 14.928783676776526, win rate is 70, sample size is 10, mean percentage is 5.802029415035332, average win percentage is 11.43793055145598, average loss percentage is -7.348406569946175, half Kelly is 0.25363108251679184, partial is 6, missing checks is 11, trades missing is 3, endpoint is 0. This data is from a trading simulation called R91304, which used a fixed-path method with a 15-minute time frame and a holdout period from 2025 to September 11. The simulation used split-normalized prices and did not account for cash dividends. The entry signal was adjusted and the execution was split-only. The simulation was filtered by a regular trading hours calendar and used 15-minute opening prices. The manager variant assumed a quarterly return of two times the benchmark, but the actual manager traded less frequently, so this is not a live replication. The drawdown was calculated daily, not intraday. The Vectorbt long-only compounded return assertion was passed. https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=TEN&feed=sip