Using a trading journal is one of the most under utilized tools by beginner stock traders. Recapping trades to break down what went right or wrong will help prevent future mistakes and improve returns down the road. In this guide, I will break down the five best trading journal available today for analyzing stocks, options, futures, forex, and cryptocurrency trades. After the summary, I will cover some tips for success with examples from my own personal trading for those who are new to journaling their trades.
Put simply, TraderSync takes the crown because of its features and outstanding usability. When David the founder reached and I started testing TraderSync, it felt like David had taken our Trading Journal tool and rebuilt it for Along with our own journal see No 3 below , TraderSync is the only other journal that I actively use myself.
Oh, and it is the only journal to include iOS and Android mobile apps. Bottom line, for an easy to use and overall feature rich replacement for excel, TraderSync delivers. It is popular among hedge funds and professional institutions because it is so reliable and includes a variety of features, including automatic trade marking on charts and community sharing. Trademetria is very basic as far as what data is tracked and what you can analyze; however, it does include real-time quote data for paid subscriptions. This allows Trademetria to serve as effectively a watch list tool as well as a trading journal.
As the head of research for StockBrokers. See also: best online stock brokers Edgewonk is downloadable trading journal software that offers pretty deep analysis of your trades. The upside is the customization possibilities pending you enter in detailed notes and tags for each trade. Also, since it is software, you only need to pay for it once; there is no monthly subscription. The downside is that the broker import tool support is nearly non-existent for US based stock traders and is instead focused primarily on a handful of popular forex brokers and platforms like MetaTrader4 MT4.
Reviewing the film is critical part of professional sports, and investing is no different. Taking a screenshot of the stock chart after the trade is completed, plotting buy and sell points, writing down your notes recapping the trade, and tweaking trade rules thereafter all fall under the post trade analysis. Trading journals provide you an easy way to figure out what went right, what went wrong, and look back at your trade history.
There is simply no better way to improve over time. You can improve your success rate, and ultimately make more money from your investing if you put in the time to conduct post-trade analysis. Tagging your trades means marking the strategy you used to make the trade. By tagging each trade, you can assess performance over time and identify whether or not the strategy you are using is successful.
5 Best Trading Journals and How to Journal Successfully
Their periodic success is not free of charge : catastrophic events burn enormous values in dollars and the economic systems in severe danger. Are traders so sure that elaborated strategies fit the dynamics of the markets? Rational expectations theorists would immediately bet that the random strategy would loose the competition as it is not making use of any information but, as we will show, our results are quite surprising.
We consider four very popular indexes of financial markets and in particular, we analyze the following corresponding time series, shown in Fig. See text for further details. In general, the possibility to predict financial time series has been stimulated by the finding of some kind of persistent behavior in some of them [38] , [54] , [55]. The main purpose of the present section is to investigate the possible presence of correlations in the previous four financial series of European and US stock market all share indexes. In this connection, we will calculate the time-dependent Hurst exponent by using the detrended moving average DMA technique [56].
Let us begin with a summary of the DMA algorithm. The computational procedure is based on the calculation of the standard deviation along a given time series defined as. In order to determine the Hurst exponent , the function is calculated for increasing values of inside the interval , being the length of the time series, and the obtained values are reported as a function of on a log-log plot.
Journal of Financial Markets
In general, exhibits a power-law dependence with exponent , i. In particular, if , one has a negative correlation or anti-persistent behavior, while if one has a positive correlation or persistent behavior.
The case of corresponds to an uncorrelated Brownian process. In our case, as a first step, we calculated the Hurst exponent considering the complete series. This analysis is illustrated in the four plots of Fig. Here, a linear fit to the log-log plots reveals that all the values of the Hurst index H obtained in this way for the time series studied are, on average, very close to 0.
This result seems to indicate an absence of correlations on large time scales and a consistence with a random process. The power law behavior of the DMA standard deviation allows to derive an Hurst index that, in all the four cases, oscillates around 0. See text. On the other hand, it is interesting to calculate the Hurst exponent locally in time. In order to perform this analysis, we consider subsets of the complete series by means of sliding windows of size , which move along the series with time step. This means that, at each time , we calculate the inside the sliding window by changing with in Eq.
Hence, following the same procedure described above, a sequence of Hurst exponent values is obtained as function of time. In Fig. In this case, the values obtained for the Hurst exponent differ very much locally from 0. This investigation, which is in line with what was found previously in Ref.
As we will see in the next sections, this feature will affect the performances of the trading strategies considered. In the present study we consider five trading strategies defined as follows:. Then, if , the trader predicts an increment of the closing index for the next day i. In the following simulations we will consider days, since this is one of the most used time lag for the momentum indicator.
See Ref. A divergence can be defined referring to a comparison between the original data series and the generated RSI-series, and it is the most significant trading signal delivered by any oscillator-style indicator. It is the case when the significant trend between two local extrema shown by the RSI trend is oriented in the opposite direction to the significant trend between two extrema in the same time lag shown by the original series.
When the RSI line slopes differently from the original series line, a divergence occurs. Look at the example in Fig. In the case shown, the analyst will interpret this divergence as a bullish expectation since the RSI oscillator diverges from the original series: it starts increasing when the original series is still decreasing. In our simplified model, the presence of such a divergence translates into a change in the prediction of the sign, depending on the bullish or bearish trend of the previous days.
In the following simulations we will choose days, since - again - this value is one of the mostly used in RSI-based actual trading strategies.
OPTIMAL TRADING STRATEGIES AND PERFORMANCE OF OPTIONS AT NSE.
A divergence is a disagreement between the indicator RSI and the underlying price. By means of trend-lines, the analyst check that slopes of both series agree. When the divergence occurs, an inversion of the price dynamic is expected. In the example a bullish period is expected. This deterministic strategy does not come from technical analysis.
If, e. In any moment t ,.
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In particular, the first is the Exponential Moving Average of taken over twelve days, whereas the second refers to twenty-six days. The calculation of these EMAs on a pre-determined time lag, x , given a proportionality weight , is executed by the following recursive formula: with , where.
Once the MACD series has been calculated, its 9-days Exponential Moving Average is obtained and, finally, the trading strategy for the market dynamics prediction can be defined: the expectation for the market is bullish bearish if. In this connection we are only interested in evaluating the percentage of wins achieved by each strategy, assuming that - at every time step - the traders perfectly know the past history of the indexes but do not possess any other information and can neither exert any influence on the market, nor receive any information about future moves.
In the following, we test the performance of the five strategies by dividing each of the four time series into a sequence of trading windows of equal size in days and evaluating the average percentage of wins for each strategy inside each window while the traders move along the series day by day, from to. This procedure, when applied for , allows us to explore the performance of the various strategies for several time scales ranging, approximatively, from months to years.
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The motivation behind this choice is connected to the fact that the time evolution of each index clearly alternates between calm and volatile periods, which at a finer resolution would reveal a further, self-similar, alternation of intermittent and regular behavior over smaller time scales, a characteristic feature of turbulent financial markets [35] , [36] , [38] , [58]. Such a feature makes any long-term prediction of their behavior very difficult or even impossible with instruments of standard financial analysis.
The point is that, due to the presence of correlations over small temporal scales as confirmed by the analysis of the time dependent Hurst exponent in Fig. But this could depend much more on chance than on the real effectiveness of the adopted algorithm. On the other hand, if on a very large temporal scale the financial market time evolution is an uncorrelated Brownian process as indicated by the average Hurst exponent, which result to be around for all the financial time series considered , one might also expect that the performance of the standard trading strategies on a large time scale becomes comparable to random ones.
In fact, this is exactly what we found as explained in the following. In Figs. From top to bottom, we report the index time series, the corresponding returns time series, the volatility, the percentages of wins for the five strategies over all the windows and the corresponding standard deviations. The last two quantities are averaged over 10 different runs events inside each window. The average percentages of wins for the five strategies are always comparable and oscillate around , with small random differences which depend on the financial index considered. The performance of of wins for all the strategies may seem paradoxical, but it depends on the averaging procedure over all the windows along each time series.
Moreover, referring again to Figs. In any case the advantage of a strategy seems purely coincidental. As visible, the performances of the strategies can be very different one from the others inside a single time window, but averaging over the whole series these differences tend to disappear and one recovers the common outcome shown in the previous figures.
The second important result is that the fluctuations of the random strategy are always smaller than those of the other strategies as it is also visible in Fig. However, the first big loss may drive them out of the market. In this paper we have explored the role of random strategies in financial systems from a micro-economic point of view. In particular, we simulated the performance of five trading strategies, including a completely random one, applied to four very popular financial markets indexes, in order to compare their predictive capacity.
Our main result, which is independent of the market considered, is that standard trading strategies and their algorithms, based on the past history of the time series, although have occasionally the chance to be successful inside small temporal windows, on a large temporal scale perform on average not better than the purely random strategy, which, on the other hand, is also much less volatile.
In this respect, for the individual trader, a purely random strategy represents a costless alternative to expensive professional financial consulting, being at the same time also much less risky, if compared to the other trading strategies. This result, obtained at a micro-level, could have many implications for real markets also at the macro-level, where other important phenomena, like herding, asymmetric information, rational bubbles occur.
In fact, one might expect that a widespread adoption of a random approach for financial transactions would result in a more stable market with lower volatility. In this connection, random strategies could play the role of reducing herding behavior over the whole market since, if agents knew that financial transactions do not necessarily carry an information role, bandwagon effects could probably fade. On the other hand, as recently suggested by one of us [59] , if the policy-maker Central Banks intervened by randomly buying and selling financial assets, two results could be simultaneously obtained.
Of course, this has to be explored in detail as well as the feedback effect of a global reaction of the market to the application of these actions. This topic is however beyond the goal of the present paper and it will be investigated in a future work. We thank H. Trummer for DAX historical series and the other institutions for the respective data sets.