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Backtest

MACD Crossover Backtest MACD – Moving Average Convergence Divergence. The MACD is calculated by subtracting a 26-day moving average of a security's price from a 12-day moving average of its price. The result is an indicator that oscillates above and below zero. When the MACD is above zero, it means the 12-day moving average is higher than the 26-day moving average. This is bullish as it shows that current expectations (i.e., the 12-day moving average) are more bullish than previous expectations (i.e., the 26-day average). This implies a bullish, or upward, shift in the supply/demand lines. When the MACD falls below zero, it means that the 12-day moving average is less than the 26-day moving average, implying a bearish shift in the supply/demand lines. A 9-day moving average of the MACD (not of the security's price) is usually plotted on top of the MACD indicator. This line is referred to as the "signal" line. The signal line anticipates the convergence of the two moving averages (i.e., the movement of the MACD toward the zero line). Let's consider the rational behind this technique. The MACD is the difference between two moving averages of price. When the shorter-term moving average rises above the longer-term moving average (i.e., the MACD rises above zero), it means that investor expectations are becoming more bullish (i.e., there has been an upward shift in the supply/demand lines). By plotting a 9-day moving average of the MACD, we can see the changing of expectations (i.e., the shifting of the supply/demand lines) as they occur. You can change long to short in the Input Settings WARNING: - For purpose educate only - This script to change bars colors.
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Kaufman Moving Average Adaptive (KAMA) Backtest Everyone wants a short-term, fast trading trend that works without large losses. That combination does not exist. But it is possible to have fast trading trends in which one must get in or out of the market quickly, but these have the distinct disadvantage of being whipsawed by market noise when the market is volatile in a sideways trending market. During these periods, the trader is jumping in and out of positions with no profit-making trend in sight. In an attempt to overcome the problem of noise and still be able to get closer to the actual change of the trend, Kaufman developed an indicator that adapts to market movement. This indicator, an adaptive moving average (AMA), moves very slowly when markets are moving sideways but moves swiftly when the markets also move swiftly, change directions or break out of a trading range. You can change long to short in the Input Settings Please, use it only for learning or paper trading. Do not for real trading.
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1-2-3 Reversal Backtest StrategyThis is backtesting script. This System was created from the Book "How I Tripled My Money In The Futures Market" by Ulf Jensen, Page 183. This is reverse type of strategies. The strategy buys at market, if close price is higher than the previous close during 2 days and the meaning of 9-days Stochastic Slow Oscillator is lower than 50. The strategy sells at market, if close price is lower than the previous close price during 2 days and the meaning of 9-days Stochastic Fast Oscillator is higher than 50.
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MACD, backtest 2015+ only, cut in half and doubledThis is only a slight modification to the existing "MACD Strategy" strategy plugin! found the default MACD strategy to be lacking, although impressive for its simplicity. I added "year>2014" to the IF buy/sell conditions so it will only backtest from 2015 and beyond ** . I also had a problem with the standard MACD trading late, per se. To that end I modified the inputs for fast/slow/signal to double. Example: my defaults are 10, 21, 10 so I put 20, 42, 20 in. This has the effect of making a 30min interval the same as 1 hour at 10,21,10. So if you want to backtest at 4hr, you would set your time interval to 2hr on the main chart. This is a handy way to make shorter time periods more useful even regardless of strategy/testing, since you can view 15min with alot less noise but a better response. Used on BTCCNY OKcoin, with the chart set at 45 min (so really 90min in the strategy) this gave me a percent profitable of 42% and a profit factor of 1.998 on 189 trades. Personally, I like to set the length/signals to 30,63,30. Meaning you need to triple the time, it allows for much better use of shorter time periods and the backtests are remarkably profitable. (i.e. 15min chart view = 45min on script, 30min= 1.5hr on script) ** If you want more specific time periods you need to try plugging in different bar values: replace "year" with "n" and "2014" with "5500". The bars are based on unix time I believe so you will need to play around with the number for n, with n being the numbers of bars.
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BACKTEST SCRIPT 0.999 ALPHATRADINGVIEW BACKTEST SCRIPT by Lionshare (c) 2015 THS IS A REAL ALTERNATIVE FOR LONG AWAITED TV NATIVE BACKTEST ENGINE. READY FOR USE JUST RIGHT NOW. For user provided trading strategy, executes the trades on pricedata history and continues to make it over live datafeed. Calculates and (plots on premise) the next performance statistics: profit - i.e. gross profit/loss. profit_max - maximum value of gross profit/loss. profit_per_trade - each trade's profit/loss. profit_per_stop_trade - profit/loss per "stop order" trade. profit_stop - gross profit/loss caused by stop orders. profit_stop_p - percentage of "stop orders" profit/loss in gross profit/loss. security_if_bought_back - size of security portfolio if bought back. trades_count_conseq_profit - consecutive gain from profitable series. trades_count_conseq_profit_max - maxmimum gain from consecutive profitable series achieved. trades_count_conseq_loss - same as for profit, but for loss. trades_count_conseq_loss_max - same as for profit, but for loss. trades_count_conseq_won - number of trades, that were won consecutively. trades_count_conseq_won_max - maximum number of trades, won consecutively. trades_count_conseq_lost - same as for won trades, but for lost. trades_count_conseq_lost_max - same as for won trades, but for lost. drawdown - difference between local equity highs and lows. profit_factor - profit-t-loss ratio. profit_factor_r - profit(without biggest winning trade)-to-loss ratio. recovery_factor - equity-to-drawdown ratio. expected_value - median gain value of all wins and loss. zscore - shows how much your seriality of consecutive wins/loss diverges from the one of normal distributed process. valued in sigmas. zscore of +3 or -3 sigmas means nonrandom realitonship of wins series-to-loss series. confidence_limit - the limit of confidence in zscore result. values under 0.95 are considered inconclusive. sharpe - sharpe ratio - shows the level of strategy stability. basically it is how the profit/loss is deviated around the expected value. sortino - the same as sharpe, but is calculated over the negative gains. k - Kelly criterion value, means the percentage of your portfolio, you can trade the scripted strategy for optimal risk management. k_margin - Kelly criterion recalculated to be meant as optimal margin value. DISCLAIMER : The SCRIPT is in ALPHA stage. So there could be some hidden bugs. Though the basic functionality seems to work fine. Initial documentation is not detailed. There could be english grammar mistakes also. NOW Working hard on optimizing the script. Seems, some heavier strategies (especially those using the multiple SECURITY functions) call TV processing power limitation errors. Docs are here: docs.google.com
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